system

Abstract

We provide the system. [Solution] A means of using a device to collect children's physiological and learning data, A means for transmitting the aforementioned physiological data and training data to a server, A means for analyzing data received by the aforementioned server and evaluating health and learning status, A means of sending notifications and advice to the parent's device based on the analysis results, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In dual-income families and single-parent families, there is a problem that it is difficult for parents to fully grasp the health status and learning progress of their children and provide appropriate and timely care. For this reason, there is a need for a mechanism that can effectively support the health and learning of children while parents can work with peace of mind.

Means for Solving the Problems

[0005] This invention solves the problem by using a device for collecting physiological and learning data of children and means for transmitting this data to a server. The server then analyzes the received physiological and learning data and provides means for evaluating the child's health and learning status. By providing means for sending notifications and advice to the parent's terminal based on the analysis results, the invention enables parents to understand their child's condition and provide necessary support. Furthermore, it includes providing parents with a dashboard that allows them to check their child's condition in real time, as well as means for generating and providing personalized learning plans for each child. This enables parents to effectively manage their child's health and learning status remotely.

[0006] "Physiological data" refers to data obtained directly from the human body, such as heart rate, body temperature, and blood pressure, which are indicators of the body's condition.

[0007] "Learning data" refers to data collected during the educational process, and serves as an indicator of learners' progress on assignments, test scores, and engagement with learning.

[0008] A "device" is an instrument used to electronically collect physiological or learning data, and includes wearable devices, computers, tablets, and other similar devices.

[0009] A "server" is a computer system used to receive, store, and analyze data over a network.

[0010] "Analysis" is an information processing activity that uses collected data to evaluate its meaning and trends, and to identify anomalies and necessary actions.

[0011] "Notification" refers to the act of conveying specific information to the user based on analysis results, and is usually done via a device.

[0012] "Advice" refers to the act of providing suggestions based on analysis results to help users determine what actions or decisions they should take.

[0013] A "dashboard" is an interface used by users to visually view and manipulate data in real time.

[0014] A "personalized learning plan" is an educational plan that is tailored to the individual learner's abilities and progress, and is designed to meet their specific learning needs. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the language used in the following description will be explained.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention relates to a system for remotely managing and optimizing a child's health status and learning progress within the home. The system consists of a device or learning terminal worn by the child, a server for collecting and analyzing data, and a terminal used by parents or guardians.

[0037] The device retrieves data.

[0038] First, wearable devices worn by children collect physiological data such as heart rate and body temperature. Simultaneously, tablets and computers used by children record learning data, such as progress in learning apps and completion status of tasks. This data is transmitted to a server periodically or in real time.

[0039] The server analyzes the data.

[0040] The server analyzes the received physiological and learning data. The AI ​​algorithms on the server detect anomalies and trends in the data, for example, evaluating whether significant fluctuations in heart rate might be a sign of stress. Furthermore, by analyzing the learning data, it measures learning comprehension and task completion, and adjusts the learning plan accordingly. Based on the analyzed data, the server generates feedback for parents regarding the child's health and learning.

[0041] Notifications and feedback

[0042] Based on the server's analysis, notifications and advice are sent to the parent's device. This allows parents to monitor their child's health and learning progress in real time. For example, if there are heart rate fluctuations suggestive of stress, a notification such as, "Please pay attention to your child's recent heart rate fluctuations. Adequate rest is recommended," will be sent.

[0043] User behavior

[0044] Users (parents) can view this information on the provided dashboard. The dashboard visually displays the data, allowing parents to take specific actions based on it (e.g., managing break times, communicating with the school). Furthermore, by utilizing the dashboard, parents can monitor the progress of each child's learning plan and provide learning support as needed.

[0045] As a concrete example, consider a case where a child wears a wearable device after school. If the device detects an abnormal heart rate, the data is immediately sent to a server. If the server's analysis suggests a stress response, the parent receives the information on their smartphone and can quickly consider countermeasures. In this way, the system enables parents to effectively manage their child's health and learning remotely.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The device acquires the child's physiological and learning data. Wearable devices record heart rate and body temperature, while learning devices such as tablets collect data on the use of learning apps and progress on assignments.

[0049] Step 2:

[0050] The device sends acquired physiological and training data to the server. This transmission can be done periodically or in real time.

[0051] Step 3:

[0052] The server saves the received data to a database. This makes subsequent analysis and historical reference easier.

[0053] Step 4:

[0054] The server uses AI algorithms to analyze the stored data. It determines whether there are any abnormalities in physiological data or delays in training data, and evaluates the health status and learning comprehension level.

[0055] Step 5:

[0056] The server generates notifications and advice for the parent based on the analysis results. For example, if there are health concerns, it will create a specific alert message.

[0057] Step 6:

[0058] The server sends the generated notification to the parent's device. This allows the user to receive real-time updates on the child's status.

[0059] Step 7:

[0060] Users can use the provided dashboard to check their child's health status and learning progress, along with notifications. The dashboard includes detailed data and visual information through graphs.

[0061] Step 8:

[0062] Users use the information on the dashboard to decide on appropriate actions for their children and take concrete steps. This includes contacting schools and medical institutions, and reviewing learning plans.

[0063] Step 9:

[0064] Users regularly check for new data and notifications from the server and take further action as needed. This continuous process optimizes children's health and learning on a daily basis.

[0065] (Example 1)

[0066] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0067] The challenge lies in efficiently managing children's health and learning progress at home using a single system, even remotely and in real time. In particular, it is necessary to integrate and analyze physiological characteristics and learning progress data so that parents can immediately take appropriate action.

[0068] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0069] In this invention, the server includes means for transmitting data on the child's physiological characteristics and educational progress to a central processing unit, means for analyzing the data received by the central processing unit to determine the child's health status and educational progress, and means for transmitting notifications and suggestions to the parent's information processing unit based on the analysis results. This enables parents to comprehensively understand their child's health and learning progress and take immediate action.

[0070] "Children's physiological characteristics data" refers to data that indicates a child's physical condition, such as heart rate, body temperature, and activity level.

[0071] "Educational progress data" refers to data obtained through educational applications and systems that shows children's learning outcomes, learning speed, and level of comprehension.

[0072] "Devices" refer to wearable devices attached to a child's body, as well as tablets and computers used to record educational progress data.

[0073] A "central processing unit" refers to a computer server used to analyze various types of data it receives.

[0074] "Analysis" refers to the act of processing received data and analyzing it to determine health status or learning comprehension.

[0075] "Parental information processing device" refers to a device (smartphone or tablet) used by parents to receive analyzed data and notifications.

[0076] A "visualized management screen" refers to a dashboard or interface used by parents to remotely monitor their child's status and visually review data.

[0077] An "individualized education plan" refers to a learning program that is tailored to each child's characteristics and progress.

[0078] This invention is a system for remotely managing a child's health status and learning progress within the home. The system utilizes a device for acquiring and transmitting the child's physiological characteristics data and educational progress data, a central processing unit, and an information processing unit used by the parent or guardian.

[0079] The device periodically acquires physiological characteristic data such as heart rate, body temperature, and activity level through a wearable device worn by the child. Additionally, the child's tablet or computer records educational progress data, such as learning progress and correct answer rates, through educational apps. This data is transmitted to the central processing unit in an encrypted state using a specified protocol.

[0080] The central processing unit (the server) analyzes the received data using AI algorithms to detect abnormalities in the child's health. For example, if a sudden increase in heart rate is detected, it is notified to the parent as a sign of stress. It also adjusts individualized educational plans according to the child's learning progress and suggests educational support. The analysis results are transmitted in real time to the parent's information processing unit, allowing the parent to check the information through a visualized management screen and take appropriate action quickly.

[0081] For example, if a child's heart rate is abnormal while wearing a wearable device after school, that data is immediately sent to the central processing unit. The server instantly analyzes the data and notifies the parent of a possible stress level, allowing the parent to immediately consider concrete measures such as rest or environmental improvements. Another example of a prompt to be input to the generating AI model is the question, "How can the learning plan be adjusted to improve learning efficiency?" In this way, parents can accurately understand and effectively manage their child's health and learning status.

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

[0083] Step 1:

[0084] The device collects physiological characteristic data through a wearable device attached to the child. Specifically, a heart rate sensor measures heart rate every second, and a body temperature sensor records body temperature every five minutes. The inputs are heart rate and body temperature data, and the output is that this data is temporarily stored in a buffer within the device.

[0085] Step 2:

[0086] The device uses a child's tablet to retrieve educational progress data from educational applications. Specifically, it records application usage, task completion rate, and number of correct answers. The input is progress data from the learning app, and the output is this progress data saved as a log file on the device.

[0087] Step 3:

[0088] The terminal collects physiological characteristics data and educational progress data and sends the data to the central processing unit. Here, the data is encrypted using the HTTPS protocol and sent to the server every 5 minutes. The inputs are physiological characteristics data and educational progress data, and the output is the encoded data sent to the server.

[0089] Step 4:

[0090] The server decodes the received data and filters it as needed. Specifically, it performs anomaly detection and missing data imputation to generate a dataset suitable for analysis. The input is the data sent to the server, and the output is a cleansed dataset.

[0091] Step 5:

[0092] The server uses a generated AI model to perform analysis and determine the child's health status and educational progress. The AI ​​model analyzes heart rate variability in relation to stress indicators and generates proposed adjustments to the educational plan based on the training data. The input is a cleansed dataset, and the output is a health status report and proposed adjustments to the educational plan.

[0093] Step 6:

[0094] The server sends a notification to the parent's information processing device based on the analysis results. Here, the analysis results and suggestions are delivered immediately via push notifications. Inputs include health status reports and proposed adjustments to the educational plan, and output is the notification content displayed on the parent's device.

[0095] Step 7:

[0096] Parents, as users, manage their children's status based on the notifications they receive and take necessary actions. They can check information through the management screen and, for example, set break times based on increased stress levels or adjust learning schedules. Input is the notification content, and output is the implementation of measures to improve the child's health and education based on the parent's actions.

[0097] (Application Example 1)

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

[0099] Today, personalized health-based recommendation services are limited in consumer behavior. A system is needed that analyzes an individual's health status and consumer behavior in real time and suggests appropriate products and services based on the results. However, existing technologies present a challenge in effectively supporting consumers' health-based purchasing behavior within stores.

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

[0101] In this invention, the server includes means for using equipment to acquire an individual's physiological state data and consumer behavior data, means for transmitting the physiological state data and consumer behavior data to a data storage device, and means for analyzing the data received by the data storage device and evaluating the state of health and consumer behavior. This makes it possible to personalize consumer behavior linked to an individual's health status.

[0102] "Physiological status data" refers to data that indicates an individual's physical health, such as heart rate and body temperature.

[0103] "Consumer behavior data" refers to data that shows the actions individuals take, such as purchasing or selecting products, at stores and other locations.

[0104] "Means using equipment" refers to methods that use devices or systems used to acquire personal data.

[0105] A "data storage device" refers to a device or system used to store acquired data and analyze it.

[0106] "Means of analysis" refers to methods and devices used to analyze collected data and evaluate an individual's condition.

[0107] "Recommendation information" refers to product information and service suggestions provided to individuals based on analysis results.

[0108] An "information display device" is a device or apparatus used to visually present analysis results and recommendation information to an individual.

[0109] To implement this invention, equipment is required to acquire individual physiological state data and consumer behavior data. This includes wearable devices and smart glasses used by the individual. The data acquired from these devices is transmitted wirelessly to a cloud-based data storage device. Examples of cloud services that can be used include AWS® and Microsoft® Azure®.

[0110] The server analyzes physiological state data and consumer behavior data received by the data storage device and uses an AI algorithm to evaluate health status and consumption patterns. Based on this evaluation, it generates product recommendation information optimized for the individual's health status.

[0111] Smart glasses, acting as information display devices, visually display recommendation information transmitted from a server. This information allows individuals to engage in health-conscious purchasing behavior in stores in real time. An example of a specific prompt message is, "If this customer's heart rate is high, we want to display recommendations for relaxing products." This enables the suggestion of appropriate services tailored to the individual's health condition.

[0112] This system configuration allows consumers in physical stores to enjoy a personalized shopping experience while taking their health status into consideration.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The terminal acquires physiological state data and consumer behavior data from wearable devices and smart glasses worn by individuals. This acquired data includes physiological indicators such as heart rate and body temperature, as well as consumer behavior based on temporary shopping choices and movement patterns. This data is collected by sensors and prepared to be transmitted to a server via wireless communication.

[0116] Step 2:

[0117] The server receives physiological status data and consumer behavior data transmitted from the terminal. Based on the received data, it uses a generative AI model to perform analysis and detect patterns in health status and consumer behavior. During this process, the AI ​​algorithm detects anomalies and performs trend analysis to extract insights into health status and consumer behavior.

[0118] Step 3:

[0119] Based on the analysis results, the server generates recommendation information to suggest products and services tailored to the individual's health condition. The generated recommendation information is optimized for the individual's current state and includes suggestions such as items to reduce stress. This information is then processed within the server and converted into an appropriate format.

[0120] Step 4:

[0121] The server sends the generated recommendation information to the terminal's information display device, such as smart glasses. The terminal visually presents the received information to the user. Based on this information, the user can adjust their shopping behavior in the store and make healthier choices. In this process, the user receives real-time feedback via the terminal and can modify their behavior based on their health status.

[0122] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0123] This invention provides more sophisticated support in a system for remotely managing a child's health and learning progress by incorporating an emotion engine. The system consists of a device worn by the child, a server that processes data, a terminal that receives the analysis results, and an emotion engine that recognizes the user's emotions.

[0124] The device retrieves data.

[0125] Wearable devices used by children acquire physiological data such as heart rate and body temperature. Simultaneously, learning devices collect activity and progress during learning. In addition, the devices are equipped with sensors that capture the child's facial expressions and voice, and an emotion engine analyzes their emotional state.

[0126] The server analyzes the data.

[0127] The server receives physiological data, learning data, and emotional data transmitted from the terminal. Based on the received data, an AI algorithm performs an evaluation, analyzing the emotional state in addition to the health status and learning progress. This emotional analysis identifies specific emotions such as stress and anxiety, and the server stores this information in a database.

[0128] Providing notifications and advice

[0129] The server generates notifications and advice for parents based on the analysis results. The generated notifications include information about the child's health status, learning progress, and emotional state. For example, if the emotional data indicates that the child is experiencing stress, a notification such as, "Your child may have been experiencing stress recently. Please provide them with time to relax as needed," will be sent.

[0130] User behavior

[0131] Users can view a dashboard provided through their parent's device. The dashboard displays detailed graphs and charts for intuitive data understanding, integrating physiological, learning, and emotional data. Based on this information, users can make appropriate decisions and create a safe and secure environment for their child.

[0132] As a concrete example, consider a scenario where a child's facial expressions are analyzed via camera during remote learning, and the emotion engine recognizes signs of stress. The server aggregates and analyzes emotional data indicating stress, along with physiological data and learning progress. Parents are immediately notified, and they can check the dashboard and take appropriate action based on their child's situation. In this way, the system aims to maintain the child's overall health and optimal learning environment.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The device acquires the child's physiological data. Wearable devices periodically record data such as heart rate and body temperature. Learning devices also collect application data and assignment progress used by the child during learning.

[0136] Step 2:

[0137] The device captures the child's facial expressions using its camera. It features an emotion engine that analyzes the child's emotional state in real time based on the acquired facial data. This data, along with voice, is used for emotion analysis.

[0138] Step 3:

[0139] The device sends acquired physiological data, learning data, and emotional data to the server. This allows all data to be centrally managed.

[0140] Step 4:

[0141] The server saves the received data to a database. By recording it chronologically, it maintains a state where it can be compared with past data.

[0142] Step 5:

[0143] The server uses AI algorithms to analyze stored data. Physiological data is used to assess health status, and learning data is used to measure progress and understanding. Emotional data is used to determine emotions such as stress and anxiety.

[0144] Step 6:

[0145] The server sends notifications and advice to the parent's device based on the analysis results. The notifications comprehensively cover the child's health, learning progress, and emotional state.

[0146] Step 7:

[0147] The user checks the dashboard provided on their device. The dashboard visually displays various data, allowing parents to grasp their child's current situation at a glance.

[0148] Step 8:

[0149] Users determine appropriate responses for their children based on the information on the dashboard. Specifically, this includes providing rest when stress is recognized and offering support according to their learning progress.

[0150] Step 9:

[0151] Users receive information from the server through their devices on a daily basis, enabling continuous monitoring. This allows for constant optimization of children's health and learning environment.

[0152] (Example 2)

[0153] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0154] There is a growing need to provide more accurate support by comprehensively understanding a child's health, learning progress, and emotional state. However, conventional systems often collect and analyze this information separately, making it difficult to make a comprehensive judgment. Furthermore, the technology for evaluating emotional states is insufficient, which can lead to delays in response.

[0155] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0156] In this invention, the server includes means for integrally analyzing physiological information, learning information, and emotional information using AI; means for generating notifications and guidance based on the analysis results and providing them to parents quickly; and means for visualizing detailed information, including emotional information, so that parents can check it in real time. This makes it possible to immediately grasp the multifaceted state of the child and provide appropriate support.

[0157] "Physiological information" refers to data that indicates a child's physical condition, such as heart rate, body temperature, and steps taken.

[0158] "Learning information" refers to data related to children's educational activities, such as study time, problem-solving status, and accuracy rate.

[0159] "Emotional information" refers to data about a child's emotional state obtained through the analysis of their facial expressions and voice.

[0160] "Device" refers to equipment used to collect physiological information, learning information, and emotional information.

[0161] A "computer" is a computer system used to analyze received physiological, learning, and emotional information.

[0162] A "notification" is an informational or guidance message generated based on the analysis results and sent to the parent.

[0163] "Visualized information display" refers to dashboards or screens that visually show a child's condition using graphs, charts, and other visual aids.

[0164] This invention is a system for comprehensively managing a child's health, learning progress, and emotional state. The following components are used to implement the invention.

[0165] The device collects data.

[0166] The device uses a wearable device attached to the child to acquire physiological information such as heart rate, body temperature, and steps taken. It also collects learning information such as study time and problem-solving rate from a digital learning platform. Furthermore, it uses the camera and microphone built into the device to capture the child's facial expressions and voice, and an emotion engine generates emotional information.

[0167] The server analyzes the data.

[0168] The server receives and analyzes physiological, learning, and emotional information sent from the terminal. Using a generative AI model, the server comprehensively processes this information to evaluate health status, learning progress, and emotional state. This analysis utilizes machine learning algorithms and database technologies, and is updated in real time.

[0169] Users utilize data

[0170] Parents, as users, can visually check information using a dashboard provided through their device. The dashboard displays graphs showing health status, charts showing learning progress, and icons indicating changes in emotional state. This allows users to make an overall assessment and consider appropriate responses based on their child's condition.

[0171] As a concrete example, when a child is taking an online class, facial expression data is captured via camera, and stress levels are recognized through an emotion engine. The results are analyzed on a server, and a notification such as "Your child appears to be stressed. We recommend they take a break" is sent to the parent's device. Further improvements in accuracy are expected through analysis using "generative AI models" and "prompt messages."

[0172] An example of a prompt is, "How can we accurately detect a child's emotional state and notify them in real time?" In this way, the system enables the management of a child's multifaceted state and the provision of appropriate support.

[0173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0174] Step 1:

[0175] The device retrieves data.

[0176] The device uses a wearable device to acquire physiological information such as heart rate, body temperature, and steps taken in real time. It also collects learning information such as learning time and answer rate through a learning application. Furthermore, it uses the device's camera and microphone to record facial expressions and voice, and generates emotional information for the emotion engine. The input is data from each sensor, and the output is digitized physiological information, learning information, and emotional information.

[0177] Step 2:

[0178] The device sends data to the server.

[0179] The device transmits collected physiological, learning, and emotional information to the server at regular intervals. The data is encrypted using a secure communication protocol. The input is the information on the device, and the output is the dataset sent to the server.

[0180] Step 3:

[0181] The server analyzes the data.

[0182] The server uses a generative AI model to analyze the received data. It evaluates health status from physiological information, learning progress from learning information, and emotional state from emotional information. The algorithm analyzes the data and detects outliers and specific patterns. The input is the dataset sent to the server, and the output is the evaluation result from the analysis.

[0183] Step 4:

[0184] The server generates notifications and advice.

[0185] The server generates notifications and advice for parents based on the analysis results. For example, if a stressed state is detected, a message such as "Your child is stressed. We recommend they take a break" will be generated. The input is the evaluation result from the analysis, and the output is the content of the notification.

[0186] Step 5:

[0187] The user checks the dashboard.

[0188] Through the provided dashboard, users can visually monitor their health, learning progress, and emotional state. The dashboard displays graphs and charts, which users use to make appropriate decisions. The input is visualized information based on analysis results, and the output is the user's understanding and response.

[0189] (Application Example 2)

[0190] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0191] In production environments, it is crucial to monitor workers' health and stress levels in real time and to promptly issue instructions for appropriate rest and work adjustments. However, currently, there are limited means to achieve this efficiently, and maintaining productivity and managing worker health in the workplace is essential.

[0192] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0193] In this invention, the server includes means for using a device for collecting physiological and emotional data of workers, means for transmitting the physiological and emotional data to a central processing unit, and means for analyzing the data received by the central processing unit and evaluating the worker's health status and work efficiency. This makes it possible to monitor the worker's health status and work efficiency in real time and provide appropriate notifications and advice to managers as needed.

[0194] A "worker" is a human employee who is responsible for various tasks in a factory or production site.

[0195] "Physiological data" refers to data that indicates the physical condition of a worker, such as heart rate, body temperature, and blood pressure.

[0196] "Emotional data" refers to data about an worker's emotional state obtained by analyzing their facial expressions and voice.

[0197] "Device" refers to a virtual equipment or device that is worn or used by an operator to collect necessary data.

[0198] A "central processing unit" is a control computer or server that receives various types of data and performs analysis and interpretation.

[0199] "Analysis" is a procedure that uses data collected by the central processing unit to evaluate the health status and work efficiency of workers.

[0200] In a mode for carrying out the invention, the system is for monitoring the health and emotional state of workers in real time and providing appropriate instructions to managers. The system consists of a wearable device worn by the worker, a central processing unit (server), and a terminal used by the manager.

[0201] First, the wearable device used by the worker is equipped with sensors to monitor heart rate and body temperature, and a camera to analyze the worker's facial expressions. This makes it possible to acquire physiological and emotional data in real time. The acquired data is then transmitted from the device to a central processing unit.

[0202] Next, the server receives this data and analyzes it using AI algorithms. Specifically, it uses a machine learning framework such as TENSORFLOW® to evaluate the health status and stress levels of individual workers. As a result of the analysis, advice based on the worker's condition is generated.

[0203] The generated advice is sent as a notification to the administrator's terminal. The administrator can review this and quickly provide appropriate instructions or work adjustments to workers. This allows for maintaining productivity on the worksite while also managing the health of the workers.

[0204] For example, if a worker has a higher-than-normal heart rate and facial analysis reveals signs of stress, the server will notify the administrator with a message stating, "We recommend taking a 15-minute break depending on the situation." In this case, an example of a prompt message for the generating AI model would be, "The worker's heart rate is higher than normal, and facial analysis revealed signs of stress. Please generate a message to provide appropriate advice."

[0205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0206] Step 1:

[0207] The wearable device worn by the worker collects heart rate, body temperature, and facial expression data in real time. Physiological and emotional data are acquired as input and transmitted digitally to a central processing unit. The data is collected from sensors and temporarily stored within the device.

[0208] Step 2:

[0209] The server receives physiological and emotional data transmitted from the wearable device. The received data is then analyzed by an AI algorithm. Specifically, the data is first compared to the normal range to determine whether or not an abnormal value has formed.

[0210] Step 3:

[0211] The server evaluates the worker's health status and work efficiency based on the analysis results. Using the processed input data, it utilizes generative AI models such as TensorFlow to make judgments about stress levels and work efficiency. Here, data processing is performed, such as identifying abnormal heart rates and peaks in tension.

[0212] Step 4:

[0213] The server generates a notification message for the administrator based on the evaluation results. It passes the results obtained from the analysis as prompts to an AI model, which then outputs an appropriate advice message. A possible example of this might be a message like, "This worker is experiencing increased stress. We suggest a 15-minute break."

[0214] Step 5:

[0215] The user (administrator) receives notifications sent to the terminal and checks the worker's status through the dashboard. Based on the advice displayed as output, they provide specific instructions to the worker and adjust their work content. Specific actions include issuing break instructions and reassigning tasks.

[0216] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0217] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0218] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0219] [Second Embodiment]

[0220] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0221] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0222] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0223] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0224] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0225] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0226] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0227] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0228] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0229] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0230] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0231] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0232] This invention relates to a system for remotely managing and optimizing a child's health status and learning progress within the home. The system consists of a device or learning terminal worn by the child, a server for collecting and analyzing data, and a terminal used by parents or guardians.

[0233] The device retrieves data.

[0234] First, wearable devices worn by children collect physiological data such as heart rate and body temperature. Simultaneously, tablets and computers used by children record learning data, such as progress in learning apps and completion status of tasks. This data is transmitted to a server periodically or in real time.

[0235] The server analyzes the data.

[0236] The server analyzes the received physiological and learning data. The AI ​​algorithms on the server detect anomalies and trends in the data, for example, evaluating whether significant fluctuations in heart rate might be a sign of stress. Furthermore, by analyzing the learning data, it measures learning comprehension and task completion, and adjusts the learning plan accordingly. Based on the analyzed data, the server generates feedback for parents regarding the child's health and learning.

[0237] Notifications and feedback

[0238] Based on the server's analysis, notifications and advice are sent to the parent's device. This allows parents to monitor their child's health and learning progress in real time. For example, if there are heart rate fluctuations suggestive of stress, a notification such as, "Please pay attention to your child's recent heart rate fluctuations. Adequate rest is recommended," will be sent.

[0239] User behavior

[0240] Users (parents) can view this information on the provided dashboard. The dashboard visually displays the data, allowing parents to take specific actions based on it (e.g., managing break times, communicating with the school). Furthermore, by utilizing the dashboard, parents can monitor the progress of each child's learning plan and provide learning support as needed.

[0241] As a concrete example, consider a case where a child wears a wearable device after school. If the device detects an abnormal heart rate, the data is immediately sent to a server. If the server's analysis suggests a stress response, the parent receives the information on their smartphone and can quickly consider countermeasures. In this way, the system enables parents to effectively manage their child's health and learning remotely.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] The device acquires the child's physiological and learning data. Wearable devices record heart rate and body temperature, while learning devices such as tablets collect data on the use of learning apps and progress on assignments.

[0245] Step 2:

[0246] The device sends acquired physiological and training data to the server. This transmission can be done periodically or in real time.

[0247] Step 3:

[0248] The server saves the received data to a database. This makes subsequent analysis and historical reference easier.

[0249] Step 4:

[0250] The server uses AI algorithms to analyze the stored data. It determines whether there are any abnormalities in physiological data or delays in training data, and evaluates the health status and learning comprehension level.

[0251] Step 5:

[0252] The server generates notifications and advice for the parent based on the analysis results. For example, if there are health concerns, it will create a specific alert message.

[0253] Step 6:

[0254] The server sends the generated notification to the parent's device. This allows the user to receive real-time updates on the child's status.

[0255] Step 7:

[0256] Users can use the provided dashboard to check their child's health status and learning progress, along with notifications. The dashboard includes detailed data and visual information through graphs.

[0257] Step 8:

[0258] Users use the information on the dashboard to decide on appropriate actions for their children and take concrete steps. This includes contacting schools and medical institutions, and reviewing learning plans.

[0259] Step 9:

[0260] Users regularly check for new data and notifications from the server and take further action as needed. This continuous process optimizes children's health and learning on a daily basis.

[0261] (Example 1)

[0262] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0263] The challenge lies in efficiently managing children's health and learning progress at home using a single system, even remotely and in real time. In particular, it is necessary to integrate and analyze physiological characteristics and learning progress data so that parents can immediately take appropriate action.

[0264] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0265] In this invention, the server includes means for transmitting data on the child's physiological characteristics and educational progress to a central processing unit, means for analyzing the data received by the central processing unit to determine the child's health status and educational progress, and means for transmitting notifications and suggestions to the parent's information processing unit based on the analysis results. This enables parents to comprehensively understand their child's health and learning progress and take immediate action.

[0266] "Children's physiological characteristics data" refers to data that indicates a child's physical condition, such as heart rate, body temperature, and activity level.

[0267] "Educational progress data" refers to data obtained through educational applications and systems that shows children's learning outcomes, learning speed, and level of comprehension.

[0268] "Devices" refer to wearable devices attached to a child's body, as well as tablets and computers used to record educational progress data.

[0269] A "central processing unit" refers to a computer server used to analyze various types of data it receives.

[0270] "Analysis" refers to the act of processing received data and analyzing it to determine health status or learning comprehension.

[0271] "Parental information processing device" refers to a device (smartphone or tablet) used by parents to receive analyzed data and notifications.

[0272] A "visualized management screen" refers to a dashboard or interface used by parents to remotely monitor their child's status and visually review data.

[0273] An "individualized education plan" refers to a learning program that is tailored to each child's characteristics and progress.

[0274] This invention is a system for remotely managing a child's health status and learning progress within the home. The system utilizes a device for acquiring and transmitting the child's physiological characteristics data and educational progress data, a central processing unit, and an information processing unit used by the parent or guardian.

[0275] The device periodically acquires physiological characteristic data such as heart rate, body temperature, and activity level through a wearable device worn by the child. Additionally, the child's tablet or computer records educational progress data, such as learning progress and correct answer rates, through educational apps. This data is transmitted to the central processing unit in an encrypted state using a specified protocol.

[0276] The central processing unit (the server) analyzes the received data using AI algorithms to detect abnormalities in the child's health. For example, if a sudden increase in heart rate is detected, it is notified to the parent as a sign of stress. It also adjusts individualized educational plans according to the child's learning progress and suggests educational support. The analysis results are transmitted in real time to the parent's information processing unit, allowing the parent to check the information through a visualized management screen and take appropriate action quickly.

[0277] For example, if a child's heart rate is abnormal while wearing a wearable device after school, that data is immediately sent to the central processing unit. The server instantly analyzes the data and notifies the parent of a possible stress level, allowing the parent to immediately consider concrete measures such as rest or environmental improvements. Another example of a prompt to be input to the generating AI model is the question, "How can the learning plan be adjusted to improve learning efficiency?" In this way, parents can accurately understand and effectively manage their child's health and learning status.

[0278] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0279] Step 1:

[0280] The terminal collects physiological characteristic data through a wearable device worn by the child. As specific operations, the heart rate sensor measures the heart rate every second, and the body temperature sensor records the body temperature every five minutes. The inputs are the heart rate and body temperature data, and the output is that this data is temporarily stored in the buffer in the terminal.

[0281] Step 2:

[0282] The terminal uses the child's tablet to obtain education progress data from an education application. Specifically, it records the usage status of the application, the task completion rate, and the number of correct answers. The input is the progress data from the learning application, and the output is that these progress data are saved in the terminal as a log file.

[0283] Step 3:

[0284] The terminal combines the collected physiological characteristic data and education progress data and sends the data to the central processing unit. Here, the data is encrypted using the HTTPS protocol and sent to the server every five minutes. The inputs are the physiological characteristic data and education progress data, and the output is the encoded data sent to the server.

[0285] Step 4:

[0286] The server decrypts the received data and filters the data as needed. Specifically, it performs outlier detection and missing data complementation to generate a data set suitable for analysis. The input is the data sent to the server, and the output is a cleansed data set.

[0287] Step 5:

[0288] The server uses a generated AI model to perform analysis and determine the child's health status and educational progress. The AI ​​model analyzes heart rate variability in relation to stress indicators and generates proposed adjustments to the educational plan based on the training data. The input is a cleansed dataset, and the output is a health status report and proposed adjustments to the educational plan.

[0289] Step 6:

[0290] The server sends a notification to the parent's information processing device based on the analysis results. Here, the analysis results and suggestions are delivered immediately via push notifications. Inputs include health status reports and proposed adjustments to the educational plan, and output is the notification content displayed on the parent's device.

[0291] Step 7:

[0292] Parents, as users, manage their children's status based on the notifications they receive and take necessary actions. They can check information through the management screen and, for example, set break times based on increased stress levels or adjust learning schedules. Input is the notification content, and output is the implementation of measures to improve the child's health and education based on the parent's actions.

[0293] (Application Example 1)

[0294] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0295] Today, personalized health-based recommendation services are limited in consumer behavior. A system is needed that analyzes an individual's health status and consumer behavior in real time and suggests appropriate products and services based on the results. However, existing technologies present a challenge in effectively supporting consumers' health-based purchasing behavior within stores.

[0296] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0297] In this invention, the server includes means for using equipment to acquire an individual's physiological state data and consumer behavior data, means for transmitting the physiological state data and consumer behavior data to a data storage device, and means for analyzing the data received by the data storage device and evaluating the state of health and consumer behavior. This makes it possible to personalize consumer behavior linked to an individual's health status.

[0298] "Physiological status data" refers to data that indicates an individual's physical health, such as heart rate and body temperature.

[0299] "Consumer behavior data" refers to data that shows the actions individuals take, such as purchasing or selecting products, at stores and other locations.

[0300] "Means using equipment" refers to methods that use devices or systems used to acquire personal data.

[0301] A "data storage device" refers to a device or system used to store acquired data and analyze it.

[0302] "Means of analysis" refers to methods and devices used to analyze collected data and evaluate an individual's condition.

[0303] "Recommendation information" refers to product information and service suggestions provided to individuals based on analysis results.

[0304] An "information display device" is a device or apparatus used to visually present analysis results and recommendation information to an individual.

[0305] To implement this invention, equipment for acquiring personal physiological state data and consumption behavior data is required. This includes wearable devices, smart glasses, etc. used by individuals. The data obtained from these devices is transmitted through wireless communication to a cloud-based data storage device. Examples of cloud services to be used include AWS and Microsoft Azure.

[0306] The server analyzes the physiological state data and consumption behavior data received by the data storage device, and uses AI algorithms to evaluate the health state and consumption patterns. Based on this evaluation, product recommendation information optimized for the individual's health state is generated.

[0307] Smart glasses as an information display device visually display the recommendation information sent from the server. With this information, individuals can perform health-conscious consumption behavior in real time within the store. An example of a specific prompt sentence is, "When this customer has a high heart rate, I want to display an introduction to relaxing products." This enables the proposal of appropriate services tailored to the individual's health state.

[0308] With this system configuration, consumers in physical stores can enjoy a personalized consumption experience while considering their own health state.

[0309] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0310] Step 1:

[0311] The terminal acquires physiological state data and consumption behavior data from wearable devices and smart glasses worn by an individual. The acquired data includes physiological indicators such as heart rate and body temperature, and consumption behavior based on temporary shopping choices and movement routes. These data are collected by sensors and prepared to be transmitted to the server via wireless communication.

[0312] Step 2:

[0313] The server receives physiological status data and consumer behavior data transmitted from the terminal. Based on the received data, it uses a generative AI model to perform analysis and detect patterns in health status and consumer behavior. During this process, the AI ​​algorithm detects anomalies and performs trend analysis to extract insights into health status and consumer behavior.

[0314] Step 3:

[0315] Based on the analysis results, the server generates recommendation information to suggest products and services tailored to the individual's health condition. The generated recommendation information is optimized for the individual's current state and includes suggestions such as items to reduce stress. This information is then processed within the server and converted into an appropriate format.

[0316] Step 4:

[0317] The server sends the generated recommendation information to the terminal's information display device, such as smart glasses. The terminal visually presents the received information to the user. Based on this information, the user can adjust their shopping behavior in the store and make healthier choices. In this process, the user receives real-time feedback via the terminal and can modify their behavior based on their health status.

[0318] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0319] This invention provides more sophisticated support in a system for remotely managing a child's health and learning progress by incorporating an emotion engine. The system consists of a device worn by the child, a server that processes data, a terminal that receives the analysis results, and an emotion engine that recognizes the user's emotions.

[0320] The device retrieves data.

[0321] Wearable devices used by children acquire physiological data such as heart rate and body temperature. Simultaneously, learning devices collect activity and progress during learning. In addition, the devices are equipped with sensors that capture the child's facial expressions and voice, and an emotion engine analyzes their emotional state.

[0322] The server analyzes the data.

[0323] The server receives physiological data, learning data, and emotional data transmitted from the terminal. Based on the received data, an AI algorithm performs an evaluation, analyzing the emotional state in addition to the health status and learning progress. This emotional analysis identifies specific emotions such as stress and anxiety, and the server stores this information in a database.

[0324] Providing notifications and advice

[0325] The server generates notifications and advice for parents based on the analysis results. The generated notifications include information about the child's health status, learning progress, and emotional state. For example, if the emotional data indicates that the child is experiencing stress, a notification such as, "Your child may have been experiencing stress recently. Please provide them with time to relax as needed," will be sent.

[0326] User behavior

[0327] Users can view a dashboard provided through their parent's device. The dashboard displays detailed graphs and charts for intuitive data understanding, integrating physiological, learning, and emotional data. Based on this information, users can make appropriate decisions and create a safe and secure environment for their child.

[0328] As a concrete example, consider a scenario where a child's facial expressions are analyzed via camera during remote learning, and the emotion engine recognizes signs of stress. The server aggregates and analyzes emotional data indicating stress, along with physiological data and learning progress. Parents are immediately notified, and they can check the dashboard and take appropriate action based on their child's situation. In this way, the system aims to maintain the child's overall health and optimal learning environment.

[0329] The following describes the processing flow.

[0330] Step 1:

[0331] The device acquires the child's physiological data. Wearable devices periodically record data such as heart rate and body temperature. Learning devices also collect application data and assignment progress used by the child during learning.

[0332] Step 2:

[0333] The device captures the child's facial expressions using its camera. It features an emotion engine that analyzes the child's emotional state in real time based on the acquired facial data. This data, along with voice, is used for emotion analysis.

[0334] Step 3:

[0335] The device sends acquired physiological data, learning data, and emotional data to the server. This allows all data to be centrally managed.

[0336] Step 4:

[0337] The server saves the received data to a database. By recording it chronologically, it maintains a state where it can be compared with past data.

[0338] Step 5:

[0339] The server uses AI algorithms to analyze stored data. Physiological data is used to assess health status, and learning data is used to measure progress and understanding. Emotional data is used to determine emotions such as stress and anxiety.

[0340] Step 6:

[0341] The server sends notifications and advice to the parent's device based on the analysis results. The notifications comprehensively cover the child's health, learning progress, and emotional state.

[0342] Step 7:

[0343] The user checks the dashboard provided on their device. The dashboard visually displays various data, allowing parents to grasp their child's current situation at a glance.

[0344] Step 8:

[0345] Users determine appropriate responses for their children based on the information on the dashboard. Specifically, this includes providing rest when stress is recognized and offering support according to their learning progress.

[0346] Step 9:

[0347] Users receive information from the server through their devices on a daily basis, enabling continuous monitoring. This allows for constant optimization of children's health and learning environment.

[0348] (Example 2)

[0349] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0350] There is a growing need to provide more accurate support by comprehensively understanding a child's health, learning progress, and emotional state. However, conventional systems often collect and analyze this information separately, making it difficult to make a comprehensive judgment. Furthermore, the technology for evaluating emotional states is insufficient, which can lead to delays in response.

[0351] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0352] In this invention, the server includes means for integrally analyzing physiological information, learning information, and emotional information using AI; means for generating notifications and guidance based on the analysis results and providing them to parents quickly; and means for visualizing detailed information, including emotional information, so that parents can check it in real time. This makes it possible to immediately grasp the multifaceted state of the child and provide appropriate support.

[0353] "Physiological information" refers to data that indicates a child's physical condition, such as heart rate, body temperature, and steps taken.

[0354] "Learning information" refers to data related to children's educational activities, such as study time, problem-solving status, and accuracy rate.

[0355] "Emotional information" refers to data about a child's emotional state obtained through the analysis of their facial expressions and voice.

[0356] "Device" refers to equipment used to collect physiological information, learning information, and emotional information.

[0357] A "computer" is a computer system used to analyze received physiological, learning, and emotional information.

[0358] A "notification" is an informational or guidance message generated based on the analysis results and sent to the parent.

[0359] "Visualized information display" refers to dashboards or screens that visually show a child's condition using graphs, charts, and other visual aids.

[0360] This invention is a system for comprehensively managing a child's health, learning progress, and emotional state. The following components are used to implement the invention.

[0361] The device collects data.

[0362] The device uses a wearable device attached to the child to acquire physiological information such as heart rate, body temperature, and steps taken. It also collects learning information such as study time and problem-solving rate from a digital learning platform. Furthermore, it uses the camera and microphone built into the device to capture the child's facial expressions and voice, and an emotion engine generates emotional information.

[0363] The server analyzes the data.

[0364] The server receives and analyzes physiological, learning, and emotional information sent from the terminal. Using a generative AI model, the server comprehensively processes this information to evaluate health status, learning progress, and emotional state. This analysis utilizes machine learning algorithms and database technologies, and is updated in real time.

[0365] Users utilize data

[0366] Parents, as users, can visually check information using a dashboard provided through their device. The dashboard displays graphs showing health status, charts showing learning progress, and icons indicating changes in emotional state. This allows users to make an overall assessment and consider appropriate responses based on their child's condition.

[0367] As a concrete example, when a child is taking an online class, facial expression data is captured via camera, and stress levels are recognized through an emotion engine. The results are analyzed on a server, and a notification such as "Your child appears to be stressed. We recommend they take a break" is sent to the parent's device. Further improvements in accuracy are expected through analysis using "generative AI models" and "prompt messages."

[0368] An example of a prompt is, "How can we accurately detect a child's emotional state and notify them in real time?" In this way, the system enables the management of a child's multifaceted state and the provision of appropriate support.

[0369] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0370] Step 1:

[0371] The device retrieves data.

[0372] The device uses a wearable device to acquire physiological information such as heart rate, body temperature, and steps taken in real time. It also collects learning information such as learning time and answer rate through a learning application. Furthermore, it uses the device's camera and microphone to record facial expressions and voice, and generates emotional information for the emotion engine. The input is data from each sensor, and the output is digitized physiological information, learning information, and emotional information.

[0373] Step 2:

[0374] The device sends data to the server.

[0375] The device transmits collected physiological, learning, and emotional information to the server at regular intervals. The data is encrypted using a secure communication protocol. The input is the information on the device, and the output is the dataset sent to the server.

[0376] Step 3:

[0377] The server analyzes the data.

[0378] The server uses a generative AI model to analyze the received data. It evaluates health status from physiological information, learning progress from learning information, and emotional state from emotional information. The algorithm analyzes the data and detects outliers and specific patterns. The input is the dataset sent to the server, and the output is the evaluation result from the analysis.

[0379] Step 4:

[0380] The server generates notifications and advice.

[0381] The server generates notifications and advice for parents based on the analysis results. For example, if a stressed state is detected, a message such as "Your child is stressed. We recommend they take a break" will be generated. The input is the evaluation result from the analysis, and the output is the content of the notification.

[0382] Step 5:

[0383] The user checks the dashboard.

[0384] Through the provided dashboard, users can visually monitor their health, learning progress, and emotional state. The dashboard displays graphs and charts, which users use to make appropriate decisions. The input is visualized information based on analysis results, and the output is the user's understanding and response.

[0385] (Application Example 2)

[0386] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0387] In production environments, it is crucial to monitor workers' health and stress levels in real time and to promptly issue instructions for appropriate rest and work adjustments. However, currently, there are limited means to achieve this efficiently, and maintaining productivity and managing worker health in the workplace is essential.

[0388] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0389] In this invention, the server includes means for using a device for collecting physiological and emotional data of workers, means for transmitting the physiological and emotional data to a central processing unit, and means for analyzing the data received by the central processing unit and evaluating the worker's health status and work efficiency. This makes it possible to monitor the worker's health status and work efficiency in real time and provide appropriate notifications and advice to managers as needed.

[0390] A "worker" is a human employee who is responsible for various tasks in a factory or production site.

[0391] "Physiological data" refers to data that indicates the physical condition of a worker, such as heart rate, body temperature, and blood pressure.

[0392] "Emotional data" refers to data about an worker's emotional state obtained by analyzing their facial expressions and voice.

[0393] "Device" refers to a virtual equipment or device that is worn or used by an operator to collect necessary data.

[0394] A "central processing unit" is a control computer or server that receives various types of data and performs analysis and interpretation.

[0395] "Analysis" is a procedure that uses data collected by the central processing unit to evaluate the health status and work efficiency of workers.

[0396] In a mode for carrying out the invention, the system is for monitoring the health and emotional state of workers in real time and providing appropriate instructions to managers. The system consists of a wearable device worn by the worker, a central processing unit (server), and a terminal used by the manager.

[0397] First, the wearable device used by the worker is equipped with sensors to monitor heart rate and body temperature, and a camera to analyze the worker's facial expressions. This makes it possible to acquire physiological and emotional data in real time. The acquired data is then transmitted from the device to a central processing unit.

[0398] Next, the server receives this data and analyzes it using AI algorithms. Specifically, it uses machine learning frameworks like TensorFlow to evaluate the health status and stress levels of individual workers. As a result of the analysis, advice based on the worker's condition is generated.

[0399] The generated advice is sent as a notification to the administrator's terminal. The administrator can review this and quickly provide appropriate instructions or work adjustments to workers. This allows for maintaining productivity on the worksite while also managing the health of the workers.

[0400] For example, if a worker has a higher-than-normal heart rate and facial analysis reveals signs of stress, the server will notify the administrator with a message stating, "We recommend taking a 15-minute break depending on the situation." In this case, an example of a prompt message for the generating AI model would be, "The worker's heart rate is higher than normal, and facial analysis revealed signs of stress. Please generate a message to provide appropriate advice."

[0401] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0402] Step 1:

[0403] The wearable device worn by the worker collects heart rate, body temperature, and facial expression data in real time. Physiological and emotional data are acquired as input and transmitted digitally to a central processing unit. The data is collected from sensors and temporarily stored within the device.

[0404] Step 2:

[0405] The server receives physiological and emotional data transmitted from the wearable device. The received data is then analyzed by an AI algorithm. Specifically, the data is first compared to the normal range to determine whether or not an abnormal value has formed.

[0406] Step 3:

[0407] The server evaluates the worker's health status and work efficiency based on the analysis results. Using the processed input data, it utilizes generative AI models such as TensorFlow to make judgments about stress levels and work efficiency. Here, data processing is performed, such as identifying abnormal heart rates and peaks in tension.

[0408] Step 4:

[0409] The server generates a notification message for the administrator based on the evaluation results. It passes the results obtained from the analysis as prompts to an AI model, which then outputs an appropriate advice message. A possible example of this might be a message like, "This worker is experiencing increased stress. We suggest a 15-minute break."

[0410] Step 5:

[0411] The user (administrator) receives notifications sent to the terminal and checks the worker's status through the dashboard. Based on the advice displayed as output, they provide specific instructions to the worker and adjust their work content. Specific actions include issuing break instructions and reassigning tasks.

[0412] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0413] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0414] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0415] [Third Embodiment]

[0416] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0417] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0418] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0419] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0420] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0421] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0422] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0423] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0424] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0425] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0426] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0427] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0428] This invention relates to a system for remotely managing and optimizing a child's health status and learning progress within the home. The system consists of a device or learning terminal worn by the child, a server for collecting and analyzing data, and a terminal used by parents or guardians.

[0429] The device retrieves data.

[0430] First, wearable devices worn by children collect physiological data such as heart rate and body temperature. Simultaneously, tablets and computers used by children record learning data, such as progress in learning apps and completion status of tasks. This data is transmitted to a server periodically or in real time.

[0431] The server analyzes the data.

[0432] The server analyzes the received physiological and learning data. The AI ​​algorithms on the server detect anomalies and trends in the data, for example, evaluating whether significant fluctuations in heart rate might be a sign of stress. Furthermore, by analyzing the learning data, it measures learning comprehension and task completion, and adjusts the learning plan accordingly. Based on the analyzed data, the server generates feedback for parents regarding the child's health and learning.

[0433] Notifications and feedback

[0434] Based on the server's analysis, notifications and advice are sent to the parent's device. This allows parents to monitor their child's health and learning progress in real time. For example, if there are heart rate fluctuations suggestive of stress, a notification such as, "Please pay attention to your child's recent heart rate fluctuations. Adequate rest is recommended," will be sent.

[0435] User behavior

[0436] Users (parents) can view this information on the provided dashboard. The dashboard visually displays the data, allowing parents to take specific actions based on it (e.g., managing break times, communicating with the school). Furthermore, by utilizing the dashboard, parents can monitor the progress of each child's learning plan and provide learning support as needed.

[0437] As a concrete example, consider a case where a child wears a wearable device after school. If the device detects an abnormal heart rate, the data is immediately sent to a server. If the server's analysis suggests a stress response, the parent receives the information on their smartphone and can quickly consider countermeasures. In this way, the system enables parents to effectively manage their child's health and learning remotely.

[0438] The following describes the processing flow.

[0439] Step 1:

[0440] The device acquires the child's physiological and learning data. Wearable devices record heart rate and body temperature, while learning devices such as tablets collect data on the use of learning apps and progress on assignments.

[0441] Step 2:

[0442] The device sends acquired physiological and training data to the server. This transmission can be done periodically or in real time.

[0443] Step 3:

[0444] The server saves the received data to a database. This makes subsequent analysis and historical reference easier.

[0445] Step 4:

[0446] The server uses AI algorithms to analyze the stored data. It determines whether there are any abnormalities in physiological data or delays in training data, and evaluates the health status and learning comprehension level.

[0447] Step 5:

[0448] The server generates notifications and advice for the parent based on the analysis results. For example, if there are health concerns, it will create a specific alert message.

[0449] Step 6:

[0450] The server sends the generated notification to the parent's device. This allows the user to receive real-time updates on the child's status.

[0451] Step 7:

[0452] Users can use the provided dashboard to check their child's health status and learning progress, along with notifications. The dashboard includes detailed data and visual information through graphs.

[0453] Step 8:

[0454] Users use the information on the dashboard to decide on appropriate actions for their children and take concrete steps. This includes contacting schools and medical institutions, and reviewing learning plans.

[0455] Step 9:

[0456] Users regularly check for new data and notifications from the server and take further action as needed. This continuous process optimizes children's health and learning on a daily basis.

[0457] (Example 1)

[0458] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0459] The challenge lies in efficiently managing children's health and learning progress at home using a single system, even remotely and in real time. In particular, it is necessary to integrate and analyze physiological characteristics and learning progress data so that parents can immediately take appropriate action.

[0460] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0461] In this invention, the server includes means for transmitting data on the child's physiological characteristics and educational progress to a central processing unit, means for analyzing the data received by the central processing unit to determine the child's health status and educational progress, and means for transmitting notifications and suggestions to the parent's information processing unit based on the analysis results. This enables parents to comprehensively understand their child's health and learning progress and take immediate action.

[0462] "Children's physiological characteristics data" refers to data that indicates a child's physical condition, such as heart rate, body temperature, and activity level.

[0463] "Educational progress data" refers to data obtained through educational applications and systems that shows children's learning outcomes, learning speed, and level of comprehension.

[0464] "Devices" refer to wearable devices attached to a child's body, as well as tablets and computers used to record educational progress data.

[0465] A "central processing unit" refers to a computer server used to analyze various types of data it receives.

[0466] "Analysis" refers to the act of processing received data and analyzing it to determine health status or learning comprehension.

[0467] "Parental information processing device" refers to a device (smartphone or tablet) used by parents to receive analyzed data and notifications.

[0468] A "visualized management screen" refers to a dashboard or interface used by parents to remotely monitor their child's status and visually review data.

[0469] An "individualized education plan" refers to a learning program that is tailored to each child's characteristics and progress.

[0470] This invention is a system for remotely managing a child's health status and learning progress within the home. The system utilizes a device for acquiring and transmitting the child's physiological characteristics data and educational progress data, a central processing unit, and an information processing unit used by the parent or guardian.

[0471] The device periodically acquires physiological characteristic data such as heart rate, body temperature, and activity level through a wearable device worn by the child. Additionally, the child's tablet or computer records educational progress data, such as learning progress and correct answer rates, through educational apps. This data is transmitted to the central processing unit in an encrypted state using a specified protocol.

[0472] The central processing unit (the server) analyzes the received data using AI algorithms to detect abnormalities in the child's health. For example, if a sudden increase in heart rate is detected, it is notified to the parent as a sign of stress. It also adjusts individualized educational plans according to the child's learning progress and suggests educational support. The analysis results are transmitted in real time to the parent's information processing unit, allowing the parent to check the information through a visualized management screen and take appropriate action quickly.

[0473] For example, if a child's heart rate is abnormal while wearing a wearable device after school, that data is immediately sent to the central processing unit. The server instantly analyzes the data and notifies the parent of a possible stress level, allowing the parent to immediately consider concrete measures such as rest or environmental improvements. Another example of a prompt to be input to the generating AI model is the question, "How can the learning plan be adjusted to improve learning efficiency?" In this way, parents can accurately understand and effectively manage their child's health and learning status.

[0474] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0475] Step 1:

[0476] The device collects physiological characteristic data through a wearable device attached to the child. Specifically, a heart rate sensor measures heart rate every second, and a body temperature sensor records body temperature every five minutes. The inputs are heart rate and body temperature data, and the output is that this data is temporarily stored in a buffer within the device.

[0477] Step 2:

[0478] The device uses a child's tablet to retrieve educational progress data from educational applications. Specifically, it records application usage, task completion rate, and number of correct answers. The input is progress data from the learning app, and the output is this progress data saved as a log file on the device.

[0479] Step 3:

[0480] The terminal collects physiological characteristics data and educational progress data and sends the data to the central processing unit. Here, the data is encrypted using the HTTPS protocol and sent to the server every 5 minutes. The inputs are physiological characteristics data and educational progress data, and the output is the encoded data sent to the server.

[0481] Step 4:

[0482] The server decodes the received data and filters it as needed. Specifically, it performs anomaly detection and missing data imputation to generate a dataset suitable for analysis. The input is the data sent to the server, and the output is a cleansed dataset.

[0483] Step 5:

[0484] The server uses a generated AI model to perform analysis and determine the child's health status and educational progress. The AI ​​model analyzes heart rate variability in relation to stress indicators and generates proposed adjustments to the educational plan based on the training data. The input is a cleansed dataset, and the output is a health status report and proposed adjustments to the educational plan.

[0485] Step 6:

[0486] The server sends a notification to the parent's information processing device based on the analysis results. Here, the analysis results and suggestions are delivered immediately via push notifications. Inputs include health status reports and proposed adjustments to the educational plan, and output is the notification content displayed on the parent's device.

[0487] Step 7:

[0488] Parents, as users, manage their children's status based on the notifications they receive and take necessary actions. They can check information through the management screen and, for example, set break times based on increased stress levels or adjust learning schedules. Input is the notification content, and output is the implementation of measures to improve the child's health and education based on the parent's actions.

[0489] (Application Example 1)

[0490] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0491] Today, personalized health-based recommendation services are limited in consumer behavior. A system is needed that analyzes an individual's health status and consumer behavior in real time and suggests appropriate products and services based on the results. However, existing technologies present a challenge in effectively supporting consumers' health-based purchasing behavior within stores.

[0492] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0493] In this invention, the server includes means for using equipment to acquire an individual's physiological state data and consumer behavior data, means for transmitting the physiological state data and consumer behavior data to a data storage device, and means for analyzing the data received by the data storage device and evaluating the state of health and consumer behavior. This makes it possible to personalize consumer behavior linked to an individual's health status.

[0494] "Physiological status data" refers to data that indicates an individual's physical health, such as heart rate and body temperature.

[0495] "Consumer behavior data" refers to data that shows the actions individuals take, such as purchasing or selecting products, at stores and other locations.

[0496] "Means using equipment" refers to methods that use devices or systems used to acquire personal data.

[0497] A "data storage device" refers to a device or system used to store acquired data and analyze it.

[0498] "Means of analysis" refers to methods and devices used to analyze collected data and evaluate an individual's condition.

[0499] "Recommendation information" refers to product information and service suggestions provided to individuals based on analysis results.

[0500] An "information display device" is a device or apparatus used to visually present analysis results and recommendation information to an individual.

[0501] To implement this invention, equipment is needed to acquire individual physiological state data and consumer behavior data. This includes wearable devices and smart glasses used by the individual. The data acquired from these devices is transmitted wirelessly to a cloud-based data storage device. Examples of cloud services that can be used include AWS and Microsoft Azure.

[0502] The server analyzes physiological state data and consumer behavior data received by the data storage device and uses an AI algorithm to evaluate health status and consumption patterns. Based on this evaluation, it generates product recommendation information optimized for the individual's health status.

[0503] Smart glasses, acting as information display devices, visually display recommendation information transmitted from a server. This information allows individuals to engage in health-conscious purchasing behavior in stores in real time. An example of a specific prompt message is, "If this customer's heart rate is high, we want to display recommendations for relaxing products." This enables the suggestion of appropriate services tailored to the individual's health condition.

[0504] This system configuration allows consumers in physical stores to enjoy a personalized shopping experience while taking their health status into consideration.

[0505] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0506] Step 1:

[0507] The terminal acquires physiological state data and consumer behavior data from wearable devices and smart glasses worn by individuals. This acquired data includes physiological indicators such as heart rate and body temperature, as well as consumer behavior based on temporary shopping choices and movement patterns. This data is collected by sensors and prepared to be transmitted to a server via wireless communication.

[0508] Step 2:

[0509] The server receives physiological status data and consumer behavior data transmitted from the terminal. Based on the received data, it uses a generative AI model to perform analysis and detect patterns in health status and consumer behavior. During this process, the AI ​​algorithm detects anomalies and performs trend analysis to extract insights into health status and consumer behavior.

[0510] Step 3:

[0511] Based on the analysis results, the server generates recommendation information to suggest products and services tailored to the individual's health condition. The generated recommendation information is optimized for the individual's current state and includes suggestions such as items to reduce stress. This information is then processed within the server and converted into an appropriate format.

[0512] Step 4:

[0513] The server sends the generated recommendation information to the terminal's information display device, such as smart glasses. The terminal visually presents the received information to the user. Based on this information, the user can adjust their shopping behavior in the store and make healthier choices. In this process, the user receives real-time feedback via the terminal and can modify their behavior based on their health status.

[0514] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0515] This invention provides more sophisticated support in a system for remotely managing a child's health and learning progress by incorporating an emotion engine. The system consists of a device worn by the child, a server that processes data, a terminal that receives the analysis results, and an emotion engine that recognizes the user's emotions.

[0516] The device retrieves data.

[0517] Wearable devices used by children acquire physiological data such as heart rate and body temperature. Simultaneously, learning devices collect activity and progress during learning. In addition, the devices are equipped with sensors that capture the child's facial expressions and voice, and an emotion engine analyzes their emotional state.

[0518] The server analyzes the data.

[0519] The server receives physiological data, learning data, and emotional data transmitted from the terminal. Based on the received data, an AI algorithm performs an evaluation, analyzing the emotional state in addition to the health status and learning progress. This emotional analysis identifies specific emotions such as stress and anxiety, and the server stores this information in a database.

[0520] Providing notifications and advice

[0521] The server generates notifications and advice for parents based on the analysis results. The generated notifications include information about the child's health status, learning progress, and emotional state. For example, if the emotional data indicates that the child is experiencing stress, a notification such as, "Your child may have been experiencing stress recently. Please provide them with time to relax as needed," will be sent.

[0522] User behavior

[0523] Users can view a dashboard provided through their parent's device. The dashboard displays detailed graphs and charts for intuitive data understanding, integrating physiological, learning, and emotional data. Based on this information, users can make appropriate decisions and create a safe and secure environment for their child.

[0524] As a concrete example, consider a scenario where a child's facial expressions are analyzed via camera during remote learning, and the emotion engine recognizes signs of stress. The server aggregates and analyzes emotional data indicating stress, along with physiological data and learning progress. Parents are immediately notified, and they can check the dashboard and take appropriate action based on their child's situation. In this way, the system aims to maintain the child's overall health and optimal learning environment.

[0525] The following describes the processing flow.

[0526] Step 1:

[0527] The device acquires the child's physiological data. Wearable devices periodically record data such as heart rate and body temperature. Learning devices also collect application data and assignment progress used by the child during learning.

[0528] Step 2:

[0529] The device captures the child's facial expressions using its camera. It features an emotion engine that analyzes the child's emotional state in real time based on the acquired facial data. This data, along with voice, is used for emotion analysis.

[0530] Step 3:

[0531] The device sends acquired physiological data, learning data, and emotional data to the server. This allows all data to be centrally managed.

[0532] Step 4:

[0533] The server saves the received data to a database. By recording it chronologically, it maintains a state where it can be compared with past data.

[0534] Step 5:

[0535] The server uses AI algorithms to analyze stored data. Physiological data is used to assess health status, and learning data is used to measure progress and understanding. Emotional data is used to determine emotions such as stress and anxiety.

[0536] Step 6:

[0537] The server sends notifications and advice to the parent's device based on the analysis results. The notifications comprehensively cover the child's health, learning progress, and emotional state.

[0538] Step 7:

[0539] The user checks the dashboard provided on their device. The dashboard visually displays various data, allowing parents to grasp their child's current situation at a glance.

[0540] Step 8:

[0541] Users determine appropriate responses for their children based on the information on the dashboard. Specifically, this includes providing rest when stress is recognized and offering support according to their learning progress.

[0542] Step 9:

[0543] Users receive information from the server through their devices on a daily basis, enabling continuous monitoring. This allows for constant optimization of children's health and learning environment.

[0544] (Example 2)

[0545] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0546] There is a growing need to provide more accurate support by comprehensively understanding a child's health, learning progress, and emotional state. However, conventional systems often collect and analyze this information separately, making it difficult to make a comprehensive judgment. Furthermore, the technology for evaluating emotional states is insufficient, which can lead to delays in response.

[0547] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0548] In this invention, the server includes means for integrally analyzing physiological information, learning information, and emotional information using AI; means for generating notifications and guidance based on the analysis results and providing them to parents quickly; and means for visualizing detailed information, including emotional information, so that parents can check it in real time. This makes it possible to immediately grasp the multifaceted state of the child and provide appropriate support.

[0549] "Physiological information" refers to data that indicates a child's physical condition, such as heart rate, body temperature, and steps taken.

[0550] "Learning information" refers to data related to children's educational activities, such as study time, problem-solving status, and accuracy rate.

[0551] "Emotional information" refers to data about a child's emotional state obtained through the analysis of their facial expressions and voice.

[0552] "Device" refers to equipment used to collect physiological information, learning information, and emotional information.

[0553] A "computer" is a computer system used to analyze received physiological, learning, and emotional information.

[0554] A "notification" is an informational or guidance message generated based on the analysis results and sent to the parent.

[0555] "Visualized information display" refers to dashboards or screens that visually show a child's condition using graphs, charts, and other visual aids.

[0556] This invention is a system for comprehensively managing a child's health, learning progress, and emotional state. The following components are used to implement the invention.

[0557] The device collects data.

[0558] The device uses a wearable device attached to the child to acquire physiological information such as heart rate, body temperature, and steps taken. It also collects learning information such as study time and problem-solving rate from a digital learning platform. Furthermore, it uses the camera and microphone built into the device to capture the child's facial expressions and voice, and an emotion engine generates emotional information.

[0559] The server analyzes the data.

[0560] The server receives and analyzes physiological, learning, and emotional information sent from the terminal. Using a generative AI model, the server comprehensively processes this information to evaluate health status, learning progress, and emotional state. This analysis utilizes machine learning algorithms and database technologies, and is updated in real time.

[0561] Users utilize data

[0562] Parents, as users, can visually check information using a dashboard provided through their device. The dashboard displays graphs showing health status, charts showing learning progress, and icons indicating changes in emotional state. This allows users to make an overall assessment and consider appropriate responses based on their child's condition.

[0563] As a concrete example, when a child is taking an online class, facial expression data is captured via camera, and stress levels are recognized through an emotion engine. The results are analyzed on a server, and a notification such as "Your child appears to be stressed. We recommend they take a break" is sent to the parent's device. Further improvements in accuracy are expected through analysis using "generative AI models" and "prompt messages."

[0564] An example of a prompt is, "How can we accurately detect a child's emotional state and notify them in real time?" In this way, the system enables the management of a child's multifaceted state and the provision of appropriate support.

[0565] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0566] Step 1:

[0567] The device retrieves data.

[0568] The device uses a wearable device to acquire physiological information such as heart rate, body temperature, and steps taken in real time. It also collects learning information such as learning time and answer rate through a learning application. Furthermore, it uses the device's camera and microphone to record facial expressions and voice, and generates emotional information for the emotion engine. The input is data from each sensor, and the output is digitized physiological information, learning information, and emotional information.

[0569] Step 2:

[0570] The device sends data to the server.

[0571] The device transmits collected physiological, learning, and emotional information to the server at regular intervals. The data is encrypted using a secure communication protocol. The input is the information on the device, and the output is the dataset sent to the server.

[0572] Step 3:

[0573] The server analyzes the data.

[0574] The server uses a generative AI model to analyze the received data. It evaluates health status from physiological information, learning progress from learning information, and emotional state from emotional information. The algorithm analyzes the data and detects outliers and specific patterns. The input is the dataset sent to the server, and the output is the evaluation result from the analysis.

[0575] Step 4:

[0576] The server generates notifications and advice.

[0577] The server generates notifications and advice for parents based on the analysis results. For example, if a stressed state is detected, a message such as "Your child is stressed. We recommend they take a break" will be generated. The input is the evaluation result from the analysis, and the output is the content of the notification.

[0578] Step 5:

[0579] The user checks the dashboard.

[0580] Through the provided dashboard, users can visually monitor their health, learning progress, and emotional state. The dashboard displays graphs and charts, which users use to make appropriate decisions. The input is visualized information based on analysis results, and the output is the user's understanding and response.

[0581] (Application Example 2)

[0582] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0583] In production environments, it is crucial to monitor workers' health and stress levels in real time and to promptly issue instructions for appropriate rest and work adjustments. However, currently, there are limited means to achieve this efficiently, and maintaining productivity and managing worker health in the workplace is essential.

[0584] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0585] In this invention, the server includes means for using a device for collecting physiological and emotional data of workers, means for transmitting the physiological and emotional data to a central processing unit, and means for analyzing the data received by the central processing unit and evaluating the worker's health status and work efficiency. This makes it possible to monitor the worker's health status and work efficiency in real time and provide appropriate notifications and advice to managers as needed.

[0586] A "worker" is a human employee who is responsible for various tasks in a factory or production site.

[0587] "Physiological data" refers to data that indicates the physical condition of a worker, such as heart rate, body temperature, and blood pressure.

[0588] "Emotional data" refers to data about an worker's emotional state obtained by analyzing their facial expressions and voice.

[0589] "Device" refers to a virtual equipment or device that is worn or used by an operator to collect necessary data.

[0590] A "central processing unit" is a control computer or server that receives various types of data and performs analysis and interpretation.

[0591] "Analysis" is a procedure that uses data collected by the central processing unit to evaluate the health status and work efficiency of workers.

[0592] In a mode for carrying out the invention, the system is for monitoring the health and emotional state of workers in real time and providing appropriate instructions to managers. The system consists of a wearable device worn by the worker, a central processing unit (server), and a terminal used by the manager.

[0593] First, the wearable device used by the worker is equipped with sensors to monitor heart rate and body temperature, and a camera to analyze the worker's facial expressions. This makes it possible to acquire physiological and emotional data in real time. The acquired data is then transmitted from the device to a central processing unit.

[0594] Next, the server receives this data and analyzes it using AI algorithms. Specifically, it uses machine learning frameworks like TensorFlow to evaluate the health status and stress levels of individual workers. As a result of the analysis, advice based on the worker's condition is generated.

[0595] The generated advice is sent as a notification to the administrator's terminal. The administrator can review this and quickly provide appropriate instructions or work adjustments to workers. This allows for maintaining productivity on the worksite while also managing the health of the workers.

[0596] For example, if a worker has a higher-than-normal heart rate and facial analysis reveals signs of stress, the server will notify the administrator with a message stating, "We recommend taking a 15-minute break depending on the situation." In this case, an example of a prompt message for the generating AI model would be, "The worker's heart rate is higher than normal, and facial analysis revealed signs of stress. Please generate a message to provide appropriate advice."

[0597] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0598] Step 1:

[0599] The wearable device worn by the worker collects heart rate, body temperature, and facial expression data in real time. Physiological and emotional data are acquired as input and transmitted digitally to a central processing unit. The data is collected from sensors and temporarily stored within the device.

[0600] Step 2:

[0601] The server receives physiological and emotional data transmitted from the wearable device. The received data is then analyzed by an AI algorithm. Specifically, the data is first compared to the normal range to determine whether or not an abnormal value has formed.

[0602] Step 3:

[0603] The server evaluates the worker's health status and work efficiency based on the analysis results. Using the processed input data, it utilizes generative AI models such as TensorFlow to make judgments about stress levels and work efficiency. Here, data processing is performed, such as identifying abnormal heart rates and peaks in tension.

[0604] Step 4:

[0605] The server generates a notification message for the administrator based on the evaluation results. It passes the results obtained from the analysis as prompts to an AI model, which then outputs an appropriate advice message. A possible example of this might be a message like, "This worker is experiencing increased stress. We suggest a 15-minute break."

[0606] Step 5:

[0607] The user (administrator) receives notifications sent to the terminal and checks the worker's status through the dashboard. Based on the advice displayed as output, they provide specific instructions to the worker and adjust their work content. Specific actions include issuing break instructions and reassigning tasks.

[0608] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0609] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0610] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0611] [Fourth Embodiment]

[0612] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0613] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0614] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0615] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0616] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0617] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0618] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0619] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0620] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0621] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0622] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0623] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0624] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0625] This invention relates to a system for remotely managing and optimizing a child's health status and learning progress within the home. The system consists of a device or learning terminal worn by the child, a server for collecting and analyzing data, and a terminal used by parents or guardians.

[0626] The device retrieves data.

[0627] First, wearable devices worn by children collect physiological data such as heart rate and body temperature. Simultaneously, tablets and computers used by children record learning data, such as progress in learning apps and completion status of tasks. This data is transmitted to a server periodically or in real time.

[0628] The server analyzes the data.

[0629] The server analyzes the received physiological and learning data. The AI ​​algorithms on the server detect anomalies and trends in the data, for example, evaluating whether significant fluctuations in heart rate might be a sign of stress. Furthermore, by analyzing the learning data, it measures learning comprehension and task completion, and adjusts the learning plan accordingly. Based on the analyzed data, the server generates feedback for parents regarding the child's health and learning.

[0630] Notifications and feedback

[0631] Based on the server's analysis, notifications and advice are sent to the parent's device. This allows parents to monitor their child's health and learning progress in real time. For example, if there are heart rate fluctuations suggestive of stress, a notification such as, "Please pay attention to your child's recent heart rate fluctuations. Adequate rest is recommended," will be sent.

[0632] User behavior

[0633] Users (parents) can view this information on the provided dashboard. The dashboard visually displays the data, allowing parents to take specific actions based on it (e.g., managing break times, communicating with the school). Furthermore, by utilizing the dashboard, parents can monitor the progress of each child's learning plan and provide learning support as needed.

[0634] As a concrete example, consider a case where a child wears a wearable device after school. If the device detects an abnormal heart rate, the data is immediately sent to a server. If the server's analysis suggests a stress response, the parent receives the information on their smartphone and can quickly consider countermeasures. In this way, the system enables parents to effectively manage their child's health and learning remotely.

[0635] The following describes the processing flow.

[0636] Step 1:

[0637] The device acquires the child's physiological and learning data. Wearable devices record heart rate and body temperature, while learning devices such as tablets collect data on the use of learning apps and progress on assignments.

[0638] Step 2:

[0639] The device sends acquired physiological and training data to the server. This transmission can be done periodically or in real time.

[0640] Step 3:

[0641] The server saves the received data to a database. This makes subsequent analysis and historical reference easier.

[0642] Step 4:

[0643] The server uses AI algorithms to analyze the stored data. It determines whether there are any abnormalities in physiological data or delays in training data, and evaluates the health status and learning comprehension level.

[0644] Step 5:

[0645] The server generates notifications and advice for the parent based on the analysis results. For example, if there are health concerns, it will create a specific alert message.

[0646] Step 6:

[0647] The server sends the generated notification to the parent's device. This allows the user to receive real-time updates on the child's status.

[0648] Step 7:

[0649] Users can use the provided dashboard to check their child's health status and learning progress, along with notifications. The dashboard includes detailed data and visual information through graphs.

[0650] Step 8:

[0651] Users use the information on the dashboard to decide on appropriate actions for their children and take concrete steps. This includes contacting schools and medical institutions, and reviewing learning plans.

[0652] Step 9:

[0653] Users regularly check for new data and notifications from the server and take further action as needed. This continuous process optimizes children's health and learning on a daily basis.

[0654] (Example 1)

[0655] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0656] The challenge lies in efficiently managing children's health and learning progress at home using a single system, even remotely and in real time. In particular, it is necessary to integrate and analyze physiological characteristics and learning progress data so that parents can immediately take appropriate action.

[0657] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0658] In this invention, the server includes means for transmitting data on the child's physiological characteristics and educational progress to a central processing unit, means for analyzing the data received by the central processing unit to determine the child's health status and educational progress, and means for transmitting notifications and suggestions to the parent's information processing unit based on the analysis results. This enables parents to comprehensively understand their child's health and learning progress and take immediate action.

[0659] "Children's physiological characteristics data" refers to data that indicates a child's physical condition, such as heart rate, body temperature, and activity level.

[0660] "Educational progress data" refers to data obtained through educational applications and systems that shows children's learning outcomes, learning speed, and level of comprehension.

[0661] "Devices" refer to wearable devices attached to a child's body, as well as tablets and computers used to record educational progress data.

[0662] A "central processing unit" refers to a computer server used to analyze various types of data it receives.

[0663] "Analysis" refers to the act of processing received data and analyzing it to determine health status or learning comprehension.

[0664] "Parental information processing device" refers to a device (smartphone or tablet) used by parents to receive analyzed data and notifications.

[0665] A "visualized management screen" refers to a dashboard or interface used by parents to remotely monitor their child's status and visually review data.

[0666] An "individualized education plan" refers to a learning program that is tailored to each child's characteristics and progress.

[0667] This invention is a system for remotely managing a child's health status and learning progress within the home. The system utilizes a device for acquiring and transmitting the child's physiological characteristics data and educational progress data, a central processing unit, and an information processing unit used by the parent or guardian.

[0668] The device periodically acquires physiological characteristic data such as heart rate, body temperature, and activity level through a wearable device worn by the child. Additionally, the child's tablet or computer records educational progress data, such as learning progress and correct answer rates, through educational apps. This data is transmitted to the central processing unit in an encrypted state using a specified protocol.

[0669] The central processing unit (the server) analyzes the received data using AI algorithms to detect abnormalities in the child's health. For example, if a sudden increase in heart rate is detected, it is notified to the parent as a sign of stress. It also adjusts individualized educational plans according to the child's learning progress and suggests educational support. The analysis results are transmitted in real time to the parent's information processing unit, allowing the parent to check the information through a visualized management screen and take appropriate action quickly.

[0670] For example, if a child's heart rate is abnormal while wearing a wearable device after school, that data is immediately sent to the central processing unit. The server instantly analyzes the data and notifies the parent of a possible stress level, allowing the parent to immediately consider concrete measures such as rest or environmental improvements. Another example of a prompt to be input to the generating AI model is the question, "How can the learning plan be adjusted to improve learning efficiency?" In this way, parents can accurately understand and effectively manage their child's health and learning status.

[0671] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0672] Step 1:

[0673] The device collects physiological characteristic data through a wearable device attached to the child. Specifically, a heart rate sensor measures heart rate every second, and a body temperature sensor records body temperature every five minutes. The inputs are heart rate and body temperature data, and the output is that this data is temporarily stored in a buffer within the device.

[0674] Step 2:

[0675] The device uses a child's tablet to retrieve educational progress data from educational applications. Specifically, it records application usage, task completion rate, and number of correct answers. The input is progress data from the learning app, and the output is this progress data saved as a log file on the device.

[0676] Step 3:

[0677] The terminal collects physiological characteristics data and educational progress data and sends the data to the central processing unit. Here, the data is encrypted using the HTTPS protocol and sent to the server every 5 minutes. The inputs are physiological characteristics data and educational progress data, and the output is the encoded data sent to the server.

[0678] Step 4:

[0679] The server decodes the received data and filters it as needed. Specifically, it performs anomaly detection and missing data imputation to generate a dataset suitable for analysis. The input is the data sent to the server, and the output is a cleansed dataset.

[0680] Step 5:

[0681] The server uses a generated AI model to perform analysis and determine the child's health status and educational progress. The AI ​​model analyzes heart rate variability in relation to stress indicators and generates proposed adjustments to the educational plan based on the training data. The input is a cleansed dataset, and the output is a health status report and proposed adjustments to the educational plan.

[0682] Step 6:

[0683] The server sends a notification to the parent's information processing device based on the analysis results. Here, the analysis results and suggestions are delivered immediately via push notifications. Inputs include health status reports and proposed adjustments to the educational plan, and output is the notification content displayed on the parent's device.

[0684] Step 7:

[0685] Parents, as users, manage their children's status based on the notifications they receive and take necessary actions. They can check information through the management screen and, for example, set break times based on increased stress levels or adjust learning schedules. Input is the notification content, and output is the implementation of measures to improve the child's health and education based on the parent's actions.

[0686] (Application Example 1)

[0687] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0688] Today, personalized health-based recommendation services are limited in consumer behavior. A system is needed that analyzes an individual's health status and consumer behavior in real time and suggests appropriate products and services based on the results. However, existing technologies present a challenge in effectively supporting consumers' health-based purchasing behavior within stores.

[0689] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0690] In this invention, the server includes means for using equipment to acquire an individual's physiological state data and consumer behavior data, means for transmitting the physiological state data and consumer behavior data to a data storage device, and means for analyzing the data received by the data storage device and evaluating the state of health and consumer behavior. This makes it possible to personalize consumer behavior linked to an individual's health status.

[0691] "Physiological status data" refers to data that indicates an individual's physical health, such as heart rate and body temperature.

[0692] "Consumer behavior data" refers to data that shows the actions individuals take, such as purchasing or selecting products, at stores and other locations.

[0693] "Means using equipment" refers to methods that use devices or systems used to acquire personal data.

[0694] A "data storage device" refers to a device or system used to store acquired data and analyze it.

[0695] "Means of analysis" refers to methods and devices used to analyze collected data and evaluate an individual's condition.

[0696] "Recommendation information" refers to product information and service suggestions provided to individuals based on analysis results.

[0697] An "information display device" is a device or apparatus used to visually present analysis results and recommendation information to an individual.

[0698] To implement this invention, equipment is needed to acquire individual physiological state data and consumer behavior data. This includes wearable devices and smart glasses used by the individual. The data acquired from these devices is transmitted wirelessly to a cloud-based data storage device. Examples of cloud services that can be used include AWS and Microsoft Azure.

[0699] The server analyzes physiological state data and consumer behavior data received by the data storage device and uses an AI algorithm to evaluate health status and consumption patterns. Based on this evaluation, it generates product recommendation information optimized for the individual's health status.

[0700] Smart glasses, acting as information display devices, visually display recommendation information transmitted from a server. This information allows individuals to engage in health-conscious purchasing behavior in stores in real time. An example of a specific prompt message is, "If this customer's heart rate is high, we want to display recommendations for relaxing products." This enables the suggestion of appropriate services tailored to the individual's health condition.

[0701] This system configuration allows consumers in physical stores to enjoy a personalized shopping experience while taking their health status into consideration.

[0702] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0703] Step 1:

[0704] The terminal acquires physiological state data and consumer behavior data from wearable devices and smart glasses worn by individuals. This acquired data includes physiological indicators such as heart rate and body temperature, as well as consumer behavior based on temporary shopping choices and movement patterns. This data is collected by sensors and prepared to be transmitted to a server via wireless communication.

[0705] Step 2:

[0706] The server receives physiological status data and consumer behavior data transmitted from the terminal. Based on the received data, it uses a generative AI model to perform analysis and detect patterns in health status and consumer behavior. During this process, the AI ​​algorithm detects anomalies and performs trend analysis to extract insights into health status and consumer behavior.

[0707] Step 3:

[0708] Based on the analysis results, the server generates recommendation information to suggest products and services tailored to the individual's health condition. The generated recommendation information is optimized for the individual's current state and includes suggestions such as items to reduce stress. This information is then processed within the server and converted into an appropriate format.

[0709] Step 4:

[0710] The server sends the generated recommendation information to the terminal's information display device, such as smart glasses. The terminal visually presents the received information to the user. Based on this information, the user can adjust their shopping behavior in the store and make healthier choices. In this process, the user receives real-time feedback via the terminal and can modify their behavior based on their health status.

[0711] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0712] This invention provides more sophisticated support in a system for remotely managing a child's health and learning progress by incorporating an emotion engine. The system consists of a device worn by the child, a server that processes data, a terminal that receives the analysis results, and an emotion engine that recognizes the user's emotions.

[0713] The device retrieves data.

[0714] Wearable devices used by children acquire physiological data such as heart rate and body temperature. Simultaneously, learning devices collect activity and progress during learning. In addition, the devices are equipped with sensors that capture the child's facial expressions and voice, and an emotion engine analyzes their emotional state.

[0715] The server analyzes the data.

[0716] The server receives physiological data, learning data, and emotional data transmitted from the terminal. Based on the received data, an AI algorithm performs an evaluation, analyzing the emotional state in addition to the health status and learning progress. This emotional analysis identifies specific emotions such as stress and anxiety, and the server stores this information in a database.

[0717] Providing notifications and advice

[0718] The server generates notifications and advice for parents based on the analysis results. The generated notifications include information about the child's health status, learning progress, and emotional state. For example, if the emotional data indicates that the child is experiencing stress, a notification such as, "Your child may have been experiencing stress recently. Please provide them with time to relax as needed," will be sent.

[0719] User behavior

[0720] Users can view a dashboard provided through their parent's device. The dashboard displays detailed graphs and charts for intuitive data understanding, integrating physiological, learning, and emotional data. Based on this information, users can make appropriate decisions and create a safe and secure environment for their child.

[0721] As a concrete example, consider a scenario where a child's facial expressions are analyzed via camera during remote learning, and the emotion engine recognizes signs of stress. The server aggregates and analyzes emotional data indicating stress, along with physiological data and learning progress. Parents are immediately notified, and they can check the dashboard and take appropriate action based on their child's situation. In this way, the system aims to maintain the child's overall health and optimal learning environment.

[0722] The following describes the processing flow.

[0723] Step 1:

[0724] The device acquires the child's physiological data. Wearable devices periodically record data such as heart rate and body temperature. Learning devices also collect application data and assignment progress used by the child during learning.

[0725] Step 2:

[0726] The device captures the child's facial expressions using its camera. It features an emotion engine that analyzes the child's emotional state in real time based on the acquired facial data. This data, along with voice, is used for emotion analysis.

[0727] Step 3:

[0728] The device sends acquired physiological data, learning data, and emotional data to the server. This allows all data to be centrally managed.

[0729] Step 4:

[0730] The server saves the received data to a database. By recording it chronologically, it maintains a state where it can be compared with past data.

[0731] Step 5:

[0732] The server uses AI algorithms to analyze stored data. Physiological data is used to assess health status, and learning data is used to measure progress and understanding. Emotional data is used to determine emotions such as stress and anxiety.

[0733] Step 6:

[0734] The server sends notifications and advice to the parent's device based on the analysis results. The notifications comprehensively cover the child's health, learning progress, and emotional state.

[0735] Step 7:

[0736] The user checks the dashboard provided on their device. The dashboard visually displays various data, allowing parents to grasp their child's current situation at a glance.

[0737] Step 8:

[0738] Users determine appropriate responses for their children based on the information on the dashboard. Specifically, this includes providing rest when stress is recognized and offering support according to their learning progress.

[0739] Step 9:

[0740] Users receive information from the server through their devices on a daily basis, enabling continuous monitoring. This allows for constant optimization of children's health and learning environment.

[0741] (Example 2)

[0742] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0743] There is a growing need to provide more accurate support by comprehensively understanding a child's health, learning progress, and emotional state. However, conventional systems often collect and analyze this information separately, making it difficult to make a comprehensive judgment. Furthermore, the technology for evaluating emotional states is insufficient, which can lead to delays in response.

[0744] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0745] In this invention, the server includes means for integrally analyzing physiological information, learning information, and emotional information using AI; means for generating notifications and guidance based on the analysis results and providing them to parents quickly; and means for visualizing detailed information, including emotional information, so that parents can check it in real time. This makes it possible to immediately grasp the multifaceted state of the child and provide appropriate support.

[0746] "Physiological information" refers to data that indicates a child's physical condition, such as heart rate, body temperature, and steps taken.

[0747] "Learning information" refers to data related to children's educational activities, such as study time, problem-solving status, and accuracy rate.

[0748] "Emotional information" refers to data about a child's emotional state obtained through the analysis of their facial expressions and voice.

[0749] "Device" refers to equipment used to collect physiological information, learning information, and emotional information.

[0750] A "computer" is a computer system used to analyze received physiological, learning, and emotional information.

[0751] A "notification" is an informational or guidance message generated based on the analysis results and sent to the parent.

[0752] "Visualized information display" refers to dashboards or screens that visually show a child's condition using graphs, charts, and other visual aids.

[0753] This invention is a system for comprehensively managing a child's health, learning progress, and emotional state. The following components are used to implement the invention.

[0754] The device collects data.

[0755] The device uses a wearable device attached to the child to acquire physiological information such as heart rate, body temperature, and steps taken. It also collects learning information such as study time and problem-solving rate from a digital learning platform. Furthermore, it uses the camera and microphone built into the device to capture the child's facial expressions and voice, and an emotion engine generates emotional information.

[0756] The server analyzes the data.

[0757] The server receives and analyzes physiological, learning, and emotional information sent from the terminal. Using a generative AI model, the server comprehensively processes this information to evaluate health status, learning progress, and emotional state. This analysis utilizes machine learning algorithms and database technologies, and is updated in real time.

[0758] Users utilize data

[0759] Parents, as users, can visually check information using a dashboard provided through their device. The dashboard displays graphs showing health status, charts showing learning progress, and icons indicating changes in emotional state. This allows users to make an overall assessment and consider appropriate responses based on their child's condition.

[0760] As a concrete example, when a child is taking an online class, facial expression data is captured via camera, and stress levels are recognized through an emotion engine. The results are analyzed on a server, and a notification such as "Your child appears to be stressed. We recommend they take a break" is sent to the parent's device. Further improvements in accuracy are expected through analysis using "generative AI models" and "prompt messages."

[0761] An example of a prompt is, "How can we accurately detect a child's emotional state and notify them in real time?" In this way, the system enables the management of a child's multifaceted state and the provision of appropriate support.

[0762] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0763] Step 1:

[0764] The device retrieves data.

[0765] The device uses a wearable device to acquire physiological information such as heart rate, body temperature, and steps taken in real time. It also collects learning information such as learning time and answer rate through a learning application. Furthermore, it uses the device's camera and microphone to record facial expressions and voice, and generates emotional information for the emotion engine. The input is data from each sensor, and the output is digitized physiological information, learning information, and emotional information.

[0766] Step 2:

[0767] The device sends data to the server.

[0768] The device transmits collected physiological, learning, and emotional information to the server at regular intervals. The data is encrypted using a secure communication protocol. The input is the information on the device, and the output is the dataset sent to the server.

[0769] Step 3:

[0770] The server analyzes the data.

[0771] The server uses a generative AI model to analyze the received data. It evaluates health status from physiological information, learning progress from learning information, and emotional state from emotional information. The algorithm analyzes the data and detects outliers and specific patterns. The input is the dataset sent to the server, and the output is the evaluation result from the analysis.

[0772] Step 4:

[0773] The server generates notifications and advice.

[0774] The server generates notifications and advice for parents based on the analysis results. For example, if a stressed state is detected, a message such as "Your child is stressed. We recommend they take a break" will be generated. The input is the evaluation result from the analysis, and the output is the content of the notification.

[0775] Step 5:

[0776] The user checks the dashboard.

[0777] Through the provided dashboard, users can visually monitor their health, learning progress, and emotional state. The dashboard displays graphs and charts, which users use to make appropriate decisions. The input is visualized information based on analysis results, and the output is the user's understanding and response.

[0778] (Application Example 2)

[0779] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0780] In production environments, it is crucial to monitor workers' health and stress levels in real time and to promptly issue instructions for appropriate rest and work adjustments. However, currently, there are limited means to achieve this efficiently, and maintaining productivity and managing worker health in the workplace is essential.

[0781] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0782] In this invention, the server includes means for using a device for collecting physiological and emotional data of workers, means for transmitting the physiological and emotional data to a central processing unit, and means for analyzing the data received by the central processing unit and evaluating the worker's health status and work efficiency. This makes it possible to monitor the worker's health status and work efficiency in real time and provide appropriate notifications and advice to managers as needed.

[0783] A "worker" is a human employee who is responsible for various tasks in a factory or production site.

[0784] "Physiological data" refers to data that indicates the physical condition of a worker, such as heart rate, body temperature, and blood pressure.

[0785] "Emotional data" refers to data about an worker's emotional state obtained by analyzing their facial expressions and voice.

[0786] "Device" refers to a virtual equipment or device that is worn or used by an operator to collect necessary data.

[0787] A "central processing unit" is a control computer or server that receives various types of data and performs analysis and interpretation.

[0788] "Analysis" is a procedure that uses data collected by the central processing unit to evaluate the health status and work efficiency of workers.

[0789] In a mode for carrying out the invention, the system is for monitoring the health and emotional state of workers in real time and providing appropriate instructions to managers. The system consists of a wearable device worn by the worker, a central processing unit (server), and a terminal used by the manager.

[0790] First, the wearable device used by the worker is equipped with sensors to monitor heart rate and body temperature, and a camera to analyze the worker's facial expressions. This makes it possible to acquire physiological and emotional data in real time. The acquired data is then transmitted from the device to a central processing unit.

[0791] Next, the server receives this data and analyzes it using AI algorithms. Specifically, it uses machine learning frameworks like TensorFlow to evaluate the health status and stress levels of individual workers. As a result of the analysis, advice based on the worker's condition is generated.

[0792] The generated advice is sent as a notification to the administrator's terminal. The administrator can review this and quickly provide appropriate instructions or work adjustments to workers. This allows for maintaining productivity on the worksite while also managing the health of the workers.

[0793] For example, if a worker has a higher-than-normal heart rate and facial analysis reveals signs of stress, the server will notify the administrator with a message stating, "We recommend taking a 15-minute break depending on the situation." In this case, an example of a prompt message for the generating AI model would be, "The worker's heart rate is higher than normal, and facial analysis revealed signs of stress. Please generate a message to provide appropriate advice."

[0794] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0795] Step 1:

[0796] The wearable device worn by the worker collects heart rate, body temperature, and facial expression data in real time. Physiological and emotional data are acquired as input and transmitted digitally to a central processing unit. The data is collected from sensors and temporarily stored within the device.

[0797] Step 2:

[0798] The server receives physiological and emotional data transmitted from the wearable device. The received data is then analyzed by an AI algorithm. Specifically, the data is first compared to the normal range to determine whether or not an abnormal value has formed.

[0799] Step 3:

[0800] The server evaluates the worker's health status and work efficiency based on the analysis results. Using the processed input data, it utilizes generative AI models such as TensorFlow to make judgments about stress levels and work efficiency. Here, data processing is performed, such as identifying abnormal heart rates and peaks in tension.

[0801] Step 4:

[0802] The server generates a notification message for the administrator based on the evaluation results. It passes the results obtained from the analysis as prompts to an AI model, which then outputs an appropriate advice message. A possible example of this might be a message like, "This worker is experiencing increased stress. We suggest a 15-minute break."

[0803] Step 5:

[0804] The user (administrator) receives notifications sent to the terminal and checks the worker's status through the dashboard. Based on the advice displayed as output, they provide specific instructions to the worker and adjust their work content. Specific actions include issuing break instructions and reassigning tasks.

[0805] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0806] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0807] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0808] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0809] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0810] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0811] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0812] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0813] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0814] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0815] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0816] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0817] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0818] 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.

[0819] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0820] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0821] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0822] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0823] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0824] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0825] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0826] The following is further disclosed regarding the embodiments described above.

[0827] (Claim 1)

[0828] A means of using a device to collect children's physiological and learning data,

[0829] A means for transmitting the aforementioned physiological data and training data to a server,

[0830] A means for analyzing data received by the aforementioned server and evaluating health and learning status,

[0831] A means of sending notifications and advice to the parent's device based on the analysis results,

[0832] A system that includes this.

[0833] (Claim 2)

[0834] The system according to claim 1, which provides a dashboard for parents to check the status of their child in real time.

[0835] (Claim 3)

[0836] The system according to claim 1, which generates and provides a personalized learning plan for each child.

[0837] "Example 1"

[0838] (Claim 1)

[0839] A means of using a device to acquire data on children's physiological characteristics and educational progress,

[0840] Means for transmitting the aforementioned physiological characteristics data and educational progress data to a central processing unit,

[0841] The central processing unit analyzes the data received and determines the health status and educational progress.

[0842] A means for transmitting notifications and suggestions to the parent's information processing device based on the analysis results,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, which provides a visualized management screen for parents to immediately monitor the status of their children.

[0846] (Claim 3)

[0847] The system according to claim 1, which generates and provides an individualized educational plan for each child.

[0848] "Application Example 1"

[0849] (Claim 1)

[0850] A means of using equipment to acquire individual physiological state data and consumer behavior data,

[0851] Means for transmitting the aforementioned physiological state data and consumer behavior data to a data storage device,

[0852] A means for analyzing the data received by the data storage device and evaluating the state of health and consumer behavior,

[0853] A means for transmitting recommendation information to a personal information display device based on the analysis results,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, which provides a visualization system for individuals to check their own status in real time and make appropriate consumption decisions.

[0857] (Claim 3)

[0858] The system according to claim 1, which generates and provides a personalized consumption plan.

[0859] "Example 2 of combining an emotion engine"

[0860] (Claim 1)

[0861] A means of using a device for collecting physiological information, learning information, and emotional information,

[0862] Means for transmitting the aforementioned physiological information, learning information, and emotional information to a computer,

[0863] A means for analyzing information received by the aforementioned computer using AI to evaluate health status, learning progress, and emotional state,

[0864] A means of sending notifications and guidance to parents via their devices based on the analysis results,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, which provides a visualized information display for parents to check the status of their child in real time.

[0868] (Claim 3)

[0869] The system according to claim 1, further comprising means for evaluating a child's emotional state based on emotional information.

[0870] "Application example 2 when combining with an emotional engine"

[0871] (Claim 1)

[0872] A means of using a device to collect physiological and emotional data of workers,

[0873] Means for transmitting the aforementioned physiological data and emotional data to a central processing unit,

[0874] The central processing unit analyzes the data received and provides means for evaluating health status and work efficiency,

[0875] A means of sending notifications and advice to the administrator's terminal based on the analysis results,

[0876] A system that includes this.

[0877] (Claim 2)

[0878] The system according to claim 1, which provides a display device for an administrator to check the status of an employee in real time.

[0879] (Claim 3)

[0880] The system according to claim 1, which generates and provides an optimized work plan for each worker. [Explanation of symbols]

[0881] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of using a device to collect children's physiological and learning data, A means for transmitting the aforementioned physiological data and training data to a server, A means for analyzing data received by the aforementioned server and evaluating health and learning status, A means of sending notifications and advice to the parent's device based on the analysis results, A system that includes this.

2. The system according to claim 1, which provides a dashboard for parents to check the status of their child in real time.

3. The system according to claim 1, which generates and provides a personalized learning plan for each child.

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