system

Abstract

We provide the system. [Solution] Methods for collecting data on children's characteristics, A means of processing the collected data and analyzing children's interests and personalities, Based on the analysis results, a means to recommend the optimal learning activities, A means of notifying parents and children of the recommendation results, A means of continuously monitoring the progress of activities and providing feedback, Means for visually displaying this information, 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, 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] When choosing a learning activity suitable for a child, there is a problem that the guardian cannot fully understand the child's interests and talents, and it takes time and effort to choose the optimal activity. Also, if the selected learning activity does not match the child's growth and interests, there is a risk of lack of continuous motivation and hindrance to the development of talents. The prior art does not provide a systematic method for effectively solving these problems.

Means for Solving the Problems

[0005] This invention provides a system that collects and analyzes children's characteristic data and automatically recommends appropriate learning activities based on that data. Specifically, it includes means for collecting data to understand children's interests and personalities, and proposes personalized activities by analyzing the collected data. It also includes means for continuously monitoring the progress of activities and makes necessary improvements or suggestions for new activities based on feedback. The invention solves the problem by visually displaying this information so that parents can easily track their child's growth.

[0006] "Means of collecting data" refers to devices or methods for collecting information related to a child's characteristics.

[0007] "Means for processing and analyzing data" refers to the functions or processes necessary to examine the collected characteristic data of children and identify characteristics such as interests and personality.

[0008] "Means of recommending learning activities" refers to functions and methods that select and suggest activities suitable for children based on analyzed data.

[0009] "Means of notification" refers to the process or system for informing children and their guardians of the results of their learning activity recommendations.

[0010] "Means for monitoring progress and providing feedback" refer to systems and methods for checking the progress of children's learning activities and providing feedback on their achievements and areas for improvement.

[0011] "Means of visual display" refers to devices or methods for presenting collected and analyzed information in an easily viewable format. [Brief explanation of the drawing]

[0012] [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] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention relates to a system that recommends optimal learning activities based on a child's characteristics. This system collects and analyzes data on the child's personality, interests, and past activities, and then proposes learning activities and provides feedback based on this information. In this process, it also incorporates the opinions of parents and children, enabling proposals that satisfy both parties.

[0034] The device plays a role in recording the child's daily activities, collecting data through questionnaires and activity logs. For example, it gathers information such as what books the child reads and what sports activities they participate in.

[0035] After receiving this data, the server uses a generating AI to analyze it. This analysis identifies the child's areas of interest and personality traits, and then selects learning activities based on these.

[0036] Once the server has completed its selection process, the results will be notified to the user via their terminal. This notification will include a list of recommended learning activities, along with a detailed description of each activity.

[0037] Users can choose activities based on the information presented. Furthermore, parents and children can make decisions together, and their opinions are reflected in future recommendations.

[0038] Once an activity is selected, the server continuously monitors its progress and collects relevant feedback, including reports from teachers and coaches. Based on this feedback, the server suggests improvements as needed and recommends new activities.

[0039] All progress data is updated in real time and displayed visually through the device. This dashboard allows users to easily understand their child's growth and learning progress. In this way, the present invention is a system that provides parents and children with all the information and support they need to make better choices.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The device collects records of the child's daily activities. This includes data such as activity type, frequency, and duration, using mobile apps or wearable devices. For example, exercise, reading, and digital media use are recorded.

[0043] Step 2:

[0044] The server receives data sent from the terminal and performs data cleaning. This removes invalid data, standardizes the format, and converts the data into a state suitable for analysis.

[0045] Step 3:

[0046] The server uses generative AI to analyze personality and interests based on the organized data. Here, natural language processing and image recognition technologies are used to identify features and patterns extracted from the data.

[0047] Step 4:

[0048] The server uses the analysis results as input to run a recommendation algorithm and select learning activities suitable for the child. Past success stories and data from other children with similar profiles are also taken into consideration.

[0049] Step 5:

[0050] The device notifies the child and their guardian of a list of recommended learning activities. This notification includes detailed information about each activity and the reasons for its selection.

[0051] Step 6:

[0052] Users select learning activities through their devices. This selection process can be done jointly by parents and children, and the results are reflected in recommendations for future activities.

[0053] Step 7:

[0054] The server collects feedback from devices, teachers, and coaches to monitor the progress of selected learning activities. This allows for an understanding of the level of achievement and satisfaction with the activities.

[0055] Step 8:

[0056] The server generates a progress report based on the feedback and presents it to the user. The report includes the current progress, areas for improvement, and advice on the next steps.

[0057] Step 9:

[0058] The device updates a real-time dashboard, allowing users to visually monitor their child's growth and learning progress. It displays data intuitively using graphs and charts.

[0059] (Example 1)

[0060] 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."

[0061] In recent years, education based on children's individuality has become increasingly important, but discovering and recommending the most suitable learning activities for each child is not easy. In particular, achieving education that matches a child's interests and personality requires individual data analysis, and the technology to perform this efficiently is needed. Furthermore, it is desirable to consistently provide activity recommendations and feedback based on the analysis results, but achieving this presents the challenge of building a complex system.

[0062] 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.

[0063] In this invention, the server includes a device for collecting children's attribute data, a device for processing the acquired data and analyzing the children's interests and personalities, and a device for selecting appropriate learning activities based on the analysis results. This makes it possible to select learning activities tailored to each individual child.

[0064] "Child attribute data" refers to information used to provide individualized education, such as a child's interests, personality, and past activity records.

[0065] "Analysis" refers to the process of identifying children's interests and personality traits based on collected data.

[0066] "Learning activities" refer to specific actions or programs proposed to children for educational or growth-promoting purposes.

[0067] A "server" refers to a computer system that processes, stores, analyzes, and communicates data.

[0068] "Device" refers to a general term for hardware or software designed to perform a specific function.

[0069] A "generative AI model" refers to an artificial intelligence algorithm designed for data analysis or generating specific outputs.

[0070] A "prompt statement" refers to an input statement used to control the output of a generative AI model.

[0071] "Notification" refers to the act of communicating analysis results and recommended activities to the user.

[0072] "Evaluation" refers to feedback regarding the progress and results of activities, and it is the basis for determining the next steps.

[0073] This invention relates to a system for optimizing educational activities based on a child's characteristics. Specifically, it includes a process of analyzing a child's interests, personality, and past activity records, and using this information to select appropriate learning activities.

[0074] The device is the primary device used to collect children's attribute data. This device typically consists of a smartphone or tablet, which records daily activity logs and collects data in the form of questionnaires. For example, it might record information such as what books a child reads or what sports they participate in.

[0075] The collected data is sent to a server. This server is equipped with functions to process and analyze the data, and uses generative AI models to identify areas of interest and personality traits of children. Analysis tools such as Python and R are used for data analysis. This makes it possible to select the most suitable learning activities for each child.

[0076] Selected learning activities are notified to the user via their device. This information allows parents and children to choose the best activities together. Furthermore, the selected activities are continuously monitored by a server, and feedback is provided based on progress. This includes reports from educators and instructors.

[0077] This information is displayed visually, making it easy for users to understand their child's growth and learning progress. For example, one example of a prompt is, "Based on recent activities, identify the most suitable learning activities for this child."

[0078] In this way, this invention is an embodiment for customizing the educational experience for individual children and enabling the selection and adjustment of optimal learning activities.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The device collects attribute data from children's daily activities. Specifically, it obtains information through questionnaires and activity log applications. The data entered includes information such as children's reading habits and the activities they participate in. This data is then organized for subsequent analysis.

[0082] Step 2:

[0083] The terminal encrypts the collected attribute data and sends it to the server. SSL / TLS protocols are used to ensure secure data transfer. The input is encrypted attribute data, and the output is the data arriving at the server.

[0084] Step 3:

[0085] The server saves the received data to a database and begins the analysis. A generative AI model processes the data using a Python analysis library. For example, it might use the prompt "Identify the most suitable learning activities for this child based on their recent activities" to select appropriate learning activities based on the child's interests and personality. The input to the analysis is the saved attribute data, and the output is a list of learning activities selected based on the child's characteristics.

[0086] Step 4:

[0087] The server sends the analysis results to the terminal. The terminal displays the suggested learning activities in a user-friendly interface. The input is a list of selected learning activities, and the output is a visually designed user interface.

[0088] Step 5:

[0089] Users select learning activities based on the information presented. This selection can be made jointly by parents and children, and feedback is collected during this process. The input consists of the user's selections and opinions, while the output is feedback that helps improve the accuracy of future analyses.

[0090] Step 6:

[0091] The server receives reports from educators and instructors to monitor the progress of selected activities. This allows for the suggestion of new ideas and improvements as needed. The input is the activity progress report, and the output is improvement suggestions.

[0092] Step 7:

[0093] The device visually displays all progress data and feedback in real time, making it easy for users to track their child's progress. Input is the latest progress data and feedback, and output is a visualized dashboard.

[0094] (Application Example 1)

[0095] 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."

[0096] Currently, there is a problem in that optimal learning activities tailored to each child's individual characteristics are not being adequately proposed. Furthermore, there is a lack of means for parents and educators to efficiently grasp the progress of children's learning activities and effectively utilize feedback. Therefore, there is a need for learning support methods that maximize children's interests and growth.

[0097] 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.

[0098] In this invention, the server includes means for collecting data on a child's characteristics, means for processing the collected data and analyzing the child's interests and personality, and means for interacting with the child through a home-use device and informing them of suggested learning activities through voice and display. This makes it possible to stimulate the child's interests and suggest personalized learning activities.

[0099] "Child characteristics data" refers to information about a child's personality, interests, and past activities.

[0100] "Means of collection" refers to methods of obtaining data through everyday activities or surveys.

[0101] "Methods of analysis" refer to the process of identifying a child's interests and personality based on the collected data.

[0102] "Optimal learning activities" are those selected based on a child's characteristics and data, designed to maximize the child's learning effectiveness.

[0103] "Means of notification" refers to methods of communicating the results of recommended learning activities to parents and children.

[0104] "Monitoring methods" refer to methods for continuously tracking and recording the progress of learning activities.

[0105] "Means of providing feedback" refers to the process of providing evaluations and suggestions for improvement regarding the progress of learning activities.

[0106] A "means of visual display" refers to an interface for presenting information visually.

[0107] "Household machines" refer to robots and electronic devices used within the home.

[0108] "Generative AI technology" is an artificial intelligence technology that automatically generates insights from data with minimal human intervention.

[0109] 1. Generate a program for the system that implements this application example.

[0110] This invention provides a system that supports learning based on a child's characteristics. A server collects data on the child's characteristics and analyzes it using a generative AI model. Through this analysis, the child's interests and personality are identified, and the optimal learning activities are selected. The selection results are communicated to the user via voice and display through a home device.

[0111] The server utilizes speech recognition technology to transcribe collected audio and activity data of children into text. Cloud computing feeds this data into a generative AI model, which generates suggestions for learning activities tailored to the child's interests. These suggestions are displayed and notified via in-home devices in a way that is easily understood visually and audibly.

[0112] For example, the server analyzes collected data to determine that a child's preference is "insect observation," and recommends insect-related books and observation activities. This information is then communicated via voice from a device in the home, such as, "Let's borrow some books from the library for insect observation." An example of a prompt message could be, "Please recommend new learning activities for a child who is interested in insects."

[0113] This system allows parents and children to receive information tailored to the child's interests and characteristics, enabling them to select learning activities more appropriately.

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] The server collects data on the child's characteristics from the device. The device obtains daily activity data and survey information and sends it to the server. The input consists of activity logs and data related to interests, which the server receives and stores.

[0117] Step 2:

[0118] The server performs analysis using a generative AI model based on the collected data. The input is the characteristic data received in step 1. The server inputs this data into the AI ​​model and processes it to estimate the child's interests and personality. The output is the child's characteristic assessment information based on the analysis.

[0119] Step 3:

[0120] The server selects the optimal learning activity based on the generated characteristic assessment information. Here, estimated interests and personality traits are used as input, and suitable learning activities are searched and extracted from the database. The output is a list of recommended learning activities.

[0121] Step 4:

[0122] The server notifies the user of selected learning activities via a home device. The input is a list of recommended learning activities, which is converted into audio and visual information and sent to the hardware. The user receives the notification through the device's voice function and display.

[0123] Step 5:

[0124] The user selects a learning activity based on notifications from the server. Input in this step is a notification from the device, and the selected activity is returned to the server. The server records this information and uses it to recommend future activities.

[0125] Step 6:

[0126] The server continuously monitors the progress of selected learning activities and collects feedback. Input is progress data obtained during the activity, and output is evaluation and feedback on areas for improvement based on this data. This allows the server to accumulate data for suggesting new activities.

[0127] 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.

[0128] This invention relates to a system that recommends optimal learning activities based on a child's characteristics and recognizes the user's emotions to provide feedback. By incorporating an emotion engine, this system provides an appropriate learning environment according to the child's emotional state and enables emotion-based feedback and adjustment of activities.

[0129] The device plays a role in collecting data on the child's daily activities and emotions. Activity data is obtained through mobile apps and wearable devices and includes the child's behavior, preferences, and activity logs. Emotional data, on the other hand, is collected by recording voice tone and facial expressions.

[0130] The server receives data sent from the terminal and performs data cleaning and sentiment analysis. A sentiment engine assists in this process, identifying the user's emotional state from voice and facial expressions. The analysis results consider emotional trends in addition to the child's personality and interests.

[0131] The server then recommends learning activities suitable for the child based on the collected and analyzed data. These recommendations are tailored to the user's emotions; for example, if the user is experiencing high stress levels, it can suggest activities that promote relaxation.

[0132] The device notifies children and their guardians of the results of recommended learning activities. The information provided includes details of the activities and the reasons for their selection, based on the user's sentiment.

[0133] When a user selects an activity based on the information presented, the server monitors the progress based on that selection and collects necessary feedback. This includes adjusting the feedback in response to changes in the user's emotions.

[0134] Progress information and feedback are updated in real time via the device and displayed visually. This allows users to understand their child's growth and learning progress along with their emotional responses. In this way, the present invention is a system that not only provides an optimal learning environment for children but also leverages insights gained from emotions to realize a more personalized educational experience.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] The device collects data on the child's daily activities and emotions. Activity data is obtained through a mobile app or wearable device, while emotional data is acquired by recording voice tone and facial expressions in video.

[0138] Step 2:

[0139] The server receives data sent from the terminal. First, it performs data cleaning to remove and correct incomplete or incorrect data.

[0140] Step 3:

[0141] The server uses an emotion engine to analyze voice and facial expression data to identify the user's emotional state. For example, voice analysis quantifies the degree of emotion, while facial expression analysis evaluates subtle facial movements.

[0142] Step 4:

[0143] The server combines analyzed personal characteristics and emotional data, and applies a recommendation algorithm to determine learning activities suitable for the child. Emotional data acts as a modifier in activity selection, suggesting activities that are optimal for the child's mental state.

[0144] Step 5:

[0145] The device notifies the child and parent of a list of selected learning activities. The notification includes details of each activity and an explanation of how they correspond to emotional data.

[0146] Step 6:

[0147] The user selects the activity they most agree with based on the information provided. In this process, parents and children can jointly make the optimal choice.

[0148] Step 7:

[0149] The server monitors the progress of the selected activities and collects relevant feedback. It also monitors user sentiment and adjusts the feedback in real time.

[0150] Step 8:

[0151] The server analyzes the collected feedback and sentiment data and generates new suggestions and advice as needed.

[0152] Step 9:

[0153] The device updates progress and feedback information on a real-time dashboard, visually displaying it to allow users to intuitively understand their child's learning and emotional changes.

[0154] (Example 2)

[0155] 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".

[0156] In recent years, the importance of individual optimization and personalization in education has increased, but accurately understanding individual characteristics and emotional states and proposing appropriate educational activities remains a challenging task. In particular, technologies for analyzing individuals' emotional states in real time and providing educational activities accordingly are underdeveloped, and there is a need to improve the quality of education through such technologies. Furthermore, it is also important to continuously adjust educational activities through monitoring and feedback. This invention aims to solve these problems.

[0157] 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.

[0158] In this invention, the server includes means for acquiring individual attribute information, means for processing the acquired information and analyzing the individual's interests and personality, and means for performing emotion analysis and identifying the individual's emotional state. This makes it possible to adaptively provide educational activities to the individual based on their characteristics and emotional state. Furthermore, by providing emotionally appropriate feedback in real time along with suggesting educational activities, a personalized educational experience is realized.

[0159] "Individual" refers to the person receiving education or the user, who is the primary user of this invention.

[0160] "Attribute information" refers to all relevant information about an individual, including characteristic data, behavioral history, interests, and personality information.

[0161] "Means of analysis" refers to technical means used to identify and evaluate an individual's interests, personality, and emotional state based on collected data, and to utilize this information for educational activities.

[0162] "Educational activities" refer to a series of learning and training behaviors or programs proposed based on an individual's interests and emotional state.

[0163] "Means of notification" refers to technical means of informing individuals or supervisors of the proposed results of educational activities, and includes visual or auditory methods.

[0164] "Monitoring methods" refer to a series of processes for continuously observing and tracking the progress of an individual's activities and providing appropriate feedback.

[0165] "Means of visual presentation" refers to methods of visually displaying individual progress information and feedback, and includes technologies that provide information via displays.

[0166] "Emotional analysis" refers to a technology that identifies an individual's emotional state from their voice, facial expressions, etc., and this information is used to adjust educational activities.

[0167] This invention comprises a system that provides optimal educational activities based on individual characteristics and emotional states. The system mainly consists of a "terminal" and a "server," and the "user" uses this system to engage in learning activities. A specific embodiment is shown below.

[0168] The terminal is responsible for collecting individual attribute information. The terminal is configured as a smartphone or wearable device, and collects behavioral and activity logs through these devices. It uses microphones and cameras to acquire emotional data from voice tone and facial expressions. The terminal also transmits the collected information to a server in real time or in batch processing.

[0169] The server is the primary component responsible for receiving information transmitted from terminals and performing data processing and analysis. The server utilizes generative AI models to analyze the data and identify the individual's interests, personality, and emotional state. Based on this analysis, an algorithm operates to propose educational activities tailored to the individual. The server also has the capability to flexibly adjust learning activities to reflect the individual's emotional state.

[0170] As a concrete example, the device continuously records the child's strengths and weaknesses in subjects, as well as recent emotional fluctuations, and sends this information to a server. Based on this information, the server can suggest activities such as listening to music or creating art when the child needs to relax.

[0171] Users can review and select suggested activities notified via their devices. Selected educational activities are conducted through the device, and progress data is collected and used for future feedback and suggestions.

[0172] A concrete example of a prompt is, "Please tell me what activities a 7-year-old child has recently been interested in. Also, based on this week's emotional tendencies, please tell me what learning activities would be most suitable." By inputting this prompt into the AI ​​model, new suggestions will be generated.

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

[0174] Step 1:

[0175] The device collects attribute information about the individual. During this collection phase, sensors from smartphones and wearable devices are used to acquire behavioral and life log data. The input consists of raw data about the individual's daily activities and emotional state. Specifically, the device measures distance traveled using motion sensors and captures conversational tone with a microphone. The output is a dataset summarizing this information.

[0176] Step 2:

[0177] The terminal encrypts the collected data and sends it to the server. Specifically, the terminal performs data cleaning to fill in missing data and prepare it for transmission. The input is the clean data collected in step 1, and the output is the dataset securely transferred to the server.

[0178] Step 3:

[0179] The server feeds the received data into a generating AI model and begins the analysis process. The inputs here are attribute information and emotion data sent from the terminal. Based on the data, the server uses machine learning algorithms to identify the individual's interests, personality, and emotional state. The output is an analysis report of the individual.

[0180] Step 4:

[0181] The server uses an AI model to generate and propose the most suitable educational activities for each individual based on the analysis results. The input is the analysis results obtained in step 3. Here, it calculates situation-dependent activities, such as suggesting challenging learning activities when positive emotions are high. The output is a list of learning activities optimized for the individual.

[0182] Step 5:

[0183] The device receives a list of educational activities provided by the server and notifies the individual and their guardian. Specifically, it displays a pop-up message on the device screen, visually showing activity suggestions based on the history and the reasons for them. The input is the list of educational activities generated in step 4, and the output is the notification to the user and the information presented.

[0184] Step 6:

[0185] The user selects an educational activity based on notifications from their device. In this specific example, if the user chooses reading, the system records this and uses the feedback to inform the next activity selection. The input is the educational activity suggestions from the device, and the output is the user's selection data.

[0186] Step 7:

[0187] The server collects user selection and ongoing progress data to generate feedback. Input includes emotional changes and performance data as selected activities are performed. The server uses this information to refine the feedback and inform future suggestions. The output is a feedback report, which is notified to the user in real time.

[0188] (Application Example 2)

[0189] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0190] In today's educational environment, there is a problem in that learning support is insufficient because learning content and methods tailored to individual children are not provided, and therefore, learning support that is in line with children's growth and emotional state is not adequate. Furthermore, conventional learning support systems are not able to flexibly respond to changes in children's emotions and interests, making it difficult to provide a personalized learning experience.

[0191] 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.

[0192] In this invention, the server includes means for collecting children's characteristic data, means for analyzing emotional data and adjusting learning activities and feedback according to the child's emotional state, and means for visually displaying this information. This makes it possible to provide learning activities optimized for each child and to provide appropriate feedback according to their emotional state.

[0193] "Child characteristics data" refers to information about a child's interests, personality, behavioral patterns, and daily activities.

[0194] "Emotional data" refers to information indicating a child's emotional state, obtained from their facial expressions and voice.

[0195] "Methods of analysis" refers to the process of evaluating children's interests, personalities, and emotional states based on collected data, and then deriving optimal learning activities based on those results.

[0196] A "means for recommending learning activities" refers to a system that, based on analysis results, suggests learning activities that are appropriate for a child's interests and emotional state.

[0197] "Means of notification" refers to methods or devices for providing information obtained based on analysis and recommendations to children and their guardians.

[0198] "Means of adjusting feedback" refer to a system that appropriately changes the feedback on learning activities and progress according to the child's emotional state.

[0199] "Means of visual display" refers to display devices or interfaces that display information in a visual format to make it easier for users to understand.

[0200] This invention is a system that collects and analyzes children's characteristic data and emotional data, and recommends optimal learning activities based on this data. The implementation of the system includes the following steps:

[0201] First, the device collects data on the child's characteristics and emotions. Specifically, it uses a camera and microphone to capture the child's facial expressions and voice, and uses this data to determine their emotional state. Activity data is collected through wearable devices and smartphone applications. This allows for an understanding of the child's behavioral patterns and interests.

[0202] The collected data is analyzed by a server. The server processes the emotional data using Google Cloud's AI services and Amazon Web Services' emotion analysis API. It utilizes Tensorflow (registered trademark) and OpenCV technologies to perform child face recognition and voice analysis.

[0203] Next, the server recommends the most suitable learning activities for the child based on the analysis results. The recommended activities are notified to the user via the device. This notification includes explanations based on the reasons for selecting the activities and the child's emotional state.

[0204] When a user participates in a suggested learning activity, their progress is monitored in real time. The server adjusts feedback based on progress and recommends new activities as needed.

[0205] For example, if a child is feeling stressed, the system will suggest activities such as relaxing music or simple relaxation games. A key feature of this system is that it can adjust these suggestions according to the child's interests and changing emotional state.

[0206] Examples of prompts generated using a generative AI model are as follows:

[0207] "What learning activities are appropriate when a child is experiencing emotional instability?"

[0208] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0209] Step 1:

[0210] The device collects data on the child's characteristics and emotions. It uses a camera and microphone to capture the child's facial expressions and voice, and stores this as initial data. It obtains the child's facial images and voice files as input data.

[0211] Step 2:

[0212] The device sends the collected data to the server. Based on the received face images and audio files, the server performs face recognition using OpenCV, analyzes the audio using Google Cloud's AI services, and extracts emotional data. As output, it generates information about the child's emotional state.

[0213] Step 3:

[0214] The server analyzes children's characteristic and emotional data. Using TensorFlow, it models children's interests and behavioral patterns from this data. All the data collected as input is used to obtain evaluation results of children's interests and personalities as output.

[0215] Step 4:

[0216] The server recommends the most suitable learning activity based on the analysis results. It selects an appropriate learning activity from a list based on the personality and interest assessments obtained from the analysis. The output generates information including the selected learning activity and the reasons for its selection.

[0217] Step 5:

[0218] The device notifies the user of recommended learning activities. It displays activity details and selection reasons on the screen and provides an audio explanation of the activity. It receives activity recommendation information from the server as input and presents it to the user as output.

[0219] Step 6:

[0220] The user will carry out the suggested learning activities. They will start the activities according to the notifications they receive. In particular, if the child is emotionally unstable, they may choose activities that promote relaxation.

[0221] Step 7:

[0222] The server monitors the progress of learning activities and adjusts feedback as needed. It analyzes progress data and evaluates whether the activities are appropriate. It receives real-time activity data as input and generates feedback information as output.

[0223] Step 8:

[0224] The device notifies the user of feedback. It displays the progress of learning activities and feedback on the screen, and provides voice guidance if necessary. It receives feedback information from the server as input.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] [Second Embodiment]

[0229] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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).

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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".

[0241] This invention relates to a system that recommends optimal learning activities based on a child's characteristics. This system collects and analyzes data on the child's personality, interests, and past activities, and then proposes learning activities and provides feedback based on this information. In this process, it also incorporates the opinions of parents and children, enabling proposals that satisfy both parties.

[0242] The device plays a role in recording the child's daily activities, collecting data through questionnaires and activity logs. For example, it gathers information such as what books the child reads and what sports activities they participate in.

[0243] After receiving this data, the server uses a generating AI to analyze it. This analysis identifies the child's areas of interest and personality traits, and then selects learning activities based on these.

[0244] Once the server has completed its selection process, the results will be notified to the user via their terminal. This notification will include a list of recommended learning activities, along with a detailed description of each activity.

[0245] Users can choose activities based on the information presented. Furthermore, parents and children can make decisions together, and their opinions are reflected in future recommendations.

[0246] Once an activity is selected, the server continuously monitors its progress and collects relevant feedback, including reports from teachers and coaches. Based on this feedback, the server suggests improvements as needed and recommends new activities.

[0247] All progress data is updated in real time and displayed visually through the device. This dashboard allows users to easily understand their child's growth and learning progress. In this way, the present invention is a system that provides parents and children with all the information and support they need to make better choices.

[0248] The following describes the processing flow.

[0249] Step 1:

[0250] The device collects records of the child's daily activities. This includes data such as activity type, frequency, and duration, using mobile apps or wearable devices. For example, exercise, reading, and digital media use are recorded.

[0251] Step 2:

[0252] The server receives data sent from the terminal and performs data cleaning. This removes invalid data, standardizes the format, and converts the data into a state suitable for analysis.

[0253] Step 3:

[0254] The server uses generative AI to analyze personality and interests based on the organized data. Here, natural language processing and image recognition technologies are used to identify features and patterns extracted from the data.

[0255] Step 4:

[0256] The server uses the analysis results as input to run a recommendation algorithm and select learning activities suitable for the child. Past success stories and data from other children with similar profiles are also taken into consideration.

[0257] Step 5:

[0258] The device notifies the child and their guardian of a list of recommended learning activities. This notification includes detailed information about each activity and the reasons for its selection.

[0259] Step 6:

[0260] Users select learning activities through their devices. This selection process can be done jointly by parents and children, and the results are reflected in recommendations for future activities.

[0261] Step 7:

[0262] The server collects feedback from devices, teachers, and coaches to monitor the progress of selected learning activities. This allows for an understanding of the level of achievement and satisfaction with the activities.

[0263] Step 8:

[0264] The server generates a progress report based on the feedback and presents it to the user. The report includes the current progress, areas for improvement, and advice on the next steps.

[0265] Step 9:

[0266] The device updates a real-time dashboard, allowing users to visually monitor their child's growth and learning progress. It displays data intuitively using graphs and charts.

[0267] (Example 1)

[0268] 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."

[0269] In recent years, education based on children's individuality has become increasingly important, but discovering and recommending the most suitable learning activities for each child is not easy. In particular, achieving education that matches a child's interests and personality requires individual data analysis, and the technology to perform this efficiently is needed. Furthermore, it is desirable to consistently provide activity recommendations and feedback based on the analysis results, but achieving this presents the challenge of building a complex system.

[0270] 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.

[0271] In this invention, the server includes a device for collecting children's attribute data, a device for processing the acquired data and analyzing the children's interests and personalities, and a device for selecting appropriate learning activities based on the analysis results. This makes it possible to select learning activities tailored to each individual child.

[0272] "Child attribute data" refers to information used to provide individualized education, such as a child's interests, personality, and past activity records.

[0273] "Analysis" refers to the process of identifying children's interests and personality traits based on collected data.

[0274] "Learning activities" refer to specific actions or programs proposed to children for educational or growth-promoting purposes.

[0275] A "server" refers to a computer system that processes, stores, analyzes, and communicates data.

[0276] "Device" refers to a general term for hardware or software designed to perform a specific function.

[0277] A "generative AI model" refers to an artificial intelligence algorithm designed for data analysis or generating specific outputs.

[0278] A "prompt statement" refers to an input statement used to control the output of a generative AI model.

[0279] "Notification" refers to the act of communicating analysis results and recommended activities to the user.

[0280] "Evaluation" refers to feedback regarding the progress and results of activities, and it is the basis for determining the next steps.

[0281] This invention relates to a system for optimizing educational activities based on a child's characteristics. Specifically, it includes a process of analyzing a child's interests, personality, and past activity records, and using this information to select appropriate learning activities.

[0282] The device is the primary device used to collect children's attribute data. This device typically consists of a smartphone or tablet, which records daily activity logs and collects data in the form of questionnaires. For example, it might record information such as what books a child reads or what sports they participate in.

[0283] The collected data is sent to the server. This server has functions for processing and analyzing the data, and uses a generative AI model to identify the fields and personality traits that children are interested in. Analysis tools such as Python and R are used for data analysis. Thereby, it becomes possible to select the most suitable learning activities for each child.

[0284] The selected learning activities are notified to the user through the terminal. The user can use this information to choose the best activities with their children. Also, the selected activities are continuously monitored by the server, and feedback according to the progress is provided. This includes reports from educators and instructors.

[0285] Since these information are visually displayed, the user can easily grasp the growth and learning progress of the child. For example, as an example of a prompt sentence, there is an instruction such as "From the recent activities, identify the most suitable learning activities for this child".

[0286] In this way, this invention is an embodiment for customizing the educational experience for individual children and realizing the selection and adjustment of optimal learning activities.

[0287] The flow of the specific process in Example 1 will be described using FIG. 11.

[0288] Step 1:

[0289] The terminal collects attribute data from the daily activities of the child. Specifically, it gets information by having the child answer questions in the form of a questionnaire or through an application that records activity logs. The input data is information such as the child's reading habits and participated activities. These data are organized for subsequent analysis.

[0290] Step 2:

[0291] The terminal encrypts the collected attribute data and sends it to the server. SSL / TLS protocols are used to ensure secure data transfer. The input is encrypted attribute data, and the output is the data arriving at the server.

[0292] Step 3:

[0293] The server saves the received data to a database and begins the analysis. A generative AI model processes the data using a Python analysis library. For example, it might use the prompt "Identify the most suitable learning activities for this child based on their recent activities" to select appropriate learning activities based on the child's interests and personality. The input to the analysis is the saved attribute data, and the output is a list of learning activities selected based on the child's characteristics.

[0294] Step 4:

[0295] The server sends the analysis results to the terminal. The terminal displays the suggested learning activities in a user-friendly interface. The input is a list of selected learning activities, and the output is a visually designed user interface.

[0296] Step 5:

[0297] Users select learning activities based on the information presented. This selection can be made jointly by parents and children, and feedback is collected during this process. The input consists of the user's selections and opinions, while the output is feedback that helps improve the accuracy of future analyses.

[0298] Step 6:

[0299] The server receives reports from educators and instructors to monitor the progress of selected activities. This allows for the suggestion of new ideas and improvements as needed. The input is the activity progress report, and the output is improvement suggestions.

[0300] Step 7:

[0301] The terminal visually displays all progress data and feedback in real time. It is a mechanism that makes it easier for users to grasp the growth of children. The input is the latest progress data and feedback, and the output is a visualized dashboard.

[0302] (Application Example 1)

[0303] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0304] Currently, there is a problem that optimal proposals for learning activities based on the individual characteristics of children are not sufficiently made. In addition, there is a lack of means for parents and educators to efficiently grasp the progress of children's learning activities and effectively utilize feedback. For this reason, a method of learning support that maximally draws out children's interests and growth is required.

[0305] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0306] In this invention, the server includes means for collecting children's characteristic data, means for processing the collected data and analyzing children's interests and personalities, and means for interacting with children via household machines and notifying the proposed learning activities by voice or display. Thereby, it becomes possible to draw out children's interests and propose individualized learning activities.

[0307] "Children's characteristic data" is information regarding children's personalities, interests, and past activities.

[0308] "Means for collecting" is a method of acquiring data through daily activities and questionnaires.

[0309] "Means for analyzing" is a process for specifying children's interests and personalities based on the collected data.

[0310] "Optimal learning activities" are those selected based on a child's characteristics and data, designed to maximize the child's learning effectiveness.

[0311] "Means of notification" refers to methods of communicating the results of recommended learning activities to parents and children.

[0312] "Monitoring methods" refer to methods for continuously tracking and recording the progress of learning activities.

[0313] "Means of providing feedback" refers to the process of providing evaluations and suggestions for improvement regarding the progress of learning activities.

[0314] A "means of visual display" refers to an interface for presenting information visually.

[0315] "Household machines" refer to robots and electronic devices used within the home.

[0316] "Generative AI technology" is an artificial intelligence technology that automatically generates insights from data with minimal human intervention.

[0317] 1. Generate a program for the system that implements this application example.

[0318] This invention provides a system that supports learning based on a child's characteristics. A server collects data on the child's characteristics and analyzes it using a generative AI model. Through this analysis, the child's interests and personality are identified, and the optimal learning activities are selected. The selection results are communicated to the user via voice and display through a home device.

[0319] The server utilizes speech recognition technology to transcribe collected audio and activity data of children into text. Cloud computing feeds this data into a generative AI model, which generates suggestions for learning activities tailored to the child's interests. These suggestions are displayed and notified via in-home devices in a way that is easily understood visually and audibly.

[0320] For example, the server analyzes collected data to determine that a child's preference is "insect observation," and recommends insect-related books and observation activities. This information is then communicated via voice from a device in the home, such as, "Let's borrow some books from the library for insect observation." An example of a prompt message could be, "Please recommend new learning activities for a child who is interested in insects."

[0321] This system allows parents and children to receive information tailored to the child's interests and characteristics, enabling them to select learning activities more appropriately.

[0322] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0323] Step 1:

[0324] The server collects data on the child's characteristics from the device. The device obtains daily activity data and survey information and sends it to the server. The input consists of activity logs and data related to interests, which the server receives and stores.

[0325] Step 2:

[0326] The server performs analysis using a generative AI model based on the collected data. The input is the characteristic data received in step 1. The server inputs this data into the AI ​​model and processes it to estimate the child's interests and personality. The output is the child's characteristic assessment information based on the analysis.

[0327] Step 3:

[0328] The server selects the optimal learning activity based on the generated characteristic assessment information. Here, estimated interests and personality traits are used as input, and suitable learning activities are searched and extracted from the database. The output is a list of recommended learning activities.

[0329] Step 4:

[0330] The server notifies the user of selected learning activities via a home device. The input is a list of recommended learning activities, which is converted into audio and visual information and sent to the hardware. The user receives the notification through the device's voice function and display.

[0331] Step 5:

[0332] The user selects a learning activity based on notifications from the server. Input in this step is a notification from the device, and the selected activity is returned to the server. The server records this information and uses it to recommend future activities.

[0333] Step 6:

[0334] The server continuously monitors the progress of selected learning activities and collects feedback. Input is progress data obtained during the activity, and output is evaluation and feedback on areas for improvement based on this data. This allows the server to accumulate data for suggesting new activities.

[0335] 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.

[0336] This invention relates to a system that recommends optimal learning activities based on a child's characteristics and recognizes the user's emotions to provide feedback. By incorporating an emotion engine, this system provides an appropriate learning environment according to the child's emotional state and enables emotion-based feedback and adjustment of activities.

[0337] The device plays a role in collecting data on the child's daily activities and emotions. Activity data is obtained through mobile apps and wearable devices and includes the child's behavior, preferences, and activity logs. Emotional data, on the other hand, is collected by recording voice tone and facial expressions.

[0338] The server receives data sent from the terminal and performs data cleaning and sentiment analysis. A sentiment engine assists in this process, identifying the user's emotional state from voice and facial expressions. The analysis results consider emotional trends in addition to the child's personality and interests.

[0339] The server then recommends learning activities suitable for the child based on the collected and analyzed data. These recommendations are tailored to the user's emotions; for example, if the user is experiencing high stress levels, it can suggest activities that promote relaxation.

[0340] The device notifies children and their guardians of the results of recommended learning activities. The information provided includes details of the activities and the reasons for their selection, based on the user's sentiment.

[0341] When a user selects an activity based on the information presented, the server monitors the progress based on that selection and collects necessary feedback. This includes adjusting the feedback in response to changes in the user's emotions.

[0342] Progress information and feedback are updated in real time via the device and displayed visually. This allows users to understand their child's growth and learning progress along with their emotional responses. In this way, the present invention is a system that not only provides an optimal learning environment for children but also leverages insights gained from emotions to realize a more personalized educational experience.

[0343] The following describes the processing flow.

[0344] Step 1:

[0345] The device collects data on the child's daily activities and emotions. Activity data is obtained through a mobile app or wearable device, while emotional data is acquired by recording voice tone and facial expressions in video.

[0346] Step 2:

[0347] The server receives data sent from the terminal. First, it performs data cleaning to remove and correct incomplete or incorrect data.

[0348] Step 3:

[0349] The server uses an emotion engine to analyze voice and facial expression data to identify the user's emotional state. For example, voice analysis quantifies the degree of emotion, while facial expression analysis evaluates subtle facial movements.

[0350] Step 4:

[0351] The server combines analyzed personal characteristics and emotional data, and applies a recommendation algorithm to determine learning activities suitable for the child. Emotional data acts as a modifier in activity selection, suggesting activities that are optimal for the child's mental state.

[0352] Step 5:

[0353] The device notifies the child and parent of a list of selected learning activities. The notification includes details of each activity and an explanation of how they correspond to emotional data.

[0354] Step 6:

[0355] The user selects the activity they most agree with based on the information provided. In this process, parents and children can jointly make the optimal choice.

[0356] Step 7:

[0357] The server monitors the progress of the selected activities and collects relevant feedback. It also monitors user sentiment and adjusts the feedback in real time.

[0358] Step 8:

[0359] The server analyzes the collected feedback and sentiment data and generates new suggestions and advice as needed.

[0360] Step 9:

[0361] The device updates progress and feedback information on a real-time dashboard, visually displaying it to allow users to intuitively understand their child's learning and emotional changes.

[0362] (Example 2)

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

[0364] In recent years, the importance of individual optimization and personalization in education has increased, but accurately understanding individual characteristics and emotional states and proposing appropriate educational activities remains a challenging task. In particular, technologies for analyzing individuals' emotional states in real time and providing educational activities accordingly are underdeveloped, and there is a need to improve the quality of education through such technologies. Furthermore, it is also important to continuously adjust educational activities through monitoring and feedback. This invention aims to solve these problems.

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

[0366] In this invention, the server includes means for acquiring individual attribute information, means for processing the acquired information and analyzing the individual's interests and personality, and means for performing emotion analysis and identifying the individual's emotional state. This makes it possible to adaptively provide educational activities to the individual based on their characteristics and emotional state. Furthermore, by providing emotionally appropriate feedback in real time along with suggesting educational activities, a personalized educational experience is realized.

[0367] "Individual" refers to the person receiving education or the user, who is the primary user of this invention.

[0368] "Attribute information" refers to all relevant information about an individual, including characteristic data, behavioral history, interests, and personality information.

[0369] "Means of analysis" refers to technical means used to identify and evaluate an individual's interests, personality, and emotional state based on collected data, and to utilize this information for educational activities.

[0370] "Educational activities" refer to a series of learning and training behaviors or programs proposed based on an individual's interests and emotional state.

[0371] "Means of notification" refers to technical means of informing individuals or supervisors of the proposed results of educational activities, and includes visual or auditory methods.

[0372] "Monitoring methods" refer to a series of processes for continuously observing and tracking the progress of an individual's activities and providing appropriate feedback.

[0373] "Means of visual presentation" refers to methods of visually displaying individual progress information and feedback, and includes technologies that provide information via displays.

[0374] "Emotional analysis" refers to a technology that identifies an individual's emotional state from their voice, facial expressions, etc., and this information is used to adjust educational activities.

[0375] This invention comprises a system that provides optimal educational activities based on individual characteristics and emotional states. The system mainly consists of a "terminal" and a "server," and the "user" uses this system to engage in learning activities. A specific embodiment is shown below.

[0376] The terminal is responsible for collecting individual attribute information. The terminal is configured as a smartphone or wearable device, and collects behavioral and activity logs through these devices. It uses microphones and cameras to acquire emotional data from voice tone and facial expressions. The terminal also transmits the collected information to a server in real time or in batch processing.

[0377] The server is the primary component responsible for receiving information transmitted from terminals and performing data processing and analysis. The server utilizes generative AI models to analyze the data and identify the individual's interests, personality, and emotional state. Based on this analysis, an algorithm operates to propose educational activities tailored to the individual. The server also has the capability to flexibly adjust learning activities to reflect the individual's emotional state.

[0378] As a concrete example, the device continuously records the child's strengths and weaknesses in subjects, as well as recent emotional fluctuations, and sends this information to a server. Based on this information, the server can suggest activities such as listening to music or creating art when the child needs to relax.

[0379] Users can review and select suggested activities notified via their devices. Selected educational activities are conducted through the device, and progress data is collected and used for future feedback and suggestions.

[0380] A concrete example of a prompt is, "Please tell me what activities a 7-year-old child has recently been interested in. Also, based on this week's emotional tendencies, please tell me what learning activities would be most suitable." By inputting this prompt into the AI ​​model, new suggestions will be generated.

[0381] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0382] Step 1:

[0383] The device collects attribute information about the individual. During this collection phase, sensors from smartphones and wearable devices are used to acquire behavioral and life log data. The input consists of raw data about the individual's daily activities and emotional state. Specifically, the device measures distance traveled using motion sensors and captures conversational tone with a microphone. The output is a dataset summarizing this information.

[0384] Step 2:

[0385] The terminal encrypts the collected data and sends it to the server. Specifically, the terminal performs data cleaning to fill in missing data and prepare it for transmission. The input is the clean data collected in step 1, and the output is the dataset securely transferred to the server.

[0386] Step 3:

[0387] The server feeds the received data into a generating AI model and begins the analysis process. The inputs here are attribute information and emotion data sent from the terminal. Based on the data, the server uses machine learning algorithms to identify the individual's interests, personality, and emotional state. The output is an analysis report of the individual.

[0388] Step 4:

[0389] The server uses an AI model to generate and propose the most suitable educational activities for each individual based on the analysis results. The input is the analysis results obtained in step 3. Here, it calculates situation-dependent activities, such as suggesting challenging learning activities when positive emotions are high. The output is a list of learning activities optimized for the individual.

[0390] Step 5:

[0391] The device receives a list of educational activities provided by the server and notifies the individual and their guardian. Specifically, it displays a pop-up message on the device screen, visually showing activity suggestions based on the history and the reasons for them. The input is the list of educational activities generated in step 4, and the output is the notification to the user and the information presented.

[0392] Step 6:

[0393] The user selects an educational activity based on notifications from their device. In this specific example, if the user chooses reading, the system records this and uses the feedback to inform the next activity selection. The input is the educational activity suggestions from the device, and the output is the user's selection data.

[0394] Step 7:

[0395] The server collects user selection and ongoing progress data to generate feedback. Input includes emotional changes and performance data as selected activities are performed. The server uses this information to refine the feedback and inform future suggestions. The output is a feedback report, which is notified to the user in real time.

[0396] (Application Example 2)

[0397] 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."

[0398] In today's educational environment, there is a problem in that learning support is insufficient because learning content and methods tailored to individual children are not provided, and therefore, learning support that is in line with children's growth and emotional state is not adequate. Furthermore, conventional learning support systems are not able to flexibly respond to changes in children's emotions and interests, making it difficult to provide a personalized learning experience.

[0399] 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.

[0400] In this invention, the server includes means for collecting children's characteristic data, means for analyzing emotional data and adjusting learning activities and feedback according to the child's emotional state, and means for visually displaying this information. This makes it possible to provide learning activities optimized for each child and to provide appropriate feedback according to their emotional state.

[0401] "Child characteristics data" refers to information about a child's interests, personality, behavioral patterns, and daily activities.

[0402] "Emotional data" refers to information indicating a child's emotional state, obtained from their facial expressions and voice.

[0403] "Methods of analysis" refers to the process of evaluating children's interests, personalities, and emotional states based on collected data, and then deriving optimal learning activities based on those results.

[0404] A "means for recommending learning activities" refers to a system that, based on analysis results, suggests learning activities that are appropriate for a child's interests and emotional state.

[0405] "Means of notification" refers to methods or devices for providing information obtained based on analysis and recommendations to children and their guardians.

[0406] "Means of adjusting feedback" refer to a system that appropriately changes the feedback on learning activities and progress according to the child's emotional state.

[0407] "Means of visual display" refers to display devices or interfaces that display information in a visual format to make it easier for users to understand.

[0408] This invention is a system that collects and analyzes children's characteristic data and emotional data, and recommends optimal learning activities based on this data. The implementation of the system includes the following steps:

[0409] First, the device collects data on the child's characteristics and emotions. Specifically, it uses a camera and microphone to capture the child's facial expressions and voice, and uses this data to determine their emotional state. Activity data is collected through wearable devices and smartphone applications. This allows for an understanding of the child's behavioral patterns and interests.

[0410] The collected data is analyzed by a server. The server processes the emotional data using Google Cloud's AI services and Amazon Web Services' emotion analysis API. TensorFlow and OpenCV technologies are used for child face recognition and voice analysis.

[0411] Next, the server recommends the most suitable learning activities for the child based on the analysis results. The recommended activities are notified to the user via the device. This notification includes explanations based on the reasons for selecting the activities and the child's emotional state.

[0412] When a user participates in a suggested learning activity, their progress is monitored in real time. The server adjusts feedback based on progress and recommends new activities as needed.

[0413] For example, if a child is feeling stressed, the system will suggest activities such as relaxing music or simple relaxation games. A key feature of this system is that it can adjust these suggestions according to the child's interests and changing emotional state.

[0414] Examples of prompts generated using a generative AI model are as follows:

[0415] "What learning activities are appropriate when a child is experiencing emotional instability?"

[0416] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0417] Step 1:

[0418] The device collects data on the child's characteristics and emotions. It uses a camera and microphone to capture the child's facial expressions and voice, and stores this as initial data. It obtains the child's facial images and voice files as input data.

[0419] Step 2:

[0420] The device sends the collected data to the server. Based on the received face images and audio files, the server performs face recognition using OpenCV, analyzes the audio using Google Cloud's AI services, and extracts emotional data. As output, it generates information about the child's emotional state.

[0421] Step 3:

[0422] The server analyzes children's characteristic and emotional data. Using TensorFlow, it models children's interests and behavioral patterns from this data. All the data collected as input is used to obtain evaluation results of children's interests and personalities as output.

[0423] Step 4:

[0424] The server recommends the most suitable learning activity based on the analysis results. It selects an appropriate learning activity from a list based on the personality and interest assessments obtained from the analysis. The output generates information including the selected learning activity and the reasons for its selection.

[0425] Step 5:

[0426] The device notifies the user of recommended learning activities. It displays activity details and selection reasons on the screen and provides an audio explanation of the activity. It receives activity recommendation information from the server as input and presents it to the user as output.

[0427] Step 6:

[0428] The user will carry out the suggested learning activities. They will start the activities according to the notifications they receive. In particular, if the child is emotionally unstable, they may choose activities that promote relaxation.

[0429] Step 7:

[0430] The server monitors the progress of learning activities and adjusts feedback as needed. It analyzes progress data and evaluates whether the activities are appropriate. It receives real-time activity data as input and generates feedback information as output.

[0431] Step 8:

[0432] The device notifies the user of feedback. It displays the progress of learning activities and feedback on the screen, and provides voice guidance if necessary. It receives feedback information from the server as input.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] [Third Embodiment]

[0437] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0438] 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.

[0439] 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).

[0440] 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.

[0441] 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.

[0442] 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).

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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".

[0449] This invention relates to a system that recommends optimal learning activities based on a child's characteristics. This system collects and analyzes data on the child's personality, interests, and past activities, and then proposes learning activities and provides feedback based on this information. In this process, it also incorporates the opinions of parents and children, enabling proposals that satisfy both parties.

[0450] The device plays a role in recording the child's daily activities, collecting data through questionnaires and activity logs. For example, it gathers information such as what books the child reads and what sports activities they participate in.

[0451] After receiving this data, the server uses a generating AI to analyze it. This analysis identifies the child's areas of interest and personality traits, and then selects learning activities based on these.

[0452] Once the server has completed its selection process, the results will be notified to the user via their terminal. This notification will include a list of recommended learning activities, along with a detailed description of each activity.

[0453] Users can choose activities based on the information presented. Furthermore, parents and children can make decisions together, and their opinions are reflected in future recommendations.

[0454] Once an activity is selected, the server continuously monitors its progress and collects relevant feedback, including reports from teachers and coaches. Based on this feedback, the server suggests improvements as needed and recommends new activities.

[0455] All progress data is updated in real time and displayed visually through the device. This dashboard allows users to easily understand their child's growth and learning progress. In this way, the present invention is a system that provides parents and children with all the information and support they need to make better choices.

[0456] The following describes the processing flow.

[0457] Step 1:

[0458] The device collects records of the child's daily activities. This includes data such as activity type, frequency, and duration, using mobile apps or wearable devices. For example, exercise, reading, and digital media use are recorded.

[0459] Step 2:

[0460] The server receives data sent from the terminal and performs data cleaning. This removes invalid data, standardizes the format, and converts the data into a state suitable for analysis.

[0461] Step 3:

[0462] The server uses generative AI to analyze personality and interests based on the organized data. Here, natural language processing and image recognition technologies are used to identify features and patterns extracted from the data.

[0463] Step 4:

[0464] The server uses the analysis results as input to run a recommendation algorithm and select learning activities suitable for the child. Past success stories and data from other children with similar profiles are also taken into consideration.

[0465] Step 5:

[0466] The device notifies the child and their guardian of a list of recommended learning activities. This notification includes detailed information about each activity and the reasons for its selection.

[0467] Step 6:

[0468] Users select learning activities through their devices. This selection process can be done jointly by parents and children, and the results are reflected in recommendations for future activities.

[0469] Step 7:

[0470] The server collects feedback from devices, teachers, and coaches to monitor the progress of selected learning activities. This allows for an understanding of the level of achievement and satisfaction with the activities.

[0471] Step 8:

[0472] The server generates a progress report based on the feedback and presents it to the user. The report includes the current progress, areas for improvement, and advice on the next steps.

[0473] Step 9:

[0474] The device updates a real-time dashboard, allowing users to visually monitor their child's growth and learning progress. It displays data intuitively using graphs and charts.

[0475] (Example 1)

[0476] 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."

[0477] In recent years, education based on children's individuality has become increasingly important, but discovering and recommending the most suitable learning activities for each child is not easy. In particular, achieving education that matches a child's interests and personality requires individual data analysis, and the technology to perform this efficiently is needed. Furthermore, it is desirable to consistently provide activity recommendations and feedback based on the analysis results, but achieving this presents the challenge of building a complex system.

[0478] 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.

[0479] In this invention, the server includes a device for collecting children's attribute data, a device for processing the acquired data and analyzing the children's interests and personalities, and a device for selecting appropriate learning activities based on the analysis results. This makes it possible to select learning activities tailored to each individual child.

[0480] "Child attribute data" refers to information used to provide individualized education, such as a child's interests, personality, and past activity records.

[0481] "Analysis" refers to the process of identifying children's interests and personality traits based on collected data.

[0482] "Learning activities" refer to specific actions or programs proposed to children for educational or growth-promoting purposes.

[0483] A "server" refers to a computer system that processes, stores, analyzes, and communicates data.

[0484] "Device" refers to a general term for hardware or software designed to perform a specific function.

[0485] A "generative AI model" refers to an artificial intelligence algorithm designed for data analysis or generating specific outputs.

[0486] A "prompt statement" refers to an input statement used to control the output of a generative AI model.

[0487] "Notification" refers to the act of communicating analysis results and recommended activities to the user.

[0488] "Evaluation" refers to feedback regarding the progress and results of activities, and it is the basis for determining the next steps.

[0489] This invention relates to a system for optimizing educational activities based on a child's characteristics. Specifically, it includes a process of analyzing a child's interests, personality, and past activity records, and using this information to select appropriate learning activities.

[0490] The device is the primary device used to collect children's attribute data. This device typically consists of a smartphone or tablet, which records daily activity logs and collects data in the form of questionnaires. For example, it might record information such as what books a child reads or what sports they participate in.

[0491] The collected data is sent to a server. This server is equipped with functions to process and analyze the data, and uses generative AI models to identify areas of interest and personality traits of children. Analysis tools such as Python and R are used for data analysis. This makes it possible to select the most suitable learning activities for each child.

[0492] Selected learning activities are notified to the user via their device. This information allows parents and children to choose the best activities together. Furthermore, the selected activities are continuously monitored by a server, and feedback is provided based on progress. This includes reports from educators and instructors.

[0493] This information is displayed visually, making it easy for users to understand their child's growth and learning progress. For example, one example of a prompt is, "Based on recent activities, identify the most suitable learning activities for this child."

[0494] In this way, this invention is an embodiment for customizing the educational experience for individual children and enabling the selection and adjustment of optimal learning activities.

[0495] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0496] Step 1:

[0497] The device collects attribute data from children's daily activities. Specifically, it obtains information through questionnaires and activity log applications. The data entered includes information such as children's reading habits and the activities they participate in. This data is then organized for subsequent analysis.

[0498] Step 2:

[0499] The terminal encrypts the collected attribute data and sends it to the server. SSL / TLS protocols are used to ensure secure data transfer. The input is encrypted attribute data, and the output is the data arriving at the server.

[0500] Step 3:

[0501] The server saves the received data to a database and begins the analysis. A generative AI model processes the data using a Python analysis library. For example, it might use the prompt "Identify the most suitable learning activities for this child based on their recent activities" to select appropriate learning activities based on the child's interests and personality. The input to the analysis is the saved attribute data, and the output is a list of learning activities selected based on the child's characteristics.

[0502] Step 4:

[0503] The server sends the analysis results to the terminal. The terminal displays the suggested learning activities in a user-friendly interface. The input is a list of selected learning activities, and the output is a visually designed user interface.

[0504] Step 5:

[0505] Users select learning activities based on the information presented. This selection can be made jointly by parents and children, and feedback is collected during this process. The input consists of the user's selections and opinions, while the output is feedback that helps improve the accuracy of future analyses.

[0506] Step 6:

[0507] The server receives reports from educators and instructors to monitor the progress of selected activities. This allows for the suggestion of new ideas and improvements as needed. The input is the activity progress report, and the output is improvement suggestions.

[0508] Step 7:

[0509] The device visually displays all progress data and feedback in real time, making it easy for users to track their child's progress. Input is the latest progress data and feedback, and output is a visualized dashboard.

[0510] (Application Example 1)

[0511] 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."

[0512] Currently, there is a problem in that optimal learning activities tailored to each child's individual characteristics are not being adequately proposed. Furthermore, there is a lack of means for parents and educators to efficiently grasp the progress of children's learning activities and effectively utilize feedback. Therefore, there is a need for learning support methods that maximize children's interests and growth.

[0513] 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.

[0514] In this invention, the server includes means for collecting data on a child's characteristics, means for processing the collected data and analyzing the child's interests and personality, and means for interacting with the child through a home-use device and informing them of suggested learning activities through voice and display. This makes it possible to stimulate the child's interests and suggest personalized learning activities.

[0515] "Child characteristics data" refers to information about a child's personality, interests, and past activities.

[0516] "Means of collection" refers to methods of obtaining data through everyday activities or surveys.

[0517] "Methods of analysis" refer to the process of identifying a child's interests and personality based on the collected data.

[0518] "Optimal learning activities" are those selected based on a child's characteristics and data, designed to maximize the child's learning effectiveness.

[0519] "Means of notification" refers to methods of communicating the results of recommended learning activities to parents and children.

[0520] "Monitoring methods" refer to methods for continuously tracking and recording the progress of learning activities.

[0521] "Means of providing feedback" refers to the process of providing evaluations and suggestions for improvement regarding the progress of learning activities.

[0522] A "means of visual display" refers to an interface for presenting information visually.

[0523] "Household machines" refer to robots and electronic devices used within the home.

[0524] "Generative AI technology" is an artificial intelligence technology that automatically generates insights from data with minimal human intervention.

[0525] 1. Generate a program for the system that implements this application example.

[0526] This invention provides a system that supports learning based on a child's characteristics. A server collects data on the child's characteristics and analyzes it using a generative AI model. Through this analysis, the child's interests and personality are identified, and the optimal learning activities are selected. The selection results are communicated to the user via voice and display through a home device.

[0527] The server utilizes speech recognition technology to transcribe collected audio and activity data of children into text. Cloud computing feeds this data into a generative AI model, which generates suggestions for learning activities tailored to the child's interests. These suggestions are displayed and notified via in-home devices in a way that is easily understood visually and audibly.

[0528] For example, the server analyzes collected data to determine that a child's preference is "insect observation," and recommends insect-related books and observation activities. This information is then communicated via voice from a device in the home, such as, "Let's borrow some books from the library for insect observation." An example of a prompt message could be, "Please recommend new learning activities for a child who is interested in insects."

[0529] This system allows parents and children to receive information tailored to the child's interests and characteristics, enabling them to select learning activities more appropriately.

[0530] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0531] Step 1:

[0532] The server collects data on the child's characteristics from the device. The device obtains daily activity data and survey information and sends it to the server. The input consists of activity logs and data related to interests, which the server receives and stores.

[0533] Step 2:

[0534] The server performs analysis using a generative AI model based on the collected data. The input is the characteristic data received in step 1. The server inputs this data into the AI ​​model and processes it to estimate the child's interests and personality. The output is the child's characteristic assessment information based on the analysis.

[0535] Step 3:

[0536] The server selects the optimal learning activity based on the generated characteristic assessment information. Here, estimated interests and personality traits are used as input, and suitable learning activities are searched and extracted from the database. The output is a list of recommended learning activities.

[0537] Step 4:

[0538] The server notifies the user of selected learning activities via a home device. The input is a list of recommended learning activities, which is converted into audio and visual information and sent to the hardware. The user receives the notification through the device's voice function and display.

[0539] Step 5:

[0540] The user selects a learning activity based on notifications from the server. Input in this step is a notification from the device, and the selected activity is returned to the server. The server records this information and uses it to recommend future activities.

[0541] Step 6:

[0542] The server continuously monitors the progress of selected learning activities and collects feedback. Input is progress data obtained during the activity, and output is evaluation and feedback on areas for improvement based on this data. This allows the server to accumulate data for suggesting new activities.

[0543] 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.

[0544] This invention relates to a system that recommends optimal learning activities based on a child's characteristics and recognizes the user's emotions to provide feedback. By incorporating an emotion engine, this system provides an appropriate learning environment according to the child's emotional state and enables emotion-based feedback and adjustment of activities.

[0545] The device plays a role in collecting data on the child's daily activities and emotions. Activity data is obtained through mobile apps and wearable devices and includes the child's behavior, preferences, and activity logs. Emotional data, on the other hand, is collected by recording voice tone and facial expressions.

[0546] The server receives data sent from the terminal and performs data cleaning and sentiment analysis. A sentiment engine assists in this process, identifying the user's emotional state from voice and facial expressions. The analysis results consider emotional trends in addition to the child's personality and interests.

[0547] The server then recommends learning activities suitable for the child based on the collected and analyzed data. These recommendations are tailored to the user's emotions; for example, if the user is experiencing high stress levels, it can suggest activities that promote relaxation.

[0548] The device notifies children and their guardians of the results of recommended learning activities. The information provided includes details of the activities and the reasons for their selection, based on the user's sentiment.

[0549] When a user selects an activity based on the information presented, the server monitors the progress based on that selection and collects necessary feedback. This includes adjusting the feedback in response to changes in the user's emotions.

[0550] Progress information and feedback are updated in real time via the device and displayed visually. This allows users to understand their child's growth and learning progress along with their emotional responses. In this way, the present invention is a system that not only provides an optimal learning environment for children but also leverages insights gained from emotions to realize a more personalized educational experience.

[0551] The following describes the processing flow.

[0552] Step 1:

[0553] The device collects data on the child's daily activities and emotions. Activity data is obtained through a mobile app or wearable device, while emotional data is acquired by recording voice tone and facial expressions in video.

[0554] Step 2:

[0555] The server receives data sent from the terminal. First, it performs data cleaning to remove and correct incomplete or incorrect data.

[0556] Step 3:

[0557] The server uses an emotion engine to analyze voice and facial expression data to identify the user's emotional state. For example, voice analysis quantifies the degree of emotion, while facial expression analysis evaluates subtle facial movements.

[0558] Step 4:

[0559] The server combines analyzed personal characteristics and emotional data, and applies a recommendation algorithm to determine learning activities suitable for the child. Emotional data acts as a modifier in activity selection, suggesting activities that are optimal for the child's mental state.

[0560] Step 5:

[0561] The device notifies the child and parent of a list of selected learning activities. The notification includes details of each activity and an explanation of how they correspond to emotional data.

[0562] Step 6:

[0563] The user selects the activity they most agree with based on the information provided. In this process, parents and children can jointly make the optimal choice.

[0564] Step 7:

[0565] The server monitors the progress of the selected activities and collects relevant feedback. It also monitors user sentiment and adjusts the feedback in real time.

[0566] Step 8:

[0567] The server analyzes the collected feedback and sentiment data and generates new suggestions and advice as needed.

[0568] Step 9:

[0569] The device updates progress and feedback information on a real-time dashboard, visually displaying it to allow users to intuitively understand their child's learning and emotional changes.

[0570] (Example 2)

[0571] 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."

[0572] In recent years, the importance of individual optimization and personalization in education has increased, but accurately understanding individual characteristics and emotional states and proposing appropriate educational activities remains a challenging task. In particular, technologies for analyzing individuals' emotional states in real time and providing educational activities accordingly are underdeveloped, and there is a need to improve the quality of education through such technologies. Furthermore, it is also important to continuously adjust educational activities through monitoring and feedback. This invention aims to solve these problems.

[0573] 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.

[0574] In this invention, the server includes means for acquiring individual attribute information, means for processing the acquired information and analyzing the individual's interests and personality, and means for performing emotion analysis and identifying the individual's emotional state. This makes it possible to adaptively provide educational activities to the individual based on their characteristics and emotional state. Furthermore, by providing emotionally appropriate feedback in real time along with suggesting educational activities, a personalized educational experience is realized.

[0575] "Individual" refers to the person receiving education or the user, who is the primary user of this invention.

[0576] "Attribute information" refers to all relevant information about an individual, including characteristic data, behavioral history, interests, and personality information.

[0577] "Means of analysis" refers to technical means used to identify and evaluate an individual's interests, personality, and emotional state based on collected data, and to utilize this information for educational activities.

[0578] "Educational activities" refer to a series of learning and training behaviors or programs proposed based on an individual's interests and emotional state.

[0579] "Means of notification" refers to technical means of informing individuals or supervisors of the proposed results of educational activities, and includes visual or auditory methods.

[0580] "Monitoring methods" refer to a series of processes for continuously observing and tracking the progress of an individual's activities and providing appropriate feedback.

[0581] "Means of visual presentation" refers to methods of visually displaying individual progress information and feedback, and includes technologies that provide information via displays.

[0582] "Emotional analysis" refers to a technology that identifies an individual's emotional state from their voice, facial expressions, etc., and this information is used to adjust educational activities.

[0583] This invention comprises a system that provides optimal educational activities based on individual characteristics and emotional states. The system mainly consists of a "terminal" and a "server," and the "user" uses this system to engage in learning activities. A specific embodiment is shown below.

[0584] The terminal is responsible for collecting individual attribute information. The terminal is configured as a smartphone or wearable device, and collects behavioral and activity logs through these devices. It uses microphones and cameras to acquire emotional data from voice tone and facial expressions. The terminal also transmits the collected information to a server in real time or in batch processing.

[0585] The server is the primary component responsible for receiving information transmitted from terminals and performing data processing and analysis. The server utilizes generative AI models to analyze the data and identify the individual's interests, personality, and emotional state. Based on this analysis, an algorithm operates to propose educational activities tailored to the individual. The server also has the capability to flexibly adjust learning activities to reflect the individual's emotional state.

[0586] As a concrete example, the device continuously records the child's strengths and weaknesses in subjects, as well as recent emotional fluctuations, and sends this information to a server. Based on this information, the server can suggest activities such as listening to music or creating art when the child needs to relax.

[0587] Users can review and select suggested activities notified via their devices. Selected educational activities are conducted through the device, and progress data is collected and used for future feedback and suggestions.

[0588] A concrete example of a prompt is, "Please tell me what activities a 7-year-old child has recently been interested in. Also, based on this week's emotional tendencies, please tell me what learning activities would be most suitable." By inputting this prompt into the AI ​​model, new suggestions will be generated.

[0589] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0590] Step 1:

[0591] The device collects attribute information about the individual. During this collection phase, sensors from smartphones and wearable devices are used to acquire behavioral and life log data. The input consists of raw data about the individual's daily activities and emotional state. Specifically, the device measures distance traveled using motion sensors and captures conversational tone with a microphone. The output is a dataset summarizing this information.

[0592] Step 2:

[0593] The terminal encrypts the collected data and sends it to the server. Specifically, the terminal performs data cleaning to fill in missing data and prepare it for transmission. The input is the clean data collected in step 1, and the output is the dataset securely transferred to the server.

[0594] Step 3:

[0595] The server feeds the received data into a generating AI model and begins the analysis process. The inputs here are attribute information and emotion data sent from the terminal. Based on the data, the server uses machine learning algorithms to identify the individual's interests, personality, and emotional state. The output is an analysis report of the individual.

[0596] Step 4:

[0597] The server uses an AI model to generate and propose the most suitable educational activities for each individual based on the analysis results. The input is the analysis results obtained in step 3. Here, it calculates situation-dependent activities, such as suggesting challenging learning activities when positive emotions are high. The output is a list of learning activities optimized for the individual.

[0598] Step 5:

[0599] The device receives a list of educational activities provided by the server and notifies the individual and their guardian. Specifically, it displays a pop-up message on the device screen, visually showing activity suggestions based on the history and the reasons for them. The input is the list of educational activities generated in step 4, and the output is the notification to the user and the information presented.

[0600] Step 6:

[0601] The user selects an educational activity based on notifications from their device. In this specific example, if the user chooses reading, the system records this and uses the feedback to inform the next activity selection. The input is the educational activity suggestions from the device, and the output is the user's selection data.

[0602] Step 7:

[0603] The server collects user selection and ongoing progress data to generate feedback. Input includes emotional changes and performance data as selected activities are performed. The server uses this information to refine the feedback and inform future suggestions. The output is a feedback report, which is notified to the user in real time.

[0604] (Application Example 2)

[0605] 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."

[0606] In today's educational environment, there is a problem in that learning support is insufficient because learning content and methods tailored to individual children are not provided, and therefore, learning support that is in line with children's growth and emotional state is not adequate. Furthermore, conventional learning support systems are not able to flexibly respond to changes in children's emotions and interests, making it difficult to provide a personalized learning experience.

[0607] 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.

[0608] In this invention, the server includes means for collecting children's characteristic data, means for analyzing emotional data and adjusting learning activities and feedback according to the child's emotional state, and means for visually displaying this information. This makes it possible to provide learning activities optimized for each child and to provide appropriate feedback according to their emotional state.

[0609] "Child characteristics data" refers to information about a child's interests, personality, behavioral patterns, and daily activities.

[0610] "Emotional data" refers to information indicating a child's emotional state, obtained from their facial expressions and voice.

[0611] "Methods of analysis" refers to the process of evaluating children's interests, personalities, and emotional states based on collected data, and then deriving optimal learning activities based on those results.

[0612] A "means for recommending learning activities" refers to a system that, based on analysis results, suggests learning activities that are appropriate for a child's interests and emotional state.

[0613] "Means of notification" refers to methods or devices for providing information obtained based on analysis and recommendations to children and their guardians.

[0614] "Means of adjusting feedback" refer to a system that appropriately changes the feedback on learning activities and progress according to the child's emotional state.

[0615] "Means of visual display" refers to display devices or interfaces that display information in a visual format to make it easier for users to understand.

[0616] This invention is a system that collects and analyzes children's characteristic data and emotional data, and recommends optimal learning activities based on this data. The implementation of the system includes the following steps:

[0617] First, the device collects data on the child's characteristics and emotions. Specifically, it uses a camera and microphone to capture the child's facial expressions and voice, and uses this data to determine their emotional state. Activity data is collected through wearable devices and smartphone applications. This allows for an understanding of the child's behavioral patterns and interests.

[0618] The collected data is analyzed by a server. The server processes the emotional data using Google Cloud's AI services and Amazon Web Services' emotion analysis API. TensorFlow and OpenCV technologies are used for child face recognition and voice analysis.

[0619] Next, the server recommends the most suitable learning activities for the child based on the analysis results. The recommended activities are notified to the user via the device. This notification includes explanations based on the reasons for selecting the activities and the child's emotional state.

[0620] When a user participates in a suggested learning activity, their progress is monitored in real time. The server adjusts feedback based on progress and recommends new activities as needed.

[0621] For example, if a child is feeling stressed, the system will suggest activities such as relaxing music or simple relaxation games. A key feature of this system is that it can adjust these suggestions according to the child's interests and changing emotional state.

[0622] Examples of prompts generated using a generative AI model are as follows:

[0623] "What learning activities are appropriate when a child is experiencing emotional instability?"

[0624] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0625] Step 1:

[0626] The device collects data on the child's characteristics and emotions. It uses a camera and microphone to capture the child's facial expressions and voice, and stores this as initial data. It obtains the child's facial images and voice files as input data.

[0627] Step 2:

[0628] The device sends the collected data to the server. Based on the received face images and audio files, the server performs face recognition using OpenCV, analyzes the audio using Google Cloud's AI services, and extracts emotional data. As output, it generates information about the child's emotional state.

[0629] Step 3:

[0630] The server analyzes children's characteristic and emotional data. Using TensorFlow, it models children's interests and behavioral patterns from this data. All the data collected as input is used to obtain evaluation results of children's interests and personalities as output.

[0631] Step 4:

[0632] The server recommends the most suitable learning activity based on the analysis results. It selects an appropriate learning activity from a list based on the personality and interest assessments obtained from the analysis. The output generates information including the selected learning activity and the reasons for its selection.

[0633] Step 5:

[0634] The device notifies the user of recommended learning activities. It displays activity details and selection reasons on the screen and provides an audio explanation of the activity. It receives activity recommendation information from the server as input and presents it to the user as output.

[0635] Step 6:

[0636] The user will carry out the suggested learning activities. They will start the activities according to the notifications they receive. In particular, if the child is emotionally unstable, they may choose activities that promote relaxation.

[0637] Step 7:

[0638] The server monitors the progress of learning activities and adjusts feedback as needed. It analyzes progress data and evaluates whether the activities are appropriate. It receives real-time activity data as input and generates feedback information as output.

[0639] Step 8:

[0640] The device notifies the user of feedback. It displays the progress of learning activities and feedback on the screen, and provides voice guidance if necessary. It receives feedback information from the server as input.

[0641] 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.

[0642] 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.

[0643] 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.

[0644] [Fourth Embodiment]

[0645] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0646] 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.

[0647] 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).

[0648] 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.

[0649] 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.

[0650] 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).

[0651] 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.

[0652] 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.

[0653] 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.

[0654] 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.

[0655] 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.

[0656] 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.

[0657] 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".

[0658] This invention relates to a system that recommends optimal learning activities based on a child's characteristics. This system collects and analyzes data on the child's personality, interests, and past activities, and then proposes learning activities and provides feedback based on this information. In this process, it also incorporates the opinions of parents and children, enabling proposals that satisfy both parties.

[0659] The device plays a role in recording the child's daily activities, collecting data through questionnaires and activity logs. For example, it gathers information such as what books the child reads and what sports activities they participate in.

[0660] After receiving this data, the server uses a generating AI to analyze it. This analysis identifies the child's areas of interest and personality traits, and then selects learning activities based on these.

[0661] Once the server has completed its selection process, the results will be notified to the user via their terminal. This notification will include a list of recommended learning activities, along with a detailed description of each activity.

[0662] Users can choose activities based on the information presented. Furthermore, parents and children can make decisions together, and their opinions are reflected in future recommendations.

[0663] Once an activity is selected, the server continuously monitors its progress and collects relevant feedback, including reports from teachers and coaches. Based on this feedback, the server suggests improvements as needed and recommends new activities.

[0664] All progress data is updated in real time and displayed visually through the device. This dashboard allows users to easily understand their child's growth and learning progress. In this way, the present invention is a system that provides parents and children with all the information and support they need to make better choices.

[0665] The following describes the processing flow.

[0666] Step 1:

[0667] The device collects records of the child's daily activities. This includes data such as activity type, frequency, and duration, using mobile apps or wearable devices. For example, exercise, reading, and digital media use are recorded.

[0668] Step 2:

[0669] The server receives data sent from the terminal and performs data cleaning. This removes invalid data, standardizes the format, and converts the data into a state suitable for analysis.

[0670] Step 3:

[0671] The server uses generative AI to analyze personality and interests based on the organized data. Here, natural language processing and image recognition technologies are used to identify features and patterns extracted from the data.

[0672] Step 4:

[0673] The server uses the analysis results as input to run a recommendation algorithm and select learning activities suitable for the child. Past success stories and data from other children with similar profiles are also taken into consideration.

[0674] Step 5:

[0675] The device notifies the child and their guardian of a list of recommended learning activities. This notification includes detailed information about each activity and the reasons for its selection.

[0676] Step 6:

[0677] Users select learning activities through their devices. This selection process can be done jointly by parents and children, and the results are reflected in recommendations for future activities.

[0678] Step 7:

[0679] The server collects feedback from devices, teachers, and coaches to monitor the progress of selected learning activities. This allows for an understanding of the level of achievement and satisfaction with the activities.

[0680] Step 8:

[0681] The server generates a progress report based on the feedback and presents it to the user. The report includes the current progress, areas for improvement, and advice on the next steps.

[0682] Step 9:

[0683] The device updates a real-time dashboard, allowing users to visually monitor their child's growth and learning progress. It displays data intuitively using graphs and charts.

[0684] (Example 1)

[0685] 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".

[0686] In recent years, education based on children's individuality has become increasingly important, but discovering and recommending the most suitable learning activities for each child is not easy. In particular, achieving education that matches a child's interests and personality requires individual data analysis, and the technology to perform this efficiently is needed. Furthermore, it is desirable to consistently provide activity recommendations and feedback based on the analysis results, but achieving this presents the challenge of building a complex system.

[0687] 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.

[0688] In this invention, the server includes a device for collecting children's attribute data, a device for processing the acquired data and analyzing the children's interests and personalities, and a device for selecting appropriate learning activities based on the analysis results. This makes it possible to select learning activities tailored to each individual child.

[0689] "Child attribute data" refers to information used to provide individualized education, such as a child's interests, personality, and past activity records.

[0690] "Analysis" refers to the process of identifying children's interests and personality traits based on collected data.

[0691] "Learning activities" refer to specific actions or programs proposed to children for educational or growth-promoting purposes.

[0692] A "server" refers to a computer system that processes, stores, analyzes, and communicates data.

[0693] "Device" refers to a general term for hardware or software designed to perform a specific function.

[0694] A "generative AI model" refers to an artificial intelligence algorithm designed for data analysis or generating specific outputs.

[0695] A "prompt statement" refers to an input statement used to control the output of a generative AI model.

[0696] "Notification" refers to the act of communicating analysis results and recommended activities to the user.

[0697] "Evaluation" refers to feedback regarding the progress and results of activities, and it is the basis for determining the next steps.

[0698] This invention relates to a system for optimizing educational activities based on a child's characteristics. Specifically, it includes a process of analyzing a child's interests, personality, and past activity records, and using this information to select appropriate learning activities.

[0699] The device is the primary device used to collect children's attribute data. This device typically consists of a smartphone or tablet, which records daily activity logs and collects data in the form of questionnaires. For example, it might record information such as what books a child reads or what sports they participate in.

[0700] The collected data is sent to a server. This server is equipped with functions to process and analyze the data, and uses generative AI models to identify areas of interest and personality traits of children. Analysis tools such as Python and R are used for data analysis. This makes it possible to select the most suitable learning activities for each child.

[0701] Selected learning activities are notified to the user via their device. This information allows parents and children to choose the best activities together. Furthermore, the selected activities are continuously monitored by a server, and feedback is provided based on progress. This includes reports from educators and instructors.

[0702] This information is displayed visually, making it easy for users to understand their child's growth and learning progress. For example, one example of a prompt is, "Based on recent activities, identify the most suitable learning activities for this child."

[0703] In this way, this invention is an embodiment for customizing the educational experience for individual children and enabling the selection and adjustment of optimal learning activities.

[0704] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0705] Step 1:

[0706] The device collects attribute data from children's daily activities. Specifically, it obtains information through questionnaires and activity log applications. The data entered includes information such as children's reading habits and the activities they participate in. This data is then organized for subsequent analysis.

[0707] Step 2:

[0708] The terminal encrypts the collected attribute data and sends it to the server. SSL / TLS protocols are used to ensure secure data transfer. The input is encrypted attribute data, and the output is the data arriving at the server.

[0709] Step 3:

[0710] The server saves the received data to a database and begins the analysis. A generative AI model processes the data using a Python analysis library. For example, it might use the prompt "Identify the most suitable learning activities for this child based on their recent activities" to select appropriate learning activities based on the child's interests and personality. The input to the analysis is the saved attribute data, and the output is a list of learning activities selected based on the child's characteristics.

[0711] Step 4:

[0712] The server sends the analysis results to the terminal. The terminal displays the suggested learning activities in a user-friendly interface. The input is a list of selected learning activities, and the output is a visually designed user interface.

[0713] Step 5:

[0714] Users select learning activities based on the information presented. This selection can be made jointly by parents and children, and feedback is collected during this process. The input consists of the user's selections and opinions, while the output is feedback that helps improve the accuracy of future analyses.

[0715] Step 6:

[0716] The server receives reports from educators and instructors to monitor the progress of selected activities. This allows for the suggestion of new ideas and improvements as needed. The input is the activity progress report, and the output is improvement suggestions.

[0717] Step 7:

[0718] The device visually displays all progress data and feedback in real time, making it easy for users to track their child's progress. Input is the latest progress data and feedback, and output is a visualized dashboard.

[0719] (Application Example 1)

[0720] 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".

[0721] Currently, there is a problem in that optimal learning activities tailored to each child's individual characteristics are not being adequately proposed. Furthermore, there is a lack of means for parents and educators to efficiently grasp the progress of children's learning activities and effectively utilize feedback. Therefore, there is a need for learning support methods that maximize children's interests and growth.

[0722] 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.

[0723] In this invention, the server includes means for collecting data on a child's characteristics, means for processing the collected data and analyzing the child's interests and personality, and means for interacting with the child through a home-use device and informing them of suggested learning activities through voice and display. This makes it possible to stimulate the child's interests and suggest personalized learning activities.

[0724] "Child characteristics data" refers to information about a child's personality, interests, and past activities.

[0725] "Means of collection" refers to methods of obtaining data through everyday activities or surveys.

[0726] "Methods of analysis" refer to the process of identifying a child's interests and personality based on the collected data.

[0727] "Optimal learning activities" are those selected based on a child's characteristics and data, designed to maximize the child's learning effectiveness.

[0728] "Means of notification" refers to methods of communicating the results of recommended learning activities to parents and children.

[0729] "Monitoring methods" refer to methods for continuously tracking and recording the progress of learning activities.

[0730] "Means of providing feedback" refers to the process of providing evaluations and suggestions for improvement regarding the progress of learning activities.

[0731] A "means of visual display" refers to an interface for presenting information visually.

[0732] "Household machines" refer to robots and electronic devices used within the home.

[0733] "Generative AI technology" is an artificial intelligence technology that automatically generates insights from data with minimal human intervention.

[0734] 1. Generate a program for the system that implements this application example.

[0735] This invention provides a system that supports learning based on a child's characteristics. A server collects data on the child's characteristics and analyzes it using a generative AI model. Through this analysis, the child's interests and personality are identified, and the optimal learning activities are selected. The selection results are communicated to the user via voice and display through a home device.

[0736] The server utilizes speech recognition technology to transcribe collected audio and activity data of children into text. Cloud computing feeds this data into a generative AI model, which generates suggestions for learning activities tailored to the child's interests. These suggestions are displayed and notified via in-home devices in a way that is easily understood visually and audibly.

[0737] For example, the server analyzes collected data to determine that a child's preference is "insect observation," and recommends insect-related books and observation activities. This information is then communicated via voice from a device in the home, such as, "Let's borrow some books from the library for insect observation." An example of a prompt message could be, "Please recommend new learning activities for a child who is interested in insects."

[0738] This system allows parents and children to receive information tailored to the child's interests and characteristics, enabling them to select learning activities more appropriately.

[0739] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0740] Step 1:

[0741] The server collects data on the child's characteristics from the device. The device obtains daily activity data and survey information and sends it to the server. The input consists of activity logs and data related to interests, which the server receives and stores.

[0742] Step 2:

[0743] The server performs analysis using a generative AI model based on the collected data. The input is the characteristic data received in step 1. The server inputs this data into the AI ​​model and processes it to estimate the child's interests and personality. The output is the child's characteristic assessment information based on the analysis.

[0744] Step 3:

[0745] The server selects the optimal learning activity based on the generated characteristic assessment information. Here, estimated interests and personality traits are used as input, and suitable learning activities are searched and extracted from the database. The output is a list of recommended learning activities.

[0746] Step 4:

[0747] The server notifies the user of selected learning activities via a home device. The input is a list of recommended learning activities, which is converted into audio and visual information and sent to the hardware. The user receives the notification through the device's voice function and display.

[0748] Step 5:

[0749] The user selects a learning activity based on notifications from the server. Input in this step is a notification from the device, and the selected activity is returned to the server. The server records this information and uses it to recommend future activities.

[0750] Step 6:

[0751] The server continuously monitors the progress of selected learning activities and collects feedback. Input is progress data obtained during the activity, and output is evaluation and feedback on areas for improvement based on this data. This allows the server to accumulate data for suggesting new activities.

[0752] 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.

[0753] This invention relates to a system that recommends optimal learning activities based on a child's characteristics and recognizes the user's emotions to provide feedback. By incorporating an emotion engine, this system provides an appropriate learning environment according to the child's emotional state and enables emotion-based feedback and adjustment of activities.

[0754] The device plays a role in collecting data on the child's daily activities and emotions. Activity data is obtained through mobile apps and wearable devices and includes the child's behavior, preferences, and activity logs. Emotional data, on the other hand, is collected by recording voice tone and facial expressions.

[0755] The server receives data sent from the terminal and performs data cleaning and sentiment analysis. A sentiment engine assists in this process, identifying the user's emotional state from voice and facial expressions. The analysis results consider emotional trends in addition to the child's personality and interests.

[0756] The server then recommends learning activities suitable for the child based on the collected and analyzed data. These recommendations are tailored to the user's emotions; for example, if the user is experiencing high stress levels, it can suggest activities that promote relaxation.

[0757] The device notifies children and their guardians of the results of recommended learning activities. The information provided includes details of the activities and the reasons for their selection, based on the user's sentiment.

[0758] When a user selects an activity based on the information presented, the server monitors the progress based on that selection and collects necessary feedback. This includes adjusting the feedback in response to changes in the user's emotions.

[0759] Progress information and feedback are updated in real time via the device and displayed visually. This allows users to understand their child's growth and learning progress along with their emotional responses. In this way, the present invention is a system that not only provides an optimal learning environment for children but also leverages insights gained from emotions to realize a more personalized educational experience.

[0760] The following describes the processing flow.

[0761] Step 1:

[0762] The device collects data on the child's daily activities and emotions. Activity data is obtained through a mobile app or wearable device, while emotional data is acquired by recording voice tone and facial expressions in video.

[0763] Step 2:

[0764] The server receives data sent from the terminal. First, it performs data cleaning to remove and correct incomplete or incorrect data.

[0765] Step 3:

[0766] The server uses an emotion engine to analyze voice and facial expression data to identify the user's emotional state. For example, voice analysis quantifies the degree of emotion, while facial expression analysis evaluates subtle facial movements.

[0767] Step 4:

[0768] The server combines analyzed personal characteristics and emotional data, and applies a recommendation algorithm to determine learning activities suitable for the child. Emotional data acts as a modifier in activity selection, suggesting activities that are optimal for the child's mental state.

[0769] Step 5:

[0770] The device notifies the child and parent of a list of selected learning activities. The notification includes details of each activity and an explanation of how they correspond to emotional data.

[0771] Step 6:

[0772] The user selects the activity they most agree with based on the information provided. In this process, parents and children can jointly make the optimal choice.

[0773] Step 7:

[0774] The server monitors the progress of the selected activities and collects relevant feedback. It also monitors user sentiment and adjusts the feedback in real time.

[0775] Step 8:

[0776] The server analyzes the collected feedback and sentiment data and generates new suggestions and advice as needed.

[0777] Step 9:

[0778] The device updates progress and feedback information on a real-time dashboard, visually displaying it to allow users to intuitively understand their child's learning and emotional changes.

[0779] (Example 2)

[0780] 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".

[0781] In recent years, the importance of individual optimization and personalization in education has increased, but accurately understanding individual characteristics and emotional states and proposing appropriate educational activities remains a challenging task. In particular, technologies for analyzing individuals' emotional states in real time and providing educational activities accordingly are underdeveloped, and there is a need to improve the quality of education through such technologies. Furthermore, it is also important to continuously adjust educational activities through monitoring and feedback. This invention aims to solve these problems.

[0782] 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.

[0783] In this invention, the server includes means for acquiring individual attribute information, means for processing the acquired information and analyzing the individual's interests and personality, and means for performing emotion analysis and identifying the individual's emotional state. This makes it possible to adaptively provide educational activities to the individual based on their characteristics and emotional state. Furthermore, by providing emotionally appropriate feedback in real time along with suggesting educational activities, a personalized educational experience is realized.

[0784] "Individual" refers to the person receiving education or the user, who is the primary user of this invention.

[0785] "Attribute information" refers to all relevant information about an individual, including characteristic data, behavioral history, interests, and personality information.

[0786] "Means of analysis" refers to technical means used to identify and evaluate an individual's interests, personality, and emotional state based on collected data, and to utilize this information for educational activities.

[0787] "Educational activities" refer to a series of learning and training behaviors or programs proposed based on an individual's interests and emotional state.

[0788] "Means of notification" refers to technical means of informing individuals or supervisors of the proposed results of educational activities, and includes visual or auditory methods.

[0789] "Monitoring methods" refer to a series of processes for continuously observing and tracking the progress of an individual's activities and providing appropriate feedback.

[0790] "Means of visual presentation" refers to methods of visually displaying individual progress information and feedback, and includes technologies that provide information via displays.

[0791] "Emotional analysis" refers to a technology that identifies an individual's emotional state from their voice, facial expressions, etc., and this information is used to adjust educational activities.

[0792] This invention comprises a system that provides optimal educational activities based on individual characteristics and emotional states. The system mainly consists of a "terminal" and a "server," and the "user" uses this system to engage in learning activities. A specific embodiment is shown below.

[0793] The terminal is responsible for collecting individual attribute information. The terminal is configured as a smartphone or wearable device, and collects behavioral and activity logs through these devices. It uses microphones and cameras to acquire emotional data from voice tone and facial expressions. The terminal also transmits the collected information to a server in real time or in batch processing.

[0794] The server is the primary component responsible for receiving information transmitted from terminals and performing data processing and analysis. The server utilizes generative AI models to analyze the data and identify the individual's interests, personality, and emotional state. Based on this analysis, an algorithm operates to propose educational activities tailored to the individual. The server also has the capability to flexibly adjust learning activities to reflect the individual's emotional state.

[0795] As a concrete example, the device continuously records the child's strengths and weaknesses in subjects, as well as recent emotional fluctuations, and sends this information to a server. Based on this information, the server can suggest activities such as listening to music or creating art when the child needs to relax.

[0796] Users can review and select suggested activities notified via their devices. Selected educational activities are conducted through the device, and progress data is collected and used for future feedback and suggestions.

[0797] A concrete example of a prompt is, "Please tell me what activities a 7-year-old child has recently been interested in. Also, based on this week's emotional tendencies, please tell me what learning activities would be most suitable." By inputting this prompt into the AI ​​model, new suggestions will be generated.

[0798] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0799] Step 1:

[0800] The device collects attribute information about the individual. During this collection phase, sensors from smartphones and wearable devices are used to acquire behavioral and life log data. The input consists of raw data about the individual's daily activities and emotional state. Specifically, the device measures distance traveled using motion sensors and captures conversational tone with a microphone. The output is a dataset summarizing this information.

[0801] Step 2:

[0802] The terminal encrypts the collected data and sends it to the server. Specifically, the terminal performs data cleaning to fill in missing data and prepare it for transmission. The input is the clean data collected in step 1, and the output is the dataset securely transferred to the server.

[0803] Step 3:

[0804] The server feeds the received data into a generating AI model and begins the analysis process. The inputs here are attribute information and emotion data sent from the terminal. Based on the data, the server uses machine learning algorithms to identify the individual's interests, personality, and emotional state. The output is an analysis report of the individual.

[0805] Step 4:

[0806] The server uses an AI model to generate and propose the most suitable educational activities for each individual based on the analysis results. The input is the analysis results obtained in step 3. Here, it calculates situation-dependent activities, such as suggesting challenging learning activities when positive emotions are high. The output is a list of learning activities optimized for the individual.

[0807] Step 5:

[0808] The device receives a list of educational activities provided by the server and notifies the individual and their guardian. Specifically, it displays a pop-up message on the device screen, visually showing activity suggestions based on the history and the reasons for them. The input is the list of educational activities generated in step 4, and the output is the notification to the user and the information presented.

[0809] Step 6:

[0810] The user selects an educational activity based on notifications from their device. In this specific example, if the user chooses reading, the system records this and uses the feedback to inform the next activity selection. The input is the educational activity suggestions from the device, and the output is the user's selection data.

[0811] Step 7:

[0812] The server collects user selection and ongoing progress data to generate feedback. Input includes emotional changes and performance data as selected activities are performed. The server uses this information to refine the feedback and inform future suggestions. The output is a feedback report, which is notified to the user in real time.

[0813] (Application Example 2)

[0814] 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".

[0815] In today's educational environment, there is a problem in that learning support is insufficient because learning content and methods tailored to individual children are not provided, and therefore, learning support that is in line with children's growth and emotional state is not adequate. Furthermore, conventional learning support systems are not able to flexibly respond to changes in children's emotions and interests, making it difficult to provide a personalized learning experience.

[0816] 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.

[0817] In this invention, the server includes means for collecting children's characteristic data, means for analyzing emotional data and adjusting learning activities and feedback according to the child's emotional state, and means for visually displaying this information. This makes it possible to provide learning activities optimized for each child and to provide appropriate feedback according to their emotional state.

[0818] "Child characteristics data" refers to information about a child's interests, personality, behavioral patterns, and daily activities.

[0819] "Emotional data" refers to information indicating a child's emotional state, obtained from their facial expressions and voice.

[0820] "Methods of analysis" refers to the process of evaluating children's interests, personalities, and emotional states based on collected data, and then deriving optimal learning activities based on those results.

[0821] A "means for recommending learning activities" refers to a system that, based on analysis results, suggests learning activities that are appropriate for a child's interests and emotional state.

[0822] "Means of notification" refers to methods or devices for providing information obtained based on analysis and recommendations to children and their guardians.

[0823] "Means of adjusting feedback" refer to a system that appropriately changes the feedback on learning activities and progress according to the child's emotional state.

[0824] "Means of visual display" refers to display devices or interfaces that display information in a visual format to make it easier for users to understand.

[0825] This invention is a system that collects and analyzes children's characteristic data and emotional data, and recommends optimal learning activities based on this data. The implementation of the system includes the following steps:

[0826] First, the device collects data on the child's characteristics and emotions. Specifically, it uses a camera and microphone to capture the child's facial expressions and voice, and uses this data to determine their emotional state. Activity data is collected through wearable devices and smartphone applications. This allows for an understanding of the child's behavioral patterns and interests.

[0827] The collected data is analyzed by a server. The server processes the emotional data using Google Cloud's AI services and Amazon Web Services' emotion analysis API. TensorFlow and OpenCV technologies are used for child face recognition and voice analysis.

[0828] Next, the server recommends the most suitable learning activities for the child based on the analysis results. The recommended activities are notified to the user via the device. This notification includes explanations based on the reasons for selecting the activities and the child's emotional state.

[0829] When a user participates in a suggested learning activity, their progress is monitored in real time. The server adjusts feedback based on progress and recommends new activities as needed.

[0830] For example, if a child is feeling stressed, the system will suggest activities such as relaxing music or simple relaxation games. A key feature of this system is that it can adjust these suggestions according to the child's interests and changing emotional state.

[0831] Examples of prompts generated using a generative AI model are as follows:

[0832] "What learning activities are appropriate when a child is experiencing emotional instability?"

[0833] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0834] Step 1:

[0835] The device collects data on the child's characteristics and emotions. It uses a camera and microphone to capture the child's facial expressions and voice, and stores this as initial data. It obtains the child's facial images and voice files as input data.

[0836] Step 2:

[0837] The device sends the collected data to the server. Based on the received face images and audio files, the server performs face recognition using OpenCV, analyzes the audio using Google Cloud's AI services, and extracts emotional data. As output, it generates information about the child's emotional state.

[0838] Step 3:

[0839] The server analyzes children's characteristic and emotional data. Using TensorFlow, it models children's interests and behavioral patterns from this data. All the data collected as input is used to obtain evaluation results of children's interests and personalities as output.

[0840] Step 4:

[0841] The server recommends the most suitable learning activity based on the analysis results. It selects an appropriate learning activity from a list based on the personality and interest assessments obtained from the analysis. The output generates information including the selected learning activity and the reasons for its selection.

[0842] Step 5:

[0843] The device notifies the user of recommended learning activities. It displays activity details and selection reasons on the screen and provides an audio explanation of the activity. It receives activity recommendation information from the server as input and presents it to the user as output.

[0844] Step 6:

[0845] The user will carry out the suggested learning activities. They will start the activities according to the notifications they receive. In particular, if the child is emotionally unstable, they may choose activities that promote relaxation.

[0846] Step 7:

[0847] The server monitors the progress of learning activities and adjusts feedback as needed. It analyzes progress data and evaluates whether the activities are appropriate. It receives real-time activity data as input and generates feedback information as output.

[0848] Step 8:

[0849] The device notifies the user of feedback. It displays the progress of learning activities and feedback on the screen, and provides voice guidance if necessary. It receives feedback information from the server as input.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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."

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] The following is further disclosed regarding the embodiments described above.

[0872] (Claim 1)

[0873] Methods for collecting data on children's characteristics,

[0874] A means of processing the collected data and analyzing children's interests and personalities,

[0875] Based on the analysis results, a means to recommend the optimal learning activities,

[0876] A means of notifying parents and children of the recommendation results,

[0877] A means of continuously monitoring the progress of activities and providing feedback,

[0878] Means for visually displaying this information,

[0879] A system that includes this.

[0880] (Claim 2)

[0881] The system according to claim 1, which analyzes the generated feedback and suggests the next learning activity.

[0882] (Claim 3)

[0883] The system according to claim 1, which acquires and uses feedback information from teachers and coaches for analysis.

[0884] "Example 1"

[0885] (Claim 1)

[0886] A device for collecting children's attribute data,

[0887] A device that processes acquired data and analyzes children's interests and personalities,

[0888] A device that selects appropriate learning activities based on the analysis results,

[0889] A device that notifies parents and children of the selection results,

[0890] A device that continuously monitors and evaluates the status of activities,

[0891] A device that performs analysis using a generative AI model,

[0892] A device that generates appropriate output using prompt statements,

[0893] A device that visually displays this information,

[0894] A system that includes this.

[0895] (Claim 2)

[0896] The system according to claim 1, which analyzes the generated evaluation and proposes the next learning activity.

[0897] (Claim 3)

[0898] The system according to claim 1, which acquires and uses evaluation information from educators and instructors for analysis.

[0899] "Application Example 1"

[0900] (Claim 1)

[0901] Methods for collecting data on children's characteristics,

[0902] A means of processing the collected data and analyzing children's interests and personalities,

[0903] Based on the analysis results, a means to recommend the optimal learning activities,

[0904] A means of notifying parents and children of the recommendation results,

[0905] A means of continuously monitoring the progress of activities and providing feedback,

[0906] Means for visually displaying this information,

[0907] A means of interacting with children through a home-use device and informing them of suggested learning activities through voice and display,

[0908] A means of dynamically selecting interesting learning activities using generative AI technology,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The system according to claim 1, which analyzes the generated feedback and suggests the next learning activity.

[0912] (Claim 3)

[0913] The system according to claim 1, which acquires feedback information from instructors and uses it for analysis.

[0914] "Example 2 of combining an emotion engine"

[0915] (Claim 1)

[0916] A means of obtaining individual attribute information,

[0917] A means of processing acquired information and analyzing the interests and personality of individuals,

[0918] A means of proposing optimal educational activities based on the analysis results,

[0919] Means for notifying supervisors and individuals of the proposed results,

[0920] Means for continuously monitoring the progress of activities and providing advice,

[0921] Means for visually presenting this information,

[0922] A means of performing emotion analysis to identify an individual's emotional state,

[0923] Means for adjusting and providing educational activities in accordance with this emotional state,

[0924] A system that includes this.

[0925] (Claim 2)

[0926] The system according to claim 1, which analyzes the generated advice and proposes the next educational activity.

[0927] (Claim 3)

[0928] The system according to claim 1, which obtains and uses advice information from educators for analysis.

[0929] "Application example 2 when combining with an emotional engine"

[0930] (Claim 1)

[0931] Methods for collecting data on children's characteristics,

[0932] A means of processing the collected data and analyzing children's interests and personalities,

[0933] Based on the analysis results, a means to recommend the optimal learning activities,

[0934] A means of notifying parents and children of the recommendation results,

[0935] A means of analyzing emotional data and adjusting learning activities and feedback according to the child's emotional state,

[0936] A means of continuously monitoring the progress of activities and providing feedback,

[0937] Means for visually displaying this information,

[0938] A system that includes this.

[0939] (Claim 2)

[0940] The system according to claim 1, which analyzes the generated feedback and suggests the next learning activity.

[0941] (Claim 3)

[0942] The system according to claim 1, which acquires and uses feedback information from teachers and coaches for analysis. [Explanation of Symbols]

[0943] 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. Methods for collecting data on children's characteristics, A means of processing the collected data and analyzing children's interests and personalities, Based on the analysis results, a means to recommend the optimal learning activities, A means of notifying parents and children of the recommendation results, A means of continuously monitoring the progress of activities and providing feedback, Means for visually displaying this information, A system that includes this.

2. The system according to claim 1, which analyzes the generated feedback and suggests the next learning activity.

3. The system according to claim 1, which acquires and uses feedback information from teachers and coaches for analysis.

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