system

Abstract

Provide a system. 【Solution means】 Means for obtaining learning status, information on educational organization events, and information on sports activities from an electronic device that inputs information; Means for automatically collecting regional examination information and statistical information; Means for integrating the obtained and collected numerical data and performing preprocessing; Means for applying artificial intelligence to identify problem areas using the preprocessed numerical data; Means for generating an educational plan and career proposal based on the results derived by artificial intelligence; Means for transmitting and presenting the generated proposal to an electronic device; Means for incorporating a function in which a home automation device monitors the knowledge absorption status of a learner and assists in proposing educational policies; A system including the above.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern family education, there is a problem that it is difficult to provide an efficient and effective learning plan while grasping the child's learning situation and growth process in detail. Parents cannot allocate sufficient time for their children's education in their busy lives, and it is also difficult for children themselves to choose the optimal path that suits their interests and goals. Therefore, there is a problem that it is difficult to maintain the motivation for children's learning and to select an effective further education destination.

Means for Solving the Problems

[0005] This invention provides a system that automatically collects information on learning progress, educational institution events, and sports activities from an input terminal, as well as local examination information and statistical data. After integrating and pre-processing this information, the system uses artificial intelligence to identify problem areas and generate learning plans and career path suggestions, thereby providing the optimal plan for each child. This system transmits and presents the generated suggestions to the user's terminal, enabling parents and children to always manage and select the most suitable educational approach. Furthermore, to improve data accuracy, the system includes means for removing outliers and normalizing data, and the artificial intelligence model can adapt to the user and adjust the suggestions, allowing for individualized customization.

[0006] A "terminal for inputting information" is a device used by users to input data and transmit it to a system.

[0007] "Learning status" refers to information that shows what learning activities a child is currently engaged in and their learning progress.

[0008] "Educational institution events" refers to information about events, activities, tests, and other matters held at schools.

[0009] "Physical activity" refers to information that includes club activities and sports-related activities in which children participate.

[0010] "Regional exam information" refers to data such as the average academic ranking (deviation score) of schools in a specific region and the distribution of past test-takers' scores.

[0011] "Integrating and preprocessing" refers to the process of unifying data obtained from different sources and normalizing the data and handling outliers.

[0012] Artificial intelligence is a technology that analyzes large amounts of data to generate optimal learning plans and career guidance suggestions.

[0013] "Identifying areas of difficulty" means recognizing areas that need improvement and areas where a child excels based on their learning situation and academic performance.

[0014] A "learning plan" is a plan that includes specific schedules and goals created to efficiently advance a child's learning activities.

[0015] "Career guidance" refers to recommendations aimed at showing children appropriate schools or courses.

[0016] "Removing outliers" is the process of removing incorrect data that is unsuitable for analysis.

[0017] "Data normalization" is the process of improving the accuracy of analysis by arranging data into a consistent format.

[0018] "Adapting suggestions to the user" refers to the function where artificial intelligence customizes suggestions according to the individual user's needs and conditions. [Brief explanation of the drawing]

[0019] [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] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0020] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a processor with a reference number (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.

[0023] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0024] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0025] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0026] 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."

[0027] [First Embodiment]

[0028] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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".

[0040] This invention is a system that collects detailed information such as a child's learning progress, educational institution events, and sports activities from a user-inputted terminal, and uses this information to support the child's learning. This system is realized through the cooperation of both the terminal and the server to process information in real time.

[0041] Explanation of program processing in natural language

[0042] Data collection

[0043] Users input information about their children's test results, extracurricular activity schedules, and desired schools through their devices. The devices then send this information to a server. The server further collects local exam information and statistical data from the internet.

[0044] Data Integration and Preprocessing

[0045] The server integrates data received from users and data acquired from external sources. It improves data quality by standardizing data formats and removing outliers.

[0046] Data Analysis

[0047] The server applies an artificial intelligence model to the integrated data and performs analysis. This identifies children's strengths, areas for improvement, and regional testing trends. The AI ​​model performs these analyses using machine learning algorithms.

[0048] Generating learning plans and career path suggestions

[0049] Based on the analysis results, the server generates optimal learning plans and career path suggestions for the user. These suggestions include weekly and monthly schedules, specific learning goals, and a list of potential schools.

[0050] Presentation to the user

[0051] Plans and proposals generated from the server are sent to the terminal, which then presents them to the user in a visualized format. This can be presented not only as text but also visually, such as graphs and charts.

[0052] Specific example

[0053] For example, if the information entered by the user on the device includes that the child achieved a high score on a math test, the server will suggest a set of math-related problems of a relatively high difficulty level. Furthermore, based on information about extracurricular activities, the system can automatically create a schedule that ensures efficient study time according to the child's activity schedule.

[0054] This system aims to improve the quality of education by effectively supporting children's learning even when users are busy, and by providing information that can be used as a reference for career choices.

[0055] The following describes the processing flow.

[0056] Step 1:

[0057] Users use their devices to input information such as their child's learning progress, grades, and extracurricular activity schedules. This information is temporarily stored on the device.

[0058] Step 2:

[0059] The terminal sends data entered by the user to the server. During transmission, the data integrity and format are checked, and the data format is converted as needed.

[0060] Step 3:

[0061] The server stores the received data in a database and simultaneously collects local exam information and related statistical data from the internet. At this stage, APIs are used to retrieve the data.

[0062] Step 4:

[0063] The server integrates and preprocesses the collected data. Specifically, it imputes missing values, detects and removes outliers, and generates a clean dataset suitable for analysis.

[0064] Step 5:

[0065] The server uses pre-processed data to run machine learning algorithms. This process involves analyzing children's strengths and weaknesses, as well as extracting trends in regional exam preparation.

[0066] Step 6:

[0067] The server generates study plans and career path suggestions based on machine learning results. This includes allocating study time for each subject and listing recommended universities and colleges.

[0068] Step 7:

[0069] The server generates suggestions and sends them to the terminal. The suggestions are formatted in a visually easy-to-understand way and presented to the user using charts and graphics.

[0070] Step 8:

[0071] The device receives information from the server and notifies the user. The user can then view detailed learning plans and career suggestions on the device.

[0072] (Example 1)

[0073] 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."

[0074] Traditional educational support systems have struggled to provide individually tailored advice and plans based on each child's learning progress and career choices. Therefore, despite the availability of diverse learning environments and career information, there were concerns that the optimal information for each child was not being provided, leading to a decline in the quality of education. Furthermore, there was a lack of mechanisms for timely collection and utilization of local examination information, making efficient learning support difficult.

[0075] 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.

[0076] In this invention, the server includes means for automatically collecting regional test information and statistical information through a wide-area information source; means for integrating the acquired data and performing preprocessing, including standardization and outlier removal; and means for applying machine learning algorithms to identify learning areas using the preprocessed data. This makes it possible to provide individualized learning plans and career suggestions in real time, thereby improving the quality of education.

[0077] A "terminal" is a device used for inputting and displaying information, and it provides an interface with the user.

[0078] "Wide-area information sources" refer to information sources that exist on the internet and are used to obtain external data related to education.

[0079] "Local examination information" refers to information about examinations conducted by local educational institutions and within local communities, including the content and schedule of the examinations that students take.

[0080] "Statistical information" refers to a dataset that analyzes education-related data and provides indicators such as trends and averages.

[0081] Standardization is the process of transforming data according to certain standards to make it easier to process and analyze.

[0082] "Outlier removal" refers to the operation of eliminating outliers and noise present in a dataset to improve data quality.

[0083] A "machine learning algorithm" is a computational method that uses data to train a model and perform predictions or classifications for specific tasks.

[0084] An "artificial intelligence model" is a computer program designed to automatically perform analysis and make decisions based on input data.

[0085] A "study plan" refers to a set of activities and schedules designed for students to achieve specific learning objectives.

[0086] "Career guidance" refers to information that provides recommendations and guidelines regarding students' studies and future career choices.

[0087] This invention is an educational information processing system aimed at supporting children's learning, realized through the collaboration of a terminal where the user inputs information and a server that processes the data and generates suggestions. The terminal can be a personal computer, smartphone, tablet, etc., and is provided with an intuitive interface.

[0088] Users input information such as their learning progress, educational institution event information, and club activity schedules into a terminal. This information is transmitted to a server via the internet. The server is equipped with software to automatically collect regional exam information and statistical data. This system utilizes specific information collection APIs and web scraping techniques to efficiently obtain necessary data from a wide range of education-related information sources.

[0089] Next, the server integrates the aforementioned data and performs standardization and outlier removal to improve data quality. Suitable database systems include PostgreSQL and MySQL®. Based on this pre-processed data, the server applies machine learning algorithms to identify the child's strengths and areas that need improvement in their learning. AI models used here include libraries such as Python's TENSORFLOW® and scikit-learn.

[0090] Based on the analysis results, the server generates an optimal learning plan and career path suggestions. This includes specific learning goals and schedules, as well as a list of possible schools. The generated suggestions are presented on the device in text and graph formats, making it easier for the user to visually grasp the information.

[0091] For example, when a user prompts, "My child's strongest subject is mathematics, and they recently scored over 90 points on a test. What kind of learning plan and materials would you suggest going forward?", the system can automatically suggest more challenging mathematics-related learning materials. This allows users to receive support tailored to their individual learning needs.

[0092] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0093] Step 1:

[0094] The user uses the terminal to input information about their child's learning (e.g., test results, club activity schedules, desired schools). The terminal converts the entered data into a specified format and sends it to the server over the network. Upon data input, the terminal generates an information packet for the server and sends the data according to the communication protocol.

[0095] Step 2:

[0096] The server receives data sent from the terminal. The learning information obtained as input data is validated before being stored in the database. The server also automatically collects local test information and statistical data from the internet, converts this external data into a format, and integrates it into the internal database. APIs and web scraping techniques are used in this process.

[0097] Step 3:

[0098] The server integrates the received and collected data and performs data cleaning. Specifically, it detects and removes outliers from the input data and standardizes the data. This ensures data quality and enables effective subsequent analysis. The output here is a cleansed, high-quality dataset.

[0099] Step 4:

[0100] The server applies machine learning algorithms to preprocessed data. Based on the dataset provided as input, it performs analysis using a generative AI model to identify children's strengths and areas for improvement. In this step, model training and evaluation are carried out using libraries such as Python's TensorFlow and scikit-learn. The output provides an evaluation of the learning outcomes and recommendations.

[0101] Step 5:

[0102] The server generates learning plans and career suggestions based on the analysis results. Specifically, it creates weekly and monthly study schedules, reinforcement plans for specific subjects, and lists of potential schools. The suggestions are presented graphically, making the information easy for users to understand visually.

[0103] Step 6:

[0104] The server sends the generated learning plan and career suggestions to the device. The device then presents the received information to the user through an interface. This is displayed in a dashboard format, with explanations using graphs and charts. Based on the presented recommendations, the user can effectively support their child's learning.

[0105] (Application Example 1)

[0106] 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."

[0107] There is a lack of means to efficiently understand children's learning progress at home, propose appropriate learning plans and career paths, and work closely with parents and educational institutions. Furthermore, busy parents often find it difficult to effectively support their children's education.

[0108] 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.

[0109] In this invention, the server includes means for acquiring information on learning status, educational organization events, and sports activities from electronic devices that input information; means for automatically collecting local examination information and statistical information; and means for applying artificial intelligence to identify problem areas using pre-processed numerical data. This makes it possible to monitor the progress of a child's education in real time via home automation devices and to automatically propose appropriate educational policies.

[0110] An "electronic device" is a device that interacts with a user by inputting and displaying information.

[0111] "Learning status" refers to information that indicates an individual's progress and achievement level in education.

[0112] An "educational organization" is an institution or group that provides education to learners.

[0113] An "event" refers to an event or activity planned in connection with an educational organization or learning.

[0114] "Physical activity" refers to physical or recreational activities that involve using the body.

[0115] "Numerical data" refers to data that is a numerical representation of input or collected information and is processed in digital format.

[0116] "Household automation equipment" refers to robots and devices designed for use in a home environment to support daily life.

[0117] Artificial intelligence is a technology that analyzes large amounts of data and has the ability to mimic human intellectual activity.

[0118] An "educational policy" is a plan or guideline aimed at improving learners' knowledge and skills.

[0119] To implement this invention, a system is constructed in which home automation devices and a server work together to actively collect and analyze learner information. The server first acquires information from users via electronic devices regarding their learning status, educational organization events, and physical activities. The server also automatically collects local examination information and statistical information via the internet. This results in the aggregation of comprehensive and multifaceted data.

[0120] After data collection, the server performs standardization and normalization to improve data accuracy. This includes removing outliers, enabling reliable analysis. Once data preprocessing is complete, the server uses artificial intelligence to identify learners' strengths and areas for improvement. Machine learning algorithms are used to analyze trends not only for individual learners but also for the entire region.

[0121] Home automation devices visually present educational policies and plans sent from a server to the user. This helps support children's learning when parents are busy. For example, a child who scores highly on a math test can be presented with more challenging problems, while also being given suggestions for study time that fits their physical activity schedule. These suggestions are visualized in graphs and charts and designed to be intuitively understandable.

[0122] An example of a prompt message would be, "Based on the child's recent test results, please suggest the next learning plan. Test scores: 90, 85, 88. Participation activities: Basketball, Music." Information reflecting the learner's current state is presented and suggested. Based on this information, the server constructs an optimal educational plan and delivers it to parents and learners through home automation devices.

[0123] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0124] Step 1:

[0125] The server obtains information from users via their terminals regarding their learning progress, educational organization events, and physical activities. In this process, users input this information using their terminals, and it is sent to the server. The input data is received as text and numerical data and stored on the server.

[0126] Step 2:

[0127] The server automatically collects local exam information and statistics via an internet connection. At this stage, it uses APIs and web crawling technologies to retrieve information from external sources. The retrieved data is temporarily stored in a database on the server. Input is information from external data sources, and output is an update to the server's database.

[0128] Step 3:

[0129] The server integrates input data from terminals with data collected from external sources. This generates a dataset that combines individual learner information with regional education trends. The input is data from existing databases, and the output is the integrated dataset.

[0130] Step 4:

[0131] The server performs data cleaning and normalization on the integrated dataset. It detects and removes outliers and converts the data to a standard format. This ensures data integrity and improves the accuracy of the analysis. The input is the integrated dataset, and the output is clean, normalized data.

[0132] Step 5:

[0133] The server starts analysis using an artificial intelligence model with clean data. It uses machine learning algorithms to detect the learner's strengths and areas for improvement. The input is normalized data, and the output is text and numerical data as analysis results. Specifically, it performs data analysis using a generative AI model.

[0134] Step 6:

[0135] The server generates learning plans and career path suggestions based on the analysis results. This includes weekly study schedules and lists of possible schools. The input is the analysis results, and the output is the data for the proposed educational plan.

[0136] Step 7:

[0137] The server sends the generated learning plan to the home automation device, which then presents it to the user visually or audibly. The home automation device displays the information using graphs and charts, providing it in an easily understandable format for the user. The input is the learning plan, and the output is the information display via the user interface. The suggested content is adjusted based on example prompts.

[0138] 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.

[0139] The present invention aims to recognize a user's emotions and dynamically optimize learning plans and career path suggestions according to their emotional state by incorporating an emotion engine into a learning support system. The following describes embodiments for carrying out this invention.

[0140] Explanation of program processing in natural language

[0141] Data collection

[0142] Users input data on their child's learning progress, educational events, and physical activities into the device. Simultaneously, wearable devices and biosensors can be used to collect data related to the child's emotions and stress levels. This information is then transmitted from the device to a server.

[0143] Emotion recognition and data integration

[0144] The server integrates learning and emotion data obtained from the terminal or external devices. At this stage, it utilizes an emotion engine to analyze the user's emotional state (e.g., stress level, motivation). The server combines the analysis results with other learning-related data and stores them in a database.

[0145] Data Analysis

[0146] Based on the integrated data, the server uses artificial intelligence to identify learning progress, areas of difficulty, and emotional support needs. Based on emotional data, it generates parameters to adjust the learning load and builds an optimized learning plan for the user.

[0147] Generating learning plans and career path suggestions

[0148] The server provides users with learning schedules and career path options that take their emotional state into account, based on results generated by machine learning algorithms. This includes inserting breaks according to their emotions and easing the learning plan to reduce stress.

[0149] Presentation to the user

[0150] The generated learning plan and career suggestions are sent to the device and displayed visually to the user. Furthermore, feedback based on emotional data is provided to the user, allowing them to progress through their learning while understanding their own emotional state.

[0151] Specific example

[0152] For example, if the emotion engine detects, based on information entered by the user on the device, that a child has recently scored low on a test and is experiencing high stress levels, the server will suggest a learning plan that prioritizes reviewing the basics. It will also suggest setting short-term goals and incorporating short breaks into the learning schedule to reduce stress. These adjustments aim to improve the child's learning efficiency while supporting their emotional well-being.

[0153] This system aims to provide comprehensive learning support, not only by improving academic performance but also by emphasizing children's mental health.

[0154] The following describes the processing flow.

[0155] Step 1:

[0156] The user inputs information into the device, such as the child's learning progress, academic performance, educational institution event schedules, and details of their physical activities. In addition, the device prepares to send emotional data acquired from wearable devices and biosensors to the server.

[0157] Step 2:

[0158] The terminal sends all the information it has collected to the server. Upon receiving this data, the server simultaneously collects local exam information and related statistical data via the internet.

[0159] Step 3:

[0160] The server integrates the received training data and sentiment data. In this process, preprocessing is performed by standardizing the data format, detecting and removing outliers, and supplementing the data as needed.

[0161] Step 4:

[0162] The server uses an emotion engine to analyze emotional data and identify the child's current emotional state, such as stress levels and motivation levels. This analysis is then considered along with other training data to prepare for further analysis.

[0163] Step 5:

[0164] The server uses an artificial intelligence model to analyze integrated data, identifying children's strengths and areas for improvement in their learning, as well as determining where emotionally-based support is needed.

[0165] Step 6:

[0166] Based on the analysis results, the server generates learning plans and career recommendations optimized for the user's emotional state. This includes creating dynamic plans that take emotions into account, such as adjusting the learning load and inserting breaks.

[0167] Step 7:

[0168] A learning plan and career suggestions generated from the server are sent to the device, which then visually displays them to the user. The displayed content includes specific advice and points of caution based on sentiment data.

[0169] Step 8:

[0170] Users review the information provided through their device and, if necessary, adjust their own or their child's approach to learning based on the feedback provided by the emotion engine.

[0171] The above is an embodiment of a learning support system incorporating an emotional engine. The system comprehensively understands not only a child's learning but also their mental health status, and provides appropriate guidance.

[0172] (Example 2)

[0173] 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 will be referred to as the "terminal."

[0174] Conventional educational support systems have faced challenges in adequately considering emotional data when assessing learning progress and providing career guidance, making it difficult to create learning plans that address each user's individual emotional state. Furthermore, there is a need to efficiently utilize emotion recognition technology to provide optimized suggestions tailored to each user.

[0175] 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.

[0176] In this invention, the server includes means for acquiring data such as learning status from an information input device, means for analyzing and integrating emotional data acquired by a biometric information collection device, and means for analyzing the integrated data with artificial intelligence to identify the user's problem areas. This enables the generation of learning plans and career path suggestions that take into account the user's emotional state, making more personalized educational support possible.

[0177] A "device for inputting information" is a device used by users to input their learning progress and related information, and is usually a computer, tablet, or smartphone.

[0178] A "biometric information collection device" is a device used to acquire data on a user's emotional state and stress level, and generally refers to wearable devices or sensors.

[0179] "Emotion recognition technology" is a technology that analyzes acquired emotional data to identify the user's emotional state.

[0180] "Artificial intelligence" is a technology in which computer systems imitate human intelligence to learn, reason, and adapt, and in this case, it is used for data analysis and proposal generation.

[0181] "Integrated data" refers to data collected from different sources, unified, and formatted for analysis.

[0182] A "learning plan" refers to a specific schedule and content structure designed to help a user achieve their learning goals; it is a personalized plan.

[0183] "Career guidance" refers to suggestions that advise users on their future learning and career paths based on their learning data and emotional state.

[0184] Modes for carrying out the invention

[0185] This invention is a system that provides optimal learning plans and career guidance tailored to the user's emotional state by incorporating emotional elements into learning support. The following hardware and software are used to implement the system.

[0186] Hardware and software configuration

[0187] The terminal is used as a device for users to input learning progress and related information, and specifically includes computers, tablets, and smartphones. The terminal connects to wearable devices worn by the user and collects emotion-related data such as heart rate, body temperature, and facial expression data through biosensors.

[0188] The server receives this data and uses an emotion recognition engine to analyze the user's emotional state. The emotion recognition engine quantifies stress levels and motivation based on the input biometric data, and an AI model takes in and analyzes this data. Based on the identified analysis results, the AI ​​model generates a learning plan tailored to the user.

[0189] The generated learning plan is sent from the server to the device and presented to the user through a visual interface. The user receives daily learning progress and feedback based on their emotional state through the device, allowing them to learn at their own pace and according to their own feelings.

[0190] Specific examples

[0191] For example, if a user using a wearable device shows a high stress level after scoring low on a recent academic test, the system will automatically suggest a study plan focused on reviewing the basics. It can also suggest ways to reduce stress by setting short-term goals and incorporating short breaks into the schedule.

[0192] An example of a prompt message that can be input to the generating AI model is, "Create an optimal learning plan based on the user's emotional data and output suggestions for stress reduction and improved learning efficiency." This allows the system to assist the user in learning in the most efficient way possible.

[0193] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0194] Step 1:

[0195] Users input learning status and education-related information through the device. This input includes data on learning progress, schedules, and physical activity. Furthermore, the device collects biometric data such as heart rate, body temperature, and facial expression analysis from wearable devices. This data is used as input for analyzing emotions.

[0196] Step 2:

[0197] The device transmits collected learning information and biometric data to the server. During this transmission, a secure data transmission protocol is used to protect the data. The server first integrates the received data and stores it in a database. Simultaneously, it inputs the biometric data into an emotion recognition engine to analyze emotional state and stress levels.

[0198] Step 3:

[0199] The server inputs the analyzed emotional state and training data into a generating AI model to create a learning plan optimized for the user's learning needs. Here, the learning load is adjusted based on the emotional data, and the plan is refined to provide the necessary support. The generated plan may also include praise and break suggestions to boost the user's motivation.

[0200] Step 4:

[0201] The server sends the generated learning plan and career path suggestions to the terminal. The terminal displays this information in its user interface, providing the user with visual feedback. Based on the visualized emotional state-based feedback, the user can adjust their learning and progress more efficiently.

[0202] Step 5:

[0203] After receiving feedback via their device, users carry out daily learning activities based on their learning plan. When new data is collected during these activities, the process restarts from step 1, ensuring continuous learning optimization. This enables dynamic learning support tailored to individual needs.

[0204] (Application Example 2)

[0205] 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".

[0206] Existing learning support systems suggest learning plans and career paths based on the user's learning status and progress, but they do not take into account the user's emotional state, making it difficult to optimize learning efficiency and mental health. Furthermore, there is a lack of means to mitigate the impact of stress and decreased motivation on academic performance.

[0207] 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.

[0208] In this invention, the server includes means for acquiring learning status, educational facility activities, and exercise information from an information input device; means for automatically collecting regional test information and statistical information; and means for integrating and pre-processing the acquired and collected data. This makes it possible to analyze emotional states and optimize learning plans and career guidance suggestions based on them.

[0209] A "device for inputting information" is an electronic device used to receive learning progress and activity information from the user.

[0210] "Learning status" refers to information that shows the user's learning progress, performance, and level of understanding.

[0211] "Educational facility activities" refers to information such as events and curricula held at schools and other educational institutions.

[0212] "Exercise information" refers to data related to physical activity and sports.

[0213] "Local examination information" refers to all information regarding examinations and tests within a given region.

[0214] "Statistical information" refers to information that includes analysis results based on data and numerical values.

[0215] "Methods for integrating and pre-processing data" refers to the process of centralizing various acquired data and preparing them for analysis.

[0216] "Applying machine learning to identify problem areas" refers to methods of clarifying user learning and emotional issues using AI technology.

[0217] "Means for generating learning plans and career guidance" refers to methods for creating plans that indicate the next steps in a user's learning process.

[0218] "A means of analyzing emotional states and reflecting that information in learning plans and career guidance" refers to a method of evaluating the user's psychological state and making optimal suggestions accordingly.

[0219] "Means for transmitting and presenting generated suggestions to the device" refers to the communication and display process for showing the created learning plans and suggestions to the user.

[0220] The system for implementing this invention has the function of automatically collecting local test information and statistical information based on learning status, educational facility activities, and exercise information obtained from an information input device, and analyzing emotional states. The server integrates the acquired data and performs data preprocessing to generate optimal learning plans and career path suggestions for the user.

[0221] The system's hardware includes a device equipped with a camera sensor and microphone, as well as a server, through which data is collected. The server uses emotion analysis software to assess emotional states. Specifically, it leverages technologies such as Azure® Emotion API to determine the user's stress and motivation levels based on the analyzed data. It also utilizes machine learning frameworks such as TensorFlow to generate learning plans.

[0222] As a concrete example of this system, consider a situation where a user's child is studying for a school exam. The system collects the child's facial expressions and voice quality through a data input device. If the emotion engine detects a high stress level, the server provides a learning plan that emphasizes reviewing fundamentals, sets short-term goals, and suggests breaks. This allows the child to learn efficiently while maintaining their mental health.

[0223] An example of a prompt related to a generative AI model would be: "Generate a Python script that analyzes a child's emotional state based on facial expression and voice data and creates optimal learning advice." In this way, the program provides support to maximize the user's learning efficiency and psychological well-being.

[0224] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0225] Step 1:

[0226] The device uses its camera and microphone to collect user facial expression and voice data. This data serves as input and is temporarily stored on the device.

[0227] Step 2:

[0228] The device transmits collected facial expression and voice data to the server. Simultaneously, it also collects and transfers data such as learning status, educational facility activities, and local exam information to the server.

[0229] Step 3:

[0230] The server integrates all received data, removes outliers, and normalizes the data. This generates a clean dataset, which serves as the foundational data for AI analysis.

[0231] Step 4:

[0232] The server uses the Azure Emotion API to analyze the user's emotional state from the received facial and audio data. This allows it to extract emotional information such as stress levels and motivation levels.

[0233] Step 5:

[0234] The server uses a machine learning algorithm based on TensorFlow to identify the user's learning progress and areas of difficulty from the integrated data. This analysis then sets specific goals and challenges for building a learning plan.

[0235] Step 6:

[0236] The server integrates extracted emotional information with learning analysis results to generate a learning plan and career path suggestions tailored to the user. These suggestions are custom plans adjusted according to the user's emotional state.

[0237] Step 7:

[0238] The server sends the generated learning plan and career path suggestions to the terminal, which then visually displays the suggestions to the user. This allows the user to review the suggestions.

[0239] Step 8:

[0240] Users review the information displayed on their devices and provide feedback, such as modifying their plans as needed. This information is then sent back to the server and used for further data analysis.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] [Second Embodiment]

[0245] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0246] 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.

[0247] 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).

[0248] 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.

[0249] 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.

[0250] 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).

[0251] 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.

[0252] 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.

[0253] 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.

[0254] 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.

[0255] 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.

[0256] 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".

[0257] This invention is a system that collects detailed information such as a child's learning progress, educational institution events, and sports activities from a user-inputted terminal, and uses this information to support the child's learning. This system is realized through the cooperation of both the terminal and the server to process information in real time.

[0258] Explanation of program processing in natural language

[0259] Data collection

[0260] Users input information about their children's test results, extracurricular activity schedules, and desired schools through their devices. The devices then send this information to a server. The server further collects local exam information and statistical data from the internet.

[0261] Data Integration and Preprocessing

[0262] The server integrates data received from users and data acquired from external sources. It improves data quality by standardizing data formats and removing outliers.

[0263] Data Analysis

[0264] The server applies an artificial intelligence model to the integrated data and performs analysis. This identifies children's strengths, areas for improvement, and regional testing trends. The AI ​​model performs these analyses using machine learning algorithms.

[0265] Generating learning plans and career path suggestions

[0266] Based on the analysis results, the server generates optimal learning plans and career path suggestions for the user. These suggestions include weekly and monthly schedules, specific learning goals, and a list of potential schools.

[0267] Presentation to the user

[0268] Plans and proposals generated from the server are sent to the terminal, which then presents them to the user in a visualized format. This can be presented not only as text but also visually, such as graphs and charts.

[0269] Specific example

[0270] For example, if the information entered by the user on the device includes that the child achieved a high score on a math test, the server will suggest a set of math-related problems of a relatively high difficulty level. Furthermore, based on information about extracurricular activities, the system can automatically create a schedule that ensures efficient study time according to the child's activity schedule.

[0271] This system aims to improve the quality of education by effectively supporting children's learning even when users are busy, and by providing information that can be used as a reference for career choices.

[0272] The following describes the processing flow.

[0273] Step 1:

[0274] Users use their devices to input information such as their child's learning progress, grades, and extracurricular activity schedules. This information is temporarily stored on the device.

[0275] Step 2:

[0276] The terminal sends data entered by the user to the server. During transmission, the data integrity and format are checked, and the data format is converted as needed.

[0277] Step 3:

[0278] The server stores the received data in a database and simultaneously collects local exam information and related statistical data from the internet. At this stage, APIs are used to retrieve the data.

[0279] Step 4:

[0280] The server integrates and preprocesses the collected data. Specifically, it imputes missing values, detects and removes outliers, and generates a clean dataset suitable for analysis.

[0281] Step 5:

[0282] The server executes a machine learning algorithm using the preprocessed data. Here, it analyzes the fields where a child is good or not good, and extracts trends in the local exam trends.

[0283] Step 6:

[0284] Based on the results of the machine learning, the server generates a learning plan and a course recommendation. This includes allocating the learning time for each subject and listing the recommended schools to enter.

[0285] Step 7:

[0286] The server sends the proposal generated from the server to the terminal. The proposal is formatted in a visually easy-to-understand form and presented to the user using charts and graphics.

[0287] Step 8:

[0288] The terminal receives the information from the server and notifies the user. The user can check the detailed learning plan and course recommendation on the terminal.

[0289] (Example 1)

[0290] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0291] In the conventional education support system, it was difficult to provide advice and plans individually adapted to the learning situation and course selection of children. Therefore, despite the existence of diverse learning environments and course information, the optimal information was not provided to each individual child, and there was a concern that the quality of education would decline. In addition, there was also a lack of a mechanism to collect and utilize local exam information in a timely manner, making it difficult to support efficient learning.

[0292] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following respective means.

[0293] In this invention, the server includes means for automatically collecting regional test information and statistical information through a wide-area information source; means for integrating the acquired data and performing preprocessing, including standardization and outlier removal; and means for applying machine learning algorithms to identify learning areas using the preprocessed data. This makes it possible to provide individualized learning plans and career suggestions in real time, thereby improving the quality of education.

[0294] A "terminal" is a device used for inputting and displaying information, and it provides an interface with the user.

[0295] "Wide-area information sources" refer to information sources that exist on the internet and are used to obtain external data related to education.

[0296] "Local examination information" refers to information about examinations conducted by local educational institutions and within local communities, including the content and schedule of the examinations that students take.

[0297] "Statistical information" refers to a dataset that analyzes education-related data and provides indicators such as trends and averages.

[0298] Standardization is the process of transforming data according to certain standards to make it easier to process and analyze.

[0299] "Outlier removal" refers to the operation of eliminating outliers and noise present in a dataset to improve data quality.

[0300] A "machine learning algorithm" is a computational method that uses data to train a model and perform predictions or classifications for specific tasks.

[0301] An "artificial intelligence model" is a computer program designed to automatically perform analysis and make decisions based on input data.

[0302] The "learning plan" refers to a series of activities and schedules formulated by students to achieve specific learning goals.

[0303] The "course recommendation" is information that provides recommendations and guidelines regarding students' learning and future career choices.

[0304] This invention is an educational information processing system aimed at assisting children's learning, which is realized through the cooperation of a terminal for users to input information and a server for processing data and generating proposals. The terminal can use personal computers, smartphones, tablets, etc., and provides an intuitive interface for use.

[0305] Users input information such as learning status, school operation information, and club activity schedules into the terminal. This information is transmitted to the server via the Internet. The server is equipped with software for automatically collecting regional examination information and statistical information. In this system, specific information collection APIs and web scraping technologies are utilized to efficiently obtain necessary data from a wide range of educational information sources.

[0306] Next, the server integrates the aforementioned data and performs standardization and outlier removal to improve data quality. Database systems such as PostgreSQL and MySQL are suitable. Based on this preprocessed data, the server applies machine learning algorithms to identify the areas where children are good at learning and the areas that need improvement. As the AI models used here, libraries such as Python's TensorFlow and scikit-learn are utilized.

[0307] Based on the analysis results, the server generates an optimal learning plan and course recommendation. This includes specific learning goals, schedules, and a list of schools that can be entered. The generated proposals are presented to the terminal in text or graph format. This makes it easier for users to visually grasp the information.

[0308] For example, when a user prompts, "My child's strongest subject is mathematics, and they recently scored over 90 points on a test. What kind of learning plan and materials would you suggest going forward?", the system can automatically suggest more challenging mathematics-related learning materials. This allows users to receive support tailored to their individual learning needs.

[0309] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0310] Step 1:

[0311] The user uses the terminal to input information about their child's learning (e.g., test results, club activity schedules, desired schools). The terminal converts the entered data into a specified format and sends it to the server over the network. Upon data input, the terminal generates an information packet for the server and sends the data according to the communication protocol.

[0312] Step 2:

[0313] The server receives data sent from the terminal. The learning information obtained as input data is validated before being stored in the database. The server also automatically collects local test information and statistical data from the internet, converts this external data into a format, and integrates it into the internal database. APIs and web scraping techniques are used in this process.

[0314] Step 3:

[0315] The server integrates the received and collected data and performs data cleaning. Specifically, it detects and removes outliers from the input data and standardizes the data. This ensures data quality and enables effective subsequent analysis. The output here is a cleansed, high-quality dataset.

[0316] Step 4:

[0317] The server applies machine learning algorithms to preprocessed data. Based on the dataset provided as input, it performs analysis using a generative AI model to identify children's strengths and areas for improvement. In this step, model training and evaluation are carried out using libraries such as Python's TensorFlow and scikit-learn. The output provides an evaluation of the learning outcomes and recommendations.

[0318] Step 5:

[0319] The server generates learning plans and career suggestions based on the analysis results. Specifically, it creates weekly and monthly study schedules, reinforcement plans for specific subjects, and lists of potential schools. The suggestions are presented graphically, making the information easy for users to understand visually.

[0320] Step 6:

[0321] The server sends the generated learning plan and career suggestions to the device. The device then presents the received information to the user through an interface. This is displayed in a dashboard format, with explanations using graphs and charts. Based on the presented recommendations, the user can effectively support their child's learning.

[0322] (Application Example 1)

[0323] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0324] There is a lack of means to efficiently understand children's learning progress at home, propose appropriate learning plans and career paths, and work closely with parents and educational institutions. Furthermore, busy parents often find it difficult to effectively support their children's education.

[0325] 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.

[0326] In this invention, the server includes means for acquiring information on learning status, educational organization events, and sports activities from electronic devices that input information; means for automatically collecting local examination information and statistical information; and means for applying artificial intelligence to identify problem areas using pre-processed numerical data. This makes it possible to monitor the progress of a child's education in real time via home automation devices and to automatically propose appropriate educational policies.

[0327] An "electronic device" is a device that interacts with a user by inputting and displaying information.

[0328] "Learning status" refers to information that indicates an individual's progress and achievement level in education.

[0329] An "educational organization" is an institution or group that provides education to learners.

[0330] An "event" refers to an event or activity planned in connection with an educational organization or learning.

[0331] "Physical activity" refers to physical or recreational activities that involve using the body.

[0332] "Numerical data" refers to data that is a numerical representation of input or collected information and is processed in digital format.

[0333] "Household automation equipment" refers to robots and devices designed for use in a home environment to support daily life.

[0334] Artificial intelligence is a technology that analyzes large amounts of data and has the ability to mimic human intellectual activity.

[0335] An "educational policy" is a plan or guideline aimed at improving learners' knowledge and skills.

[0336] To implement this invention, a system is constructed in which home automation devices and a server work together to actively collect and analyze learner information. The server first acquires information from users via electronic devices regarding their learning status, educational organization events, and physical activities. The server also automatically collects local examination information and statistical information via the internet. This results in the aggregation of comprehensive and multifaceted data.

[0337] After data collection, the server performs standardization and normalization to improve data accuracy. This includes removing outliers, enabling reliable analysis. Once data preprocessing is complete, the server uses artificial intelligence to identify learners' strengths and areas for improvement. Machine learning algorithms are used to analyze trends not only for individual learners but also for the entire region.

[0338] Home automation devices visually present educational policies and plans sent from a server to the user. This helps support children's learning when parents are busy. For example, a child who scores highly on a math test can be presented with more challenging problems, while also being given suggestions for study time that fits their physical activity schedule. These suggestions are visualized in graphs and charts and designed to be intuitively understandable.

[0339] An example of a prompt message would be, "Based on the child's recent test results, please suggest the next learning plan. Test scores: 90, 85, 88. Participation activities: Basketball, Music." Information reflecting the learner's current state is presented and suggested. Based on this information, the server constructs an optimal educational plan and delivers it to parents and learners through home automation devices.

[0340] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0341] Step 1:

[0342] The server obtains information from users via their terminals regarding their learning progress, educational organization events, and physical activities. In this process, users input this information using their terminals, and it is sent to the server. The input data is received as text and numerical data and stored on the server.

[0343] Step 2:

[0344] The server automatically collects local exam information and statistics via an internet connection. At this stage, it uses APIs and web crawling technologies to retrieve information from external sources. The retrieved data is temporarily stored in a database on the server. Input is information from external data sources, and output is an update to the server's database.

[0345] Step 3:

[0346] The server integrates input data from terminals with data collected from external sources. This generates a dataset that combines individual learner information with regional education trends. The input is data from existing databases, and the output is the integrated dataset.

[0347] Step 4:

[0348] The server performs data cleaning and normalization on the integrated dataset. It detects and removes outliers and converts the data to a standard format. This ensures data integrity and improves the accuracy of the analysis. The input is the integrated dataset, and the output is clean, normalized data.

[0349] Step 5:

[0350] The server starts analysis using an artificial intelligence model with clean data. It uses machine learning algorithms to detect the learner's strengths and areas for improvement. The input is normalized data, and the output is text and numerical data as analysis results. Specifically, it performs data analysis using a generative AI model.

[0351] Step 6:

[0352] The server generates learning plans and career path suggestions based on the analysis results. This includes weekly study schedules and lists of possible schools. The input is the analysis results, and the output is the data for the proposed educational plan.

[0353] Step 7:

[0354] The server sends the generated learning plan to the home automation device, which then presents it to the user visually or audibly. The home automation device displays the information using graphs and charts, providing it in an easily understandable format for the user. The input is the learning plan, and the output is the information display via the user interface. The suggested content is adjusted based on example prompts.

[0355] 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.

[0356] The present invention aims to recognize a user's emotions and dynamically optimize learning plans and career path suggestions according to their emotional state by incorporating an emotion engine into a learning support system. The following describes embodiments for carrying out this invention.

[0357] Explanation of program processing in natural language

[0358] Data collection

[0359] Users input data on their child's learning progress, educational events, and physical activities into the device. Simultaneously, wearable devices and biosensors can be used to collect data related to the child's emotions and stress levels. This information is then transmitted from the device to a server.

[0360] Emotion recognition and data integration

[0361] The server integrates learning and emotion data obtained from the terminal or external devices. At this stage, it utilizes an emotion engine to analyze the user's emotional state (e.g., stress level, motivation). The server combines the analysis results with other learning-related data and stores them in a database.

[0362] Data Analysis

[0363] Based on the integrated data, the server uses artificial intelligence to identify learning progress, areas of difficulty, and emotional support needs. Based on emotional data, it generates parameters to adjust the learning load and builds an optimized learning plan for the user.

[0364] Generating learning plans and career path suggestions

[0365] The server provides users with learning schedules and career path options that take their emotional state into account, based on results generated by machine learning algorithms. This includes inserting breaks according to their emotions and easing the learning plan to reduce stress.

[0366] Presentation to the user

[0367] The generated learning plan and career suggestions are sent to the device and displayed visually to the user. Furthermore, feedback based on emotional data is provided to the user, allowing them to progress through their learning while understanding their own emotional state.

[0368] Specific example

[0369] For example, if the emotion engine detects, based on information entered by the user on the device, that a child has recently scored low on a test and is experiencing high stress levels, the server will suggest a learning plan that prioritizes reviewing the basics. It will also suggest setting short-term goals and incorporating short breaks into the learning schedule to reduce stress. These adjustments aim to improve the child's learning efficiency while supporting their emotional well-being.

[0370] This system aims to provide comprehensive learning support, not only by improving academic performance but also by emphasizing children's mental health.

[0371] The following describes the processing flow.

[0372] Step 1:

[0373] The user inputs information into the device, such as the child's learning progress, academic performance, educational institution event schedules, and details of their physical activities. In addition, the device prepares to send emotional data acquired from wearable devices and biosensors to the server.

[0374] Step 2:

[0375] The terminal sends all the information it has collected to the server. Upon receiving this data, the server simultaneously collects local exam information and related statistical data via the internet.

[0376] Step 3:

[0377] The server integrates the received training data and sentiment data. In this process, preprocessing is performed by standardizing the data format, detecting and removing outliers, and supplementing the data as needed.

[0378] Step 4:

[0379] The server uses an emotion engine to analyze emotional data and identify the child's current emotional state, such as stress levels and motivation levels. This analysis is then considered along with other training data to prepare for further analysis.

[0380] Step 5:

[0381] The server uses an artificial intelligence model to analyze integrated data, identifying children's strengths and areas for improvement in their learning, as well as determining where emotionally-based support is needed.

[0382] Step 6:

[0383] Based on the analysis results, the server generates learning plans and career recommendations optimized for the user's emotional state. This includes creating dynamic plans that take emotions into account, such as adjusting the learning load and inserting breaks.

[0384] Step 7:

[0385] A learning plan and career suggestions generated from the server are sent to the device, which then visually displays them to the user. The displayed content includes specific advice and points of caution based on sentiment data.

[0386] Step 8:

[0387] Users review the information provided through their device and, if necessary, adjust their own or their child's approach to learning based on the feedback provided by the emotion engine.

[0388] The above is an embodiment of a learning support system incorporating an emotional engine. The system comprehensively understands not only a child's learning but also their mental health status, and provides appropriate guidance.

[0389] (Example 2)

[0390] 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".

[0391] Conventional educational support systems have faced challenges in adequately considering emotional data when assessing learning progress and providing career guidance, making it difficult to create learning plans that address each user's individual emotional state. Furthermore, there is a need to efficiently utilize emotion recognition technology to provide optimized suggestions tailored to each user.

[0392] 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.

[0393] In this invention, the server includes means for acquiring data such as learning status from an information input device, means for analyzing and integrating emotional data acquired by a biometric information collection device, and means for analyzing the integrated data with artificial intelligence to identify the user's problem areas. This enables the generation of learning plans and career path suggestions that take into account the user's emotional state, making more personalized educational support possible.

[0394] A "device for inputting information" is a device used by users to input their learning progress and related information, and is usually a computer, tablet, or smartphone.

[0395] A "biometric information collection device" is a device used to acquire data on a user's emotional state and stress level, and generally refers to wearable devices or sensors.

[0396] "Emotion recognition technology" is a technology that analyzes acquired emotional data to identify the user's emotional state.

[0397] "Artificial intelligence" is a technology in which computer systems imitate human intelligence to learn, reason, and adapt, and in this case, it is used for data analysis and proposal generation.

[0398] "Integrated data" refers to data collected from different sources, unified, and formatted for analysis.

[0399] A "learning plan" refers to a specific schedule and content structure designed to help a user achieve their learning goals; it is a personalized plan.

[0400] "Career guidance" refers to suggestions that advise users on their future learning and career paths based on their learning data and emotional state.

[0401] Modes for carrying out the invention

[0402] This invention is a system that provides optimal learning plans and career guidance tailored to the user's emotional state by incorporating emotional elements into learning support. The following hardware and software are used to implement the system.

[0403] Hardware and software configuration

[0404] The terminal is used as a device for users to input learning progress and related information, and specifically includes computers, tablets, and smartphones. The terminal connects to wearable devices worn by the user and collects emotion-related data such as heart rate, body temperature, and facial expression data through biosensors.

[0405] The server receives this data and uses an emotion recognition engine to analyze the user's emotional state. The emotion recognition engine quantifies stress levels and motivation based on the input biometric data, and an AI model takes in and analyzes this data. Based on the identified analysis results, the AI ​​model generates a learning plan tailored to the user.

[0406] The generated learning plan is sent from the server to the device and presented to the user through a visual interface. The user receives daily learning progress and feedback based on their emotional state through the device, allowing them to learn at their own pace and according to their own feelings.

[0407] Specific examples

[0408] For example, if a user using a wearable device shows a high stress level after scoring low on a recent academic test, the system will automatically suggest a study plan focused on reviewing the basics. It can also suggest ways to reduce stress by setting short-term goals and incorporating short breaks into the schedule.

[0409] An example of a prompt message that can be input to the generating AI model is, "Create an optimal learning plan based on the user's emotional data and output suggestions for stress reduction and improved learning efficiency." This allows the system to assist the user in learning in the most efficient way possible.

[0410] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0411] Step 1:

[0412] Users input learning status and education-related information through the device. This input includes data on learning progress, schedules, and physical activity. Furthermore, the device collects biometric data such as heart rate, body temperature, and facial expression analysis from wearable devices. This data is used as input for analyzing emotions.

[0413] Step 2:

[0414] The device transmits collected learning information and biometric data to the server. During this transmission, a secure data transmission protocol is used to protect the data. The server first integrates the received data and stores it in a database. Simultaneously, it inputs the biometric data into an emotion recognition engine to analyze emotional state and stress levels.

[0415] Step 3:

[0416] The server inputs the analyzed emotional state and training data into a generating AI model to create a learning plan optimized for the user's learning needs. Here, the learning load is adjusted based on the emotional data, and the plan is refined to provide the necessary support. The generated plan may also include praise and break suggestions to boost the user's motivation.

[0417] Step 4:

[0418] The server sends the generated learning plan and career path suggestions to the terminal. The terminal displays this information in its user interface, providing the user with visual feedback. Based on the visualized emotional state-based feedback, the user can adjust their learning and progress more efficiently.

[0419] Step 5:

[0420] After receiving feedback via their device, users carry out daily learning activities based on their learning plan. When new data is collected during these activities, the process restarts from step 1, ensuring continuous learning optimization. This enables dynamic learning support tailored to individual needs.

[0421] (Application Example 2)

[0422] 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 as the "terminal".

[0423] Existing learning support systems suggest learning plans and career paths based on the user's learning status and progress, but they do not take into account the user's emotional state, making it difficult to optimize learning efficiency and mental health. Furthermore, there is a lack of means to mitigate the impact of stress and decreased motivation on academic performance.

[0424] 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.

[0425] In this invention, the server includes means for acquiring learning status, educational facility activities, and exercise information from an information input device; means for automatically collecting regional test information and statistical information; and means for integrating and pre-processing the acquired and collected data. This makes it possible to analyze emotional states and optimize learning plans and career guidance suggestions based on them.

[0426] A "device for inputting information" is an electronic device used to receive learning progress and activity information from the user.

[0427] "Learning status" refers to information that shows the user's learning progress, performance, and level of understanding.

[0428] "Educational facility activities" refers to information such as events and curricula held at schools and other educational institutions.

[0429] "Exercise information" refers to data related to physical activity and sports.

[0430] "Local examination information" refers to all information regarding examinations and tests within a given region.

[0431] "Statistical information" refers to information that includes analysis results based on data and numerical values.

[0432] "Methods for integrating and pre-processing data" refers to the process of centralizing various acquired data and preparing them for analysis.

[0433] "Applying machine learning to identify problem areas" refers to methods of clarifying user learning and emotional issues using AI technology.

[0434] "Means for generating learning plans and career guidance" refers to methods for creating plans that indicate the next steps in a user's learning process.

[0435] "A means of analyzing emotional states and reflecting that information in learning plans and career guidance" refers to a method of evaluating the user's psychological state and making optimal suggestions accordingly.

[0436] "Means for transmitting and presenting generated suggestions to the device" refers to the communication and display process for showing the created learning plans and suggestions to the user.

[0437] The system for implementing this invention has the function of automatically collecting local test information and statistical information based on learning status, educational facility activities, and exercise information obtained from an information input device, and analyzing emotional states. The server integrates the acquired data and performs data preprocessing to generate optimal learning plans and career path suggestions for the user.

[0438] The system's hardware includes a device equipped with a camera sensor and microphone, as well as a server, through which data is collected. The server uses sentiment analysis software to assess emotional states. Specifically, it leverages technologies such as the Azure Emotion API to determine the user's stress and motivation levels based on the analyzed data. It also utilizes machine learning frameworks such as TensorFlow to generate learning plans.

[0439] As a concrete example of this system, consider a situation where a user's child is studying for a school exam. The system collects the child's facial expressions and voice quality through a data input device. If the emotion engine detects a high stress level, the server provides a learning plan that emphasizes reviewing fundamentals, sets short-term goals, and suggests breaks. This allows the child to learn efficiently while maintaining their mental health.

[0440] An example of a prompt related to a generative AI model would be: "Generate a Python script that analyzes a child's emotional state based on facial expression and voice data and creates optimal learning advice." In this way, the program provides support to maximize the user's learning efficiency and psychological well-being.

[0441] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0442] Step 1:

[0443] The device uses its camera and microphone to collect user facial expression and voice data. This data serves as input and is temporarily stored on the device.

[0444] Step 2:

[0445] The device transmits collected facial expression and voice data to the server. Simultaneously, it also collects and transfers data such as learning status, educational facility activities, and local exam information to the server.

[0446] Step 3:

[0447] The server integrates all received data, removes outliers, and normalizes the data. This generates a clean dataset, which serves as the foundational data for AI analysis.

[0448] Step 4:

[0449] The server uses the Azure Emotion API to analyze the user's emotional state from the received facial and audio data. This allows it to extract emotional information such as stress levels and motivation levels.

[0450] Step 5:

[0451] The server uses a machine learning algorithm based on TensorFlow to identify the user's learning progress and areas of difficulty from the integrated data. This analysis then sets specific goals and challenges for building a learning plan.

[0452] Step 6:

[0453] The server integrates extracted emotional information with learning analysis results to generate a learning plan and career path suggestions tailored to the user. These suggestions are custom plans adjusted according to the user's emotional state.

[0454] Step 7:

[0455] The server sends the generated learning plan and career path suggestions to the terminal, which then visually displays the suggestions to the user. This allows the user to review the suggestions.

[0456] Step 8:

[0457] Users review the information displayed on their devices and provide feedback, such as modifying their plans as needed. This information is then sent back to the server and used for further data analysis.

[0458] 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.

[0459] 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.

[0460] 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.

[0461] [Third Embodiment]

[0462] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0463] 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.

[0464] 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).

[0465] 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.

[0466] 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.

[0467] 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).

[0468] 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.

[0469] 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.

[0470] 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.

[0471] 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.

[0472] 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.

[0473] 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".

[0474] This invention is a system that collects detailed information such as a child's learning progress, educational institution events, and sports activities from a user-inputted terminal, and uses this information to support the child's learning. This system is realized through the cooperation of both the terminal and the server to process information in real time.

[0475] Explanation of program processing in natural language

[0476] Data collection

[0477] Users input information about their children's test results, extracurricular activity schedules, and desired schools through their devices. The devices then send this information to a server. The server further collects local exam information and statistical data from the internet.

[0478] Data Integration and Preprocessing

[0479] The server integrates data received from users and data acquired from external sources. It improves data quality by standardizing data formats and removing outliers.

[0480] Data Analysis

[0481] The server applies an artificial intelligence model to the integrated data and performs analysis. This identifies children's strengths, areas for improvement, and regional testing trends. The AI ​​model performs these analyses using machine learning algorithms.

[0482] Generating learning plans and career path suggestions

[0483] Based on the analysis results, the server generates optimal learning plans and career path suggestions for the user. These suggestions include weekly and monthly schedules, specific learning goals, and a list of potential schools.

[0484] Presentation to the user

[0485] Plans and proposals generated from the server are sent to the terminal, which then presents them to the user in a visualized format. This can be presented not only as text but also visually, such as graphs and charts.

[0486] Specific example

[0487] For example, if the information entered by the user on the device includes that the child achieved a high score on a math test, the server will suggest a set of math-related problems of a relatively high difficulty level. Furthermore, based on information about extracurricular activities, the system can automatically create a schedule that ensures efficient study time according to the child's activity schedule.

[0488] This system aims to improve the quality of education by effectively supporting children's learning even when users are busy, and by providing information that can be used as a reference for career choices.

[0489] The following describes the processing flow.

[0490] Step 1:

[0491] Users use their devices to input information such as their child's learning progress, grades, and extracurricular activity schedules. This information is temporarily stored on the device.

[0492] Step 2:

[0493] The terminal sends data entered by the user to the server. During transmission, the data integrity and format are checked, and the data format is converted as needed.

[0494] Step 3:

[0495] The server stores the received data in a database and simultaneously collects local exam information and related statistical data from the internet. At this stage, APIs are used to retrieve the data.

[0496] Step 4:

[0497] The server integrates and preprocesses the collected data. Specifically, it imputes missing values, detects and removes outliers, and generates a clean dataset suitable for analysis.

[0498] Step 5:

[0499] The server uses pre-processed data to run machine learning algorithms. This process involves analyzing children's strengths and weaknesses, as well as extracting trends in regional exam preparation.

[0500] Step 6:

[0501] The server generates study plans and career path suggestions based on machine learning results. This includes allocating study time for each subject and listing recommended universities and colleges.

[0502] Step 7:

[0503] The server generates suggestions and sends them to the terminal. The suggestions are formatted in a visually easy-to-understand way and presented to the user using charts and graphics.

[0504] Step 8:

[0505] The device receives information from the server and notifies the user. The user can then view detailed learning plans and career suggestions on the device.

[0506] (Example 1)

[0507] 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."

[0508] Traditional educational support systems have struggled to provide individually tailored advice and plans based on each child's learning progress and career choices. Therefore, despite the availability of diverse learning environments and career information, there were concerns that the optimal information for each child was not being provided, leading to a decline in the quality of education. Furthermore, there was a lack of mechanisms for timely collection and utilization of local examination information, making efficient learning support difficult.

[0509] 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.

[0510] In this invention, the server includes means for automatically collecting regional test information and statistical information through a wide-area information source; means for integrating the acquired data and performing preprocessing, including standardization and outlier removal; and means for applying machine learning algorithms to identify learning areas using the preprocessed data. This makes it possible to provide individualized learning plans and career suggestions in real time, thereby improving the quality of education.

[0511] A "terminal" is a device used for inputting and displaying information, and it provides an interface with the user.

[0512] "Wide-area information sources" refer to information sources that exist on the internet and are used to obtain external data related to education.

[0513] "Local examination information" refers to information about examinations conducted by local educational institutions and within local communities, including the content and schedule of the examinations that students take.

[0514] "Statistical information" refers to a dataset that analyzes education-related data and provides indicators such as trends and averages.

[0515] Standardization is the process of transforming data according to certain standards to make it easier to process and analyze.

[0516] "Outlier removal" refers to the operation of eliminating outliers and noise present in a dataset to improve data quality.

[0517] A "machine learning algorithm" is a computational method that uses data to train a model and perform predictions or classifications for specific tasks.

[0518] An "artificial intelligence model" is a computer program designed to automatically perform analysis and make decisions based on input data.

[0519] A "study plan" refers to a set of activities and schedules designed for students to achieve specific learning objectives.

[0520] "Career guidance" refers to information that provides recommendations and guidelines regarding students' studies and future career choices.

[0521] This invention is an educational information processing system aimed at supporting children's learning, realized through the collaboration of a terminal where the user inputs information and a server that processes the data and generates suggestions. The terminal can be a personal computer, smartphone, tablet, etc., and is provided with an intuitive interface.

[0522] Users input information such as their learning progress, educational institution event information, and club activity schedules into a terminal. This information is transmitted to a server via the internet. The server is equipped with software to automatically collect regional exam information and statistical data. This system utilizes specific information collection APIs and web scraping techniques to efficiently obtain necessary data from a wide range of education-related information sources.

[0523] Next, the server integrates the aforementioned data and performs standardization and outlier removal to improve data quality. Suitable database systems include PostgreSQL and MySQL. Based on this pre-processed data, the server applies machine learning algorithms to identify the child's strengths and areas that need improvement in their learning. AI models used here include libraries such as Python's TensorFlow and scikit-learn.

[0524] Based on the analysis results, the server generates an optimal learning plan and career path suggestions. This includes specific learning goals and schedules, as well as a list of possible schools. The generated suggestions are presented on the device in text and graph formats, making it easier for the user to visually grasp the information.

[0525] For example, when a user prompts, "My child's strongest subject is mathematics, and they recently scored over 90 points on a test. What kind of learning plan and materials would you suggest going forward?", the system can automatically suggest more challenging mathematics-related learning materials. This allows users to receive support tailored to their individual learning needs.

[0526] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0527] Step 1:

[0528] The user uses the terminal to input information about their child's learning (e.g., test results, club activity schedules, desired schools). The terminal converts the entered data into a specified format and sends it to the server over the network. Upon data input, the terminal generates an information packet for the server and sends the data according to the communication protocol.

[0529] Step 2:

[0530] The server receives data sent from the terminal. The learning information obtained as input data is validated before being stored in the database. The server also automatically collects local test information and statistical data from the internet, converts this external data into a format, and integrates it into the internal database. APIs and web scraping techniques are used in this process.

[0531] Step 3:

[0532] The server integrates the received and collected data and performs data cleaning. Specifically, it detects and removes outliers from the input data and standardizes the data. This ensures data quality and enables effective subsequent analysis. The output here is a cleansed, high-quality dataset.

[0533] Step 4:

[0534] The server applies machine learning algorithms to preprocessed data. Based on the dataset provided as input, it performs analysis using a generative AI model to identify children's strengths and areas for improvement. In this step, model training and evaluation are carried out using libraries such as Python's TensorFlow and scikit-learn. The output provides an evaluation of the learning outcomes and recommendations.

[0535] Step 5:

[0536] The server generates learning plans and career suggestions based on the analysis results. Specifically, it creates weekly and monthly study schedules, reinforcement plans for specific subjects, and lists of potential schools. The suggestions are presented graphically, making the information easy for users to understand visually.

[0537] Step 6:

[0538] The server sends the generated learning plan and career suggestions to the device. The device then presents the received information to the user through an interface. This is displayed in a dashboard format, with explanations using graphs and charts. Based on the presented recommendations, the user can effectively support their child's learning.

[0539] (Application Example 1)

[0540] 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."

[0541] There is a lack of means to efficiently understand children's learning progress at home, propose appropriate learning plans and career paths, and work closely with parents and educational institutions. Furthermore, busy parents often find it difficult to effectively support their children's education.

[0542] 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.

[0543] In this invention, the server includes means for acquiring information on learning status, educational organization events, and sports activities from electronic devices that input information; means for automatically collecting local examination information and statistical information; and means for applying artificial intelligence to identify problem areas using pre-processed numerical data. This makes it possible to monitor the progress of a child's education in real time via home automation devices and to automatically propose appropriate educational policies.

[0544] An "electronic device" is a device that interacts with a user by inputting and displaying information.

[0545] "Learning status" refers to information that indicates an individual's progress and achievement level in education.

[0546] An "educational organization" is an institution or group that provides education to learners.

[0547] An "event" refers to an event or activity planned in connection with an educational organization or learning.

[0548] "Physical activity" refers to physical or recreational activities that involve using the body.

[0549] "Numerical data" refers to data that is a numerical representation of input or collected information and is processed in digital format.

[0550] "Household automation equipment" refers to robots and devices designed for use in a home environment to support daily life.

[0551] Artificial intelligence is a technology that analyzes large amounts of data and has the ability to mimic human intellectual activity.

[0552] An "educational policy" is a plan or guideline aimed at improving learners' knowledge and skills.

[0553] To implement this invention, a system is constructed in which home automation devices and a server work together to actively collect and analyze learner information. The server first acquires information from users via electronic devices regarding their learning status, educational organization events, and physical activities. The server also automatically collects local examination information and statistical information via the internet. This results in the aggregation of comprehensive and multifaceted data.

[0554] After data collection, the server performs standardization and normalization to improve data accuracy. This includes removing outliers, enabling reliable analysis. Once data preprocessing is complete, the server uses artificial intelligence to identify learners' strengths and areas for improvement. Machine learning algorithms are used to analyze trends not only for individual learners but also for the entire region.

[0555] Home automation devices visually present educational policies and plans sent from a server to the user. This helps support children's learning when parents are busy. For example, a child who scores highly on a math test can be presented with more challenging problems, while also being given suggestions for study time that fits their physical activity schedule. These suggestions are visualized in graphs and charts and designed to be intuitively understandable.

[0556] An example of a prompt message would be, "Based on the child's recent test results, please suggest the next learning plan. Test scores: 90, 85, 88. Participation activities: Basketball, Music." Information reflecting the learner's current state is presented and suggested. Based on this information, the server constructs an optimal educational plan and delivers it to parents and learners through home automation devices.

[0557] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0558] Step 1:

[0559] The server obtains information from users via their terminals regarding their learning progress, educational organization events, and physical activities. In this process, users input this information using their terminals, and it is sent to the server. The input data is received as text and numerical data and stored on the server.

[0560] Step 2:

[0561] The server automatically collects local exam information and statistics via an internet connection. At this stage, it uses APIs and web crawling technologies to retrieve information from external sources. The retrieved data is temporarily stored in a database on the server. Input is information from external data sources, and output is an update to the server's database.

[0562] Step 3:

[0563] The server integrates input data from terminals with data collected from external sources. This generates a dataset that combines individual learner information with regional education trends. The input is data from existing databases, and the output is the integrated dataset.

[0564] Step 4:

[0565] The server performs data cleaning and normalization on the integrated dataset. It detects and removes outliers and converts the data to a standard format. This ensures data integrity and improves the accuracy of the analysis. The input is the integrated dataset, and the output is clean, normalized data.

[0566] Step 5:

[0567] The server starts analysis using an artificial intelligence model with clean data. It uses machine learning algorithms to detect the learner's strengths and areas for improvement. The input is normalized data, and the output is text and numerical data as analysis results. Specifically, it performs data analysis using a generative AI model.

[0568] Step 6:

[0569] The server generates learning plans and career path suggestions based on the analysis results. This includes weekly study schedules and lists of possible schools. The input is the analysis results, and the output is the data for the proposed educational plan.

[0570] Step 7:

[0571] The server sends the generated learning plan to the home automation device, which then presents it to the user visually or audibly. The home automation device displays the information using graphs and charts, providing it in an easily understandable format for the user. The input is the learning plan, and the output is the information display via the user interface. The suggested content is adjusted based on example prompts.

[0572] 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.

[0573] The present invention aims to recognize a user's emotions and dynamically optimize learning plans and career path suggestions according to their emotional state by incorporating an emotion engine into a learning support system. The following describes embodiments for carrying out this invention.

[0574] Explanation of program processing in natural language

[0575] Data collection

[0576] Users input data on their child's learning progress, educational events, and physical activities into the device. Simultaneously, wearable devices and biosensors can be used to collect data related to the child's emotions and stress levels. This information is then transmitted from the device to a server.

[0577] Emotion recognition and data integration

[0578] The server integrates learning and emotion data obtained from the terminal or external devices. At this stage, it utilizes an emotion engine to analyze the user's emotional state (e.g., stress level, motivation). The server combines the analysis results with other learning-related data and stores them in a database.

[0579] Data Analysis

[0580] Based on the integrated data, the server uses artificial intelligence to identify learning progress, areas of difficulty, and emotional support needs. Based on emotional data, it generates parameters to adjust the learning load and builds an optimized learning plan for the user.

[0581] Generating learning plans and career path suggestions

[0582] The server provides users with learning schedules and career path options that take their emotional state into account, based on results generated by machine learning algorithms. This includes inserting breaks according to their emotions and easing the learning plan to reduce stress.

[0583] Presentation to the user

[0584] The generated learning plan and career suggestions are sent to the device and displayed visually to the user. Furthermore, feedback based on emotional data is provided to the user, allowing them to progress through their learning while understanding their own emotional state.

[0585] Specific example

[0586] For example, if the emotion engine detects, based on information entered by the user on the device, that a child has recently scored low on a test and is experiencing high stress levels, the server will suggest a learning plan that prioritizes reviewing the basics. It will also suggest setting short-term goals and incorporating short breaks into the learning schedule to reduce stress. These adjustments aim to improve the child's learning efficiency while supporting their emotional well-being.

[0587] This system aims to provide comprehensive learning support, not only by improving academic performance but also by emphasizing children's mental health.

[0588] The following describes the processing flow.

[0589] Step 1:

[0590] The user inputs information into the device, such as the child's learning progress, academic performance, educational institution event schedules, and details of their physical activities. In addition, the device prepares to send emotional data acquired from wearable devices and biosensors to the server.

[0591] Step 2:

[0592] The terminal sends all the information it has collected to the server. Upon receiving this data, the server simultaneously collects local exam information and related statistical data via the internet.

[0593] Step 3:

[0594] The server integrates the received training data and sentiment data. In this process, preprocessing is performed by standardizing the data format, detecting and removing outliers, and supplementing the data as needed.

[0595] Step 4:

[0596] The server uses an emotion engine to analyze emotional data and identify the child's current emotional state, such as stress levels and motivation levels. This analysis is then considered along with other training data to prepare for further analysis.

[0597] Step 5:

[0598] The server uses an artificial intelligence model to analyze integrated data, identifying children's strengths and areas for improvement in their learning, as well as determining where emotionally-based support is needed.

[0599] Step 6:

[0600] Based on the analysis results, the server generates learning plans and career recommendations optimized for the user's emotional state. This includes creating dynamic plans that take emotions into account, such as adjusting the learning load and inserting breaks.

[0601] Step 7:

[0602] A learning plan and career suggestions generated from the server are sent to the device, which then visually displays them to the user. The displayed content includes specific advice and points of caution based on sentiment data.

[0603] Step 8:

[0604] Users review the information provided through their device and, if necessary, adjust their own or their child's approach to learning based on the feedback provided by the emotion engine.

[0605] The above is an embodiment of a learning support system incorporating an emotional engine. The system comprehensively understands not only a child's learning but also their mental health status, and provides appropriate guidance.

[0606] (Example 2)

[0607] 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."

[0608] Conventional educational support systems have faced challenges in adequately considering emotional data when assessing learning progress and providing career guidance, making it difficult to create learning plans that address each user's individual emotional state. Furthermore, there is a need to efficiently utilize emotion recognition technology to provide optimized suggestions tailored to each user.

[0609] 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.

[0610] In this invention, the server includes means for acquiring data such as learning status from an information input device, means for analyzing and integrating emotional data acquired by a biometric information collection device, and means for analyzing the integrated data with artificial intelligence to identify the user's problem areas. This enables the generation of learning plans and career path suggestions that take into account the user's emotional state, making more personalized educational support possible.

[0611] A "device for inputting information" is a device used by users to input their learning progress and related information, and is usually a computer, tablet, or smartphone.

[0612] A "biometric information collection device" is a device used to acquire data on a user's emotional state and stress level, and generally refers to wearable devices or sensors.

[0613] "Emotion recognition technology" is a technology that analyzes acquired emotional data to identify the user's emotional state.

[0614] "Artificial intelligence" is a technology in which computer systems imitate human intelligence to learn, reason, and adapt, and in this case, it is used for data analysis and proposal generation.

[0615] "Integrated data" refers to data collected from different sources, unified, and formatted for analysis.

[0616] A "learning plan" refers to a specific schedule and content structure designed to help a user achieve their learning goals; it is a personalized plan.

[0617] "Career guidance" refers to suggestions that advise users on their future learning and career paths based on their learning data and emotional state.

[0618] Modes for carrying out the invention

[0619] This invention is a system that provides optimal learning plans and career guidance tailored to the user's emotional state by incorporating emotional elements into learning support. The following hardware and software are used to implement the system.

[0620] Hardware and software configuration

[0621] The terminal is used as a device for users to input learning progress and related information, and specifically includes computers, tablets, and smartphones. The terminal connects to wearable devices worn by the user and collects emotion-related data such as heart rate, body temperature, and facial expression data through biosensors.

[0622] The server receives this data and uses an emotion recognition engine to analyze the user's emotional state. The emotion recognition engine quantifies stress levels and motivation based on the input biometric data, and an AI model takes in and analyzes this data. Based on the identified analysis results, the AI ​​model generates a learning plan tailored to the user.

[0623] The generated learning plan is sent from the server to the device and presented to the user through a visual interface. The user receives daily learning progress and feedback based on their emotional state through the device, allowing them to learn at their own pace and according to their own feelings.

[0624] Specific examples

[0625] For example, if a user using a wearable device shows a high stress level after scoring low on a recent academic test, the system will automatically suggest a study plan focused on reviewing the basics. It can also suggest ways to reduce stress by setting short-term goals and incorporating short breaks into the schedule.

[0626] An example of a prompt message that can be input to the generating AI model is, "Create an optimal learning plan based on the user's emotional data and output suggestions for stress reduction and improved learning efficiency." This allows the system to assist the user in learning in the most efficient way possible.

[0627] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0628] Step 1:

[0629] Users input learning status and education-related information through the device. This input includes data on learning progress, schedules, and physical activity. Furthermore, the device collects biometric data such as heart rate, body temperature, and facial expression analysis from wearable devices. This data is used as input for analyzing emotions.

[0630] Step 2:

[0631] The device transmits collected learning information and biometric data to the server. During this transmission, a secure data transmission protocol is used to protect the data. The server first integrates the received data and stores it in a database. Simultaneously, it inputs the biometric data into an emotion recognition engine to analyze emotional state and stress levels.

[0632] Step 3:

[0633] The server inputs the analyzed emotional state and training data into a generating AI model to create a learning plan optimized for the user's learning needs. Here, the learning load is adjusted based on the emotional data, and the plan is refined to provide the necessary support. The generated plan may also include praise and break suggestions to boost the user's motivation.

[0634] Step 4:

[0635] The server sends the generated learning plan and career path suggestions to the terminal. The terminal displays this information in its user interface, providing the user with visual feedback. Based on the visualized emotional state-based feedback, the user can adjust their learning and progress more efficiently.

[0636] Step 5:

[0637] After receiving feedback via their device, users carry out daily learning activities based on their learning plan. When new data is collected during these activities, the process restarts from step 1, ensuring continuous learning optimization. This enables dynamic learning support tailored to individual needs.

[0638] (Application Example 2)

[0639] 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."

[0640] Existing learning support systems suggest learning plans and career paths based on the user's learning status and progress, but they do not take into account the user's emotional state, making it difficult to optimize learning efficiency and mental health. Furthermore, there is a lack of means to mitigate the impact of stress and decreased motivation on academic performance.

[0641] 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.

[0642] In this invention, the server includes means for acquiring learning status, educational facility activities, and exercise information from an information input device; means for automatically collecting regional test information and statistical information; and means for integrating and pre-processing the acquired and collected data. This makes it possible to analyze emotional states and optimize learning plans and career guidance suggestions based on them.

[0643] A "device for inputting information" is an electronic device used to receive learning progress and activity information from the user.

[0644] "Learning status" refers to information that shows the user's learning progress, performance, and level of understanding.

[0645] "Educational facility activities" refers to information such as events and curricula held at schools and other educational institutions.

[0646] "Exercise information" refers to data related to physical activity and sports.

[0647] "Local examination information" refers to all information regarding examinations and tests within a given region.

[0648] "Statistical information" refers to information that includes analysis results based on data and numerical values.

[0649] "Methods for integrating and pre-processing data" refers to the process of centralizing various acquired data and preparing them for analysis.

[0650] "Applying machine learning to identify problem areas" refers to methods of clarifying user learning and emotional issues using AI technology.

[0651] "Means for generating learning plans and career guidance" refers to methods for creating plans that indicate the next steps in a user's learning process.

[0652] "A means of analyzing emotional states and reflecting that information in learning plans and career guidance" refers to a method of evaluating the user's psychological state and making optimal suggestions accordingly.

[0653] "Means for transmitting and presenting generated suggestions to the device" refers to the communication and display process for showing the created learning plans and suggestions to the user.

[0654] The system for implementing this invention has the function of automatically collecting local test information and statistical information based on learning status, educational facility activities, and exercise information obtained from an information input device, and analyzing emotional states. The server integrates the acquired data and performs data preprocessing to generate optimal learning plans and career path suggestions for the user.

[0655] The system's hardware includes a device equipped with a camera sensor and microphone, as well as a server, through which data is collected. The server uses sentiment analysis software to assess emotional states. Specifically, it leverages technologies such as the Azure Emotion API to determine the user's stress and motivation levels based on the analyzed data. It also utilizes machine learning frameworks such as TensorFlow to generate learning plans.

[0656] As a concrete example of this system, consider a situation where a user's child is studying for a school exam. The system collects the child's facial expressions and voice quality through a data input device. If the emotion engine detects a high stress level, the server provides a learning plan that emphasizes reviewing fundamentals, sets short-term goals, and suggests breaks. This allows the child to learn efficiently while maintaining their mental health.

[0657] An example of a prompt related to a generative AI model would be: "Generate a Python script that analyzes a child's emotional state based on facial expression and voice data and creates optimal learning advice." In this way, the program provides support to maximize the user's learning efficiency and psychological well-being.

[0658] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0659] Step 1:

[0660] The device uses its camera and microphone to collect user facial expression and voice data. This data serves as input and is temporarily stored on the device.

[0661] Step 2:

[0662] The device transmits collected facial expression and voice data to the server. Simultaneously, it also collects and transfers data such as learning status, educational facility activities, and local exam information to the server.

[0663] Step 3:

[0664] The server integrates all received data, removes outliers, and normalizes the data. This generates a clean dataset, which serves as the foundational data for AI analysis.

[0665] Step 4:

[0666] The server uses the Azure Emotion API to analyze the user's emotional state from the received facial and audio data. This allows it to extract emotional information such as stress levels and motivation levels.

[0667] Step 5:

[0668] The server uses a machine learning algorithm based on TensorFlow to identify the user's learning progress and areas of difficulty from the integrated data. This analysis then sets specific goals and challenges for building a learning plan.

[0669] Step 6:

[0670] The server integrates extracted emotional information with learning analysis results to generate a learning plan and career path suggestions tailored to the user. These suggestions are custom plans adjusted according to the user's emotional state.

[0671] Step 7:

[0672] The server sends the generated learning plan and career path suggestions to the terminal, which then visually displays the suggestions to the user. This allows the user to review the suggestions.

[0673] Step 8:

[0674] Users review the information displayed on their devices and provide feedback, such as modifying their plans as needed. This information is then sent back to the server and used for further data analysis.

[0675] 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.

[0676] 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.

[0677] 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.

[0678] [Fourth Embodiment]

[0679] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0680] 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.

[0681] 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).

[0682] 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.

[0683] 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.

[0684] 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).

[0685] 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.

[0686] 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.

[0687] 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.

[0688] 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.

[0689] 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.

[0690] 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.

[0691] 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".

[0692] This invention is a system that collects detailed information such as a child's learning progress, educational institution events, and sports activities from a user-inputted terminal, and uses this information to support the child's learning. This system is realized through the cooperation of both the terminal and the server to process information in real time.

[0693] Explanation of program processing in natural language

[0694] Data collection

[0695] Users input information about their children's test results, extracurricular activity schedules, and desired schools through their devices. The devices then send this information to a server. The server further collects local exam information and statistical data from the internet.

[0696] Data Integration and Preprocessing

[0697] The server integrates data received from users and data acquired from external sources. It improves data quality by standardizing data formats and removing outliers.

[0698] Data Analysis

[0699] The server applies an artificial intelligence model to the integrated data and performs analysis. This identifies children's strengths, areas for improvement, and regional testing trends. The AI ​​model performs these analyses using machine learning algorithms.

[0700] Generating learning plans and career path suggestions

[0701] Based on the analysis results, the server generates optimal learning plans and career path suggestions for the user. These suggestions include weekly and monthly schedules, specific learning goals, and a list of potential schools.

[0702] Presentation to the user

[0703] Plans and proposals generated from the server are sent to the terminal, which then presents them to the user in a visualized format. This can be presented not only as text but also visually, such as graphs and charts.

[0704] Specific example

[0705] For example, if the information entered by the user on the device includes that the child achieved a high score on a math test, the server will suggest a set of math-related problems of a relatively high difficulty level. Furthermore, based on information about extracurricular activities, the system can automatically create a schedule that ensures efficient study time according to the child's activity schedule.

[0706] This system aims to improve the quality of education by effectively supporting children's learning even when users are busy, and by providing information that can be used as a reference for career choices.

[0707] The following describes the processing flow.

[0708] Step 1:

[0709] Users use their devices to input information such as their child's learning progress, grades, and extracurricular activity schedules. This information is temporarily stored on the device.

[0710] Step 2:

[0711] The terminal sends data entered by the user to the server. During transmission, the data integrity and format are checked, and the data format is converted as needed.

[0712] Step 3:

[0713] The server stores the received data in a database and simultaneously collects local exam information and related statistical data from the internet. At this stage, APIs are used to retrieve the data.

[0714] Step 4:

[0715] The server integrates and preprocesses the collected data. Specifically, it imputes missing values, detects and removes outliers, and generates a clean dataset suitable for analysis.

[0716] Step 5:

[0717] The server uses pre-processed data to run machine learning algorithms. This process involves analyzing children's strengths and weaknesses, as well as extracting trends in regional exam preparation.

[0718] Step 6:

[0719] The server generates study plans and career path suggestions based on machine learning results. This includes allocating study time for each subject and listing recommended universities and colleges.

[0720] Step 7:

[0721] The server generates suggestions and sends them to the terminal. The suggestions are formatted in a visually easy-to-understand way and presented to the user using charts and graphics.

[0722] Step 8:

[0723] The device receives information from the server and notifies the user. The user can then view detailed learning plans and career suggestions on the device.

[0724] (Example 1)

[0725] 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".

[0726] Traditional educational support systems have struggled to provide individually tailored advice and plans based on each child's learning progress and career choices. Therefore, despite the availability of diverse learning environments and career information, there were concerns that the optimal information for each child was not being provided, leading to a decline in the quality of education. Furthermore, there was a lack of mechanisms for timely collection and utilization of local examination information, making efficient learning support difficult.

[0727] 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.

[0728] In this invention, the server includes means for automatically collecting regional test information and statistical information through a wide-area information source; means for integrating the acquired data and performing preprocessing, including standardization and outlier removal; and means for applying machine learning algorithms to identify learning areas using the preprocessed data. This makes it possible to provide individualized learning plans and career suggestions in real time, thereby improving the quality of education.

[0729] A "terminal" is a device used for inputting and displaying information, and it provides an interface with the user.

[0730] "Wide-area information sources" refer to information sources that exist on the internet and are used to obtain external data related to education.

[0731] "Local examination information" refers to information about examinations conducted by local educational institutions and within local communities, including the content and schedule of the examinations that students take.

[0732] "Statistical information" refers to a dataset that analyzes education-related data and provides indicators such as trends and averages.

[0733] Standardization is the process of transforming data according to certain standards to make it easier to process and analyze.

[0734] "Outlier removal" refers to the operation of eliminating outliers and noise present in a dataset to improve data quality.

[0735] A "machine learning algorithm" is a computational method that uses data to train a model and perform predictions or classifications for specific tasks.

[0736] An "artificial intelligence model" is a computer program designed to automatically perform analysis and make decisions based on input data.

[0737] A "study plan" refers to a set of activities and schedules designed for students to achieve specific learning objectives.

[0738] "Career guidance" refers to information that provides recommendations and guidelines regarding students' studies and future career choices.

[0739] This invention is an educational information processing system aimed at supporting children's learning, realized through the collaboration of a terminal where the user inputs information and a server that processes the data and generates suggestions. The terminal can be a personal computer, smartphone, tablet, etc., and is provided with an intuitive interface.

[0740] Users input information such as their learning progress, educational institution event information, and club activity schedules into a terminal. This information is transmitted to a server via the internet. The server is equipped with software to automatically collect regional exam information and statistical data. This system utilizes specific information collection APIs and web scraping techniques to efficiently obtain necessary data from a wide range of education-related information sources.

[0741] Next, the server integrates the aforementioned data and performs standardization and outlier removal to improve data quality. Suitable database systems include PostgreSQL and MySQL. Based on this pre-processed data, the server applies machine learning algorithms to identify the child's strengths and areas that need improvement in their learning. AI models used here include libraries such as Python's TensorFlow and scikit-learn.

[0742] Based on the analysis results, the server generates an optimal learning plan and career path suggestions. This includes specific learning goals and schedules, as well as a list of possible schools. The generated suggestions are presented on the device in text and graph formats, making it easier for the user to visually grasp the information.

[0743] For example, when a user prompts, "My child's strongest subject is mathematics, and they recently scored over 90 points on a test. What kind of learning plan and materials would you suggest going forward?", the system can automatically suggest more challenging mathematics-related learning materials. This allows users to receive support tailored to their individual learning needs.

[0744] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0745] Step 1:

[0746] The user uses the terminal to input information about their child's learning (e.g., test results, club activity schedules, desired schools). The terminal converts the entered data into a specified format and sends it to the server over the network. Upon data input, the terminal generates an information packet for the server and sends the data according to the communication protocol.

[0747] Step 2:

[0748] The server receives data sent from the terminal. The learning information obtained as input data is validated before being stored in the database. The server also automatically collects local test information and statistical data from the internet, converts this external data into a format, and integrates it into the internal database. APIs and web scraping techniques are used in this process.

[0749] Step 3:

[0750] The server integrates the received and collected data and performs data cleaning. Specifically, it detects and removes outliers from the input data and standardizes the data. This ensures data quality and enables effective subsequent analysis. The output here is a cleansed, high-quality dataset.

[0751] Step 4:

[0752] The server applies machine learning algorithms to preprocessed data. Based on the dataset provided as input, it performs analysis using a generative AI model to identify children's strengths and areas for improvement. In this step, model training and evaluation are carried out using libraries such as Python's TensorFlow and scikit-learn. The output provides an evaluation of the learning outcomes and recommendations.

[0753] Step 5:

[0754] The server generates learning plans and career suggestions based on the analysis results. Specifically, it creates weekly and monthly study schedules, reinforcement plans for specific subjects, and lists of potential schools. The suggestions are presented graphically, making the information easy for users to understand visually.

[0755] Step 6:

[0756] The server sends the generated learning plan and career suggestions to the device. The device then presents the received information to the user through an interface. This is displayed in a dashboard format, with explanations using graphs and charts. Based on the presented recommendations, the user can effectively support their child's learning.

[0757] (Application Example 1)

[0758] 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".

[0759] There is a lack of means to efficiently understand children's learning progress at home, propose appropriate learning plans and career paths, and work closely with parents and educational institutions. Furthermore, busy parents often find it difficult to effectively support their children's education.

[0760] 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.

[0761] In this invention, the server includes means for acquiring information on learning status, educational organization events, and sports activities from electronic devices that input information; means for automatically collecting local examination information and statistical information; and means for applying artificial intelligence to identify problem areas using pre-processed numerical data. This makes it possible to monitor the progress of a child's education in real time via home automation devices and to automatically propose appropriate educational policies.

[0762] An "electronic device" is a device that interacts with a user by inputting and displaying information.

[0763] "Learning status" refers to information that indicates an individual's progress and achievement level in education.

[0764] An "educational organization" is an institution or group that provides education to learners.

[0765] An "event" refers to an event or activity planned in connection with an educational organization or learning.

[0766] "Physical activity" refers to physical or recreational activities that involve using the body.

[0767] "Numerical data" refers to data that is a numerical representation of input or collected information and is processed in digital format.

[0768] "Household automation equipment" refers to robots and devices designed for use in a home environment to support daily life.

[0769] Artificial intelligence is a technology that analyzes large amounts of data and has the ability to mimic human intellectual activity.

[0770] An "educational policy" is a plan or guideline aimed at improving learners' knowledge and skills.

[0771] To implement this invention, a system is constructed in which home automation devices and a server work together to actively collect and analyze learner information. The server first acquires information from users via electronic devices regarding their learning status, educational organization events, and physical activities. The server also automatically collects local examination information and statistical information via the internet. This results in the aggregation of comprehensive and multifaceted data.

[0772] After data collection, the server performs standardization and normalization to improve data accuracy. This includes removing outliers, enabling reliable analysis. Once data preprocessing is complete, the server uses artificial intelligence to identify learners' strengths and areas for improvement. Machine learning algorithms are used to analyze trends not only for individual learners but also for the entire region.

[0773] Home automation devices visually present educational policies and plans sent from a server to the user. This helps support children's learning when parents are busy. For example, a child who scores highly on a math test can be presented with more challenging problems, while also being given suggestions for study time that fits their physical activity schedule. These suggestions are visualized in graphs and charts and designed to be intuitively understandable.

[0774] An example of a prompt message would be, "Based on the child's recent test results, please suggest the next learning plan. Test scores: 90, 85, 88. Participation activities: Basketball, Music." Information reflecting the learner's current state is presented and suggested. Based on this information, the server constructs an optimal educational plan and delivers it to parents and learners through home automation devices.

[0775] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0776] Step 1:

[0777] The server obtains information from users via their terminals regarding their learning progress, educational organization events, and physical activities. In this process, users input this information using their terminals, and it is sent to the server. The input data is received as text and numerical data and stored on the server.

[0778] Step 2:

[0779] The server automatically collects local exam information and statistics via an internet connection. At this stage, it uses APIs and web crawling technologies to retrieve information from external sources. The retrieved data is temporarily stored in a database on the server. Input is information from external data sources, and output is an update to the server's database.

[0780] Step 3:

[0781] The server integrates input data from terminals with data collected from external sources. This generates a dataset that combines individual learner information with regional education trends. The input is data from existing databases, and the output is the integrated dataset.

[0782] Step 4:

[0783] The server performs data cleaning and normalization on the integrated dataset. It detects and removes outliers and converts the data to a standard format. This ensures data integrity and improves the accuracy of the analysis. The input is the integrated dataset, and the output is clean, normalized data.

[0784] Step 5:

[0785] The server starts analysis using an artificial intelligence model with clean data. It uses machine learning algorithms to detect the learner's strengths and areas for improvement. The input is normalized data, and the output is text and numerical data as analysis results. Specifically, it performs data analysis using a generative AI model.

[0786] Step 6:

[0787] The server generates learning plans and career path suggestions based on the analysis results. This includes weekly study schedules and lists of possible schools. The input is the analysis results, and the output is the data for the proposed educational plan.

[0788] Step 7:

[0789] The server sends the generated learning plan to the home automation device, which then presents it to the user visually or audibly. The home automation device displays the information using graphs and charts, providing it in an easily understandable format for the user. The input is the learning plan, and the output is the information display via the user interface. The suggested content is adjusted based on example prompts.

[0790] 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.

[0791] The present invention aims to recognize a user's emotions and dynamically optimize learning plans and career path suggestions according to their emotional state by incorporating an emotion engine into a learning support system. The following describes embodiments for carrying out this invention.

[0792] Explanation of program processing in natural language

[0793] Data collection

[0794] Users input data on their child's learning progress, educational events, and physical activities into the device. Simultaneously, wearable devices and biosensors can be used to collect data related to the child's emotions and stress levels. This information is then transmitted from the device to a server.

[0795] Emotion recognition and data integration

[0796] The server integrates learning and emotion data obtained from the terminal or external devices. At this stage, it utilizes an emotion engine to analyze the user's emotional state (e.g., stress level, motivation). The server combines the analysis results with other learning-related data and stores them in a database.

[0797] Data Analysis

[0798] Based on the integrated data, the server uses artificial intelligence to identify learning progress, areas of difficulty, and emotional support needs. Based on emotional data, it generates parameters to adjust the learning load and builds an optimized learning plan for the user.

[0799] Generating learning plans and career path suggestions

[0800] The server provides users with learning schedules and career path options that take their emotional state into account, based on results generated by machine learning algorithms. This includes inserting breaks according to their emotions and easing the learning plan to reduce stress.

[0801] Presentation to the user

[0802] The generated learning plan and career suggestions are sent to the device and displayed visually to the user. Furthermore, feedback based on emotional data is provided to the user, allowing them to progress through their learning while understanding their own emotional state.

[0803] Specific example

[0804] For example, if the emotion engine detects, based on information entered by the user on the device, that a child has recently scored low on a test and is experiencing high stress levels, the server will suggest a learning plan that prioritizes reviewing the basics. It will also suggest setting short-term goals and incorporating short breaks into the learning schedule to reduce stress. These adjustments aim to improve the child's learning efficiency while supporting their emotional well-being.

[0805] This system aims to provide comprehensive learning support, not only by improving academic performance but also by emphasizing children's mental health.

[0806] The following describes the processing flow.

[0807] Step 1:

[0808] The user inputs information into the device, such as the child's learning progress, academic performance, educational institution event schedules, and details of their physical activities. In addition, the device prepares to send emotional data acquired from wearable devices and biosensors to the server.

[0809] Step 2:

[0810] The terminal sends all the information it has collected to the server. Upon receiving this data, the server simultaneously collects local exam information and related statistical data via the internet.

[0811] Step 3:

[0812] The server integrates the received training data and sentiment data. In this process, preprocessing is performed by standardizing the data format, detecting and removing outliers, and supplementing the data as needed.

[0813] Step 4:

[0814] The server uses an emotion engine to analyze emotional data and identify the child's current emotional state, such as stress levels and motivation levels. This analysis is then considered along with other training data to prepare for further analysis.

[0815] Step 5:

[0816] The server uses an artificial intelligence model to analyze integrated data, identifying children's strengths and areas for improvement in their learning, as well as determining where emotionally-based support is needed.

[0817] Step 6:

[0818] Based on the analysis results, the server generates learning plans and career recommendations optimized for the user's emotional state. This includes creating dynamic plans that take emotions into account, such as adjusting the learning load and inserting breaks.

[0819] Step 7:

[0820] A learning plan and career suggestions generated from the server are sent to the device, which then visually displays them to the user. The displayed content includes specific advice and points of caution based on sentiment data.

[0821] Step 8:

[0822] Users review the information provided through their device and, if necessary, adjust their own or their child's approach to learning based on the feedback provided by the emotion engine.

[0823] The above is an embodiment of a learning support system incorporating an emotional engine. The system comprehensively understands not only a child's learning but also their mental health status, and provides appropriate guidance.

[0824] (Example 2)

[0825] 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".

[0826] Conventional educational support systems have faced challenges in adequately considering emotional data when assessing learning progress and providing career guidance, making it difficult to create learning plans that address each user's individual emotional state. Furthermore, there is a need to efficiently utilize emotion recognition technology to provide optimized suggestions tailored to each user.

[0827] 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.

[0828] In this invention, the server includes means for acquiring data such as learning status from an information input device, means for analyzing and integrating emotional data acquired by a biometric information collection device, and means for analyzing the integrated data with artificial intelligence to identify the user's problem areas. This enables the generation of learning plans and career path suggestions that take into account the user's emotional state, making more personalized educational support possible.

[0829] A "device for inputting information" is a device used by users to input their learning progress and related information, and is usually a computer, tablet, or smartphone.

[0830] A "biometric information collection device" is a device used to acquire data on a user's emotional state and stress level, and generally refers to wearable devices or sensors.

[0831] "Emotion recognition technology" is a technology that analyzes acquired emotional data to identify the user's emotional state.

[0832] "Artificial intelligence" is a technology in which computer systems imitate human intelligence to learn, reason, and adapt, and in this case, it is used for data analysis and proposal generation.

[0833] "Integrated data" refers to data collected from different sources, unified, and formatted for analysis.

[0834] A "learning plan" refers to a specific schedule and content structure designed to help a user achieve their learning goals; it is a personalized plan.

[0835] "Career guidance" refers to suggestions that advise users on their future learning and career paths based on their learning data and emotional state.

[0836] Modes for carrying out the invention

[0837] This invention is a system that provides optimal learning plans and career guidance tailored to the user's emotional state by incorporating emotional elements into learning support. The following hardware and software are used to implement the system.

[0838] Hardware and software configuration

[0839] The terminal is used as a device for users to input learning progress and related information, and specifically includes computers, tablets, and smartphones. The terminal connects to wearable devices worn by the user and collects emotion-related data such as heart rate, body temperature, and facial expression data through biosensors.

[0840] The server receives this data and uses an emotion recognition engine to analyze the user's emotional state. The emotion recognition engine quantifies stress levels and motivation based on the input biometric data, and an AI model takes in and analyzes this data. Based on the identified analysis results, the AI ​​model generates a learning plan tailored to the user.

[0841] The generated learning plan is sent from the server to the device and presented to the user through a visual interface. The user receives daily learning progress and feedback based on their emotional state through the device, allowing them to learn at their own pace and according to their own feelings.

[0842] Specific examples

[0843] For example, if a user using a wearable device shows a high stress level after scoring low on a recent academic test, the system will automatically suggest a study plan focused on reviewing the basics. It can also suggest ways to reduce stress by setting short-term goals and incorporating short breaks into the schedule.

[0844] An example of a prompt message that can be input to the generating AI model is, "Create an optimal learning plan based on the user's emotional data and output suggestions for stress reduction and improved learning efficiency." This allows the system to assist the user in learning in the most efficient way possible.

[0845] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0846] Step 1:

[0847] Users input learning status and education-related information through the device. This input includes data on learning progress, schedules, and physical activity. Furthermore, the device collects biometric data such as heart rate, body temperature, and facial expression analysis from wearable devices. This data is used as input for analyzing emotions.

[0848] Step 2:

[0849] The device transmits collected learning information and biometric data to the server. During this transmission, a secure data transmission protocol is used to protect the data. The server first integrates the received data and stores it in a database. Simultaneously, it inputs the biometric data into an emotion recognition engine to analyze emotional state and stress levels.

[0850] Step 3:

[0851] The server inputs the analyzed emotional state and training data into a generating AI model to create a learning plan optimized for the user's learning needs. Here, the learning load is adjusted based on the emotional data, and the plan is refined to provide the necessary support. The generated plan may also include praise and break suggestions to boost the user's motivation.

[0852] Step 4:

[0853] The server sends the generated learning plan and career path suggestions to the terminal. The terminal displays this information in its user interface, providing the user with visual feedback. Based on the visualized emotional state-based feedback, the user can adjust their learning and progress more efficiently.

[0854] Step 5:

[0855] After receiving feedback via their device, users carry out daily learning activities based on their learning plan. When new data is collected during these activities, the process restarts from step 1, ensuring continuous learning optimization. This enables dynamic learning support tailored to individual needs.

[0856] (Application Example 2)

[0857] 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".

[0858] Existing learning support systems suggest learning plans and career paths based on the user's learning status and progress, but they do not take into account the user's emotional state, making it difficult to optimize learning efficiency and mental health. Furthermore, there is a lack of means to mitigate the impact of stress and decreased motivation on academic performance.

[0859] 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.

[0860] In this invention, the server includes means for acquiring learning status, educational facility activities, and exercise information from an information input device; means for automatically collecting regional test information and statistical information; and means for integrating and pre-processing the acquired and collected data. This makes it possible to analyze emotional states and optimize learning plans and career guidance suggestions based on them.

[0861] A "device for inputting information" is an electronic device used to receive learning progress and activity information from the user.

[0862] "Learning status" refers to information that shows the user's learning progress, performance, and level of understanding.

[0863] "Educational facility activities" refers to information such as events and curricula held at schools and other educational institutions.

[0864] "Exercise information" refers to data related to physical activity and sports.

[0865] "Local examination information" refers to all information regarding examinations and tests within a given region.

[0866] "Statistical information" refers to information that includes analysis results based on data and numerical values.

[0867] "Methods for integrating and pre-processing data" refers to the process of centralizing various acquired data and preparing them for analysis.

[0868] "Applying machine learning to identify problem areas" refers to methods of clarifying user learning and emotional issues using AI technology.

[0869] "Means for generating learning plans and career guidance" refers to methods for creating plans that indicate the next steps in a user's learning process.

[0870] "A means of analyzing emotional states and reflecting that information in learning plans and career guidance" refers to a method of evaluating the user's psychological state and making optimal suggestions accordingly.

[0871] "Means for transmitting and presenting generated suggestions to the device" refers to the communication and display process for showing the created learning plans and suggestions to the user.

[0872] The system for implementing this invention has the function of automatically collecting local test information and statistical information based on learning status, educational facility activities, and exercise information obtained from an information input device, and analyzing emotional states. The server integrates the acquired data and performs data preprocessing to generate optimal learning plans and career path suggestions for the user.

[0873] The system's hardware includes a device equipped with a camera sensor and microphone, as well as a server, through which data is collected. The server uses sentiment analysis software to assess emotional states. Specifically, it leverages technologies such as the Azure Emotion API to determine the user's stress and motivation levels based on the analyzed data. It also utilizes machine learning frameworks such as TensorFlow to generate learning plans.

[0874] As a concrete example of this system, consider a situation where a user's child is studying for a school exam. The system collects the child's facial expressions and voice quality through a data input device. If the emotion engine detects a high stress level, the server provides a learning plan that emphasizes reviewing fundamentals, sets short-term goals, and suggests breaks. This allows the child to learn efficiently while maintaining their mental health.

[0875] An example of a prompt related to a generative AI model would be: "Generate a Python script that analyzes a child's emotional state based on facial expression and voice data and creates optimal learning advice." In this way, the program provides support to maximize the user's learning efficiency and psychological well-being.

[0876] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0877] Step 1:

[0878] The device uses its camera and microphone to collect user facial expression and voice data. This data serves as input and is temporarily stored on the device.

[0879] Step 2:

[0880] The device transmits collected facial expression and voice data to the server. Simultaneously, it also collects and transfers data such as learning status, educational facility activities, and local exam information to the server.

[0881] Step 3:

[0882] The server integrates all received data, removes outliers, and normalizes the data. This generates a clean dataset, which serves as the foundational data for AI analysis.

[0883] Step 4:

[0884] The server uses the Azure Emotion API to analyze the user's emotional state from the received facial and audio data. This allows it to extract emotional information such as stress levels and motivation levels.

[0885] Step 5:

[0886] The server uses a machine learning algorithm based on TensorFlow to identify the user's learning progress and areas of difficulty from the integrated data. This analysis then sets specific goals and challenges for building a learning plan.

[0887] Step 6:

[0888] The server integrates extracted emotional information with learning analysis results to generate a learning plan and career path suggestions tailored to the user. These suggestions are custom plans adjusted according to the user's emotional state.

[0889] Step 7:

[0890] The server sends the generated learning plan and career path suggestions to the terminal, which then visually displays the suggestions to the user. This allows the user to review the suggestions.

[0891] Step 8:

[0892] Users review the information displayed on their devices and provide feedback, such as modifying their plans as needed. This information is then sent back to the server and used for further data analysis.

[0893] 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.

[0894] 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.

[0895] 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.

[0896] 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.

[0897] 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.

[0898] 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.

[0899] 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.

[0900] 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.

[0901] 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."

[0902] 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.

[0903] 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.

[0904] 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.

[0905] 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.

[0906] 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.

[0907] 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.

[0908] 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.

[0909] 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.

[0910] 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.

[0911] 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.

[0912] 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.

[0913] 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 to be incorporated by reference.

[0914] The following is further disclosed regarding the embodiments described above.

[0915] (Claim 1)

[0916] A means of obtaining information on learning progress, educational institution events, and sports activities from a terminal that inputs information,

[0917] A means of automatically collecting local exam information and statistical data,

[0918] A means for integrating and preprocessing acquired and collected data,

[0919] A means of applying artificial intelligence to identify problem areas using preprocessed data,

[0920] A means for generating learning plans and career path suggestions based on results derived by artificial intelligence,

[0921] A means of sending and presenting the generated proposal to the terminal,

[0922] A system that includes this.

[0923] (Claim 2)

[0924] The system according to claim 1, further comprising means for removing outliers and normalizing the data in order to improve the accuracy of the collected data.

[0925] (Claim 3)

[0926] The system according to claim 1, wherein the artificial intelligence model can adapt to the user and adjust the suggested content.

[0927] "Example 1"

[0928] (Claim 1)

[0929] A means of obtaining information on learning progress, educational institution events, and sports activities from a terminal that inputs information,

[0930] A means of automatically collecting regional test information and statistical information through a wide-area information source,

[0931] A means for integrating acquired and collected data and performing preprocessing including standardization and outlier removal,

[0932] A means of applying a machine learning algorithm to identify learning domains using preprocessed data,

[0933] A means for generating learning plans and career path suggestions based on results derived from an artificial intelligence model,

[0934] A means of sending the generated proposal to a terminal and presenting it in a visualized format,

[0935] A system that includes this.

[0936] (Claim 2)

[0937] The system according to claim 1, further comprising means for removing outliers, normalizing the data, and storing it in a format suitable for the algorithm, in order to improve the accuracy of the collected data.

[0938] (Claim 3)

[0939] The system according to claim 1, wherein the machine learning algorithm can adjust the suggested content to suit individual users.

[0940] "Application Example 1"

[0941] (Claim 1)

[0942] A means of obtaining information on learning progress, educational organization events, and sports activities from electronic devices into which information is entered.

[0943] A means of automatically collecting local exam information and statistical data,

[0944] A means for integrating and preprocessing acquired and collected numerical data,

[0945] A means of applying artificial intelligence to identify problem areas using preprocessed numerical data,

[0946] A means of generating educational plans and career guidance suggestions based on results derived by artificial intelligence,

[0947] A means of transmitting and presenting the generated proposals to an electronic device,

[0948] Means for incorporating functions into home automation devices that monitor learners' knowledge acquisition and assist in suggesting educational policies,

[0949] A system that includes this.

[0950] (Claim 2)

[0951] The system according to claim 1, further comprising means for removing outliers and normalizing the numerical data in order to improve the accuracy of the collected numerical data.

[0952] (Claim 3)

[0953] The system according to claim 1, wherein the artificial intelligence model can adapt to the user and adjust the suggested content, and has a function to present information using visual effects through an automated device in the home.

[0954] "Example 2 of combining an emotion engine"

[0955] (Claim 1)

[0956] A means of acquiring data on learning status, educational institution events, and physical activities from a device that inputs information,

[0957] A means for analyzing user emotional data acquired by a biometric information collection device using emotion recognition technology, and for integrating the analyzed emotional data,

[0958] A means of using integrated data to apply artificial intelligence to identify problem areas and the need for emotional support,

[0959] A means for generating learning plans and career path suggestions that take into account the user's emotional state, based on results derived by artificial intelligence,

[0960] A means for transmitting the generated learning plan and career suggestion to the device, including feedback based on emotional data,

[0961] A system that includes this.

[0962] (Claim 2)

[0963] The system according to claim 1, further comprising means for removing abnormal values ​​obtained from the biological information collection device and normalizing the data.

[0964] (Claim 3)

[0965] The system according to claim 1, wherein the artificial intelligence model can adjust the learning plan and career path suggestions in accordance with the user's emotional state.

[0966] "Application example 2 when combining with an emotional engine"

[0967] (Claim 1)

[0968] A means of obtaining information on learning status, educational facility activities, and exercise from a device that inputs information,

[0969] A means of automatically collecting regional test information and statistical information,

[0970] A means for integrating and preprocessing acquired and collected data,

[0971] A means of applying machine learning to identify problem areas using preprocessed data,

[0972] A means for generating learning plans and career path suggestions based on results derived from machine learning,

[0973] A means of analyzing emotional states and reflecting that information in learning plans and career guidance suggestions,

[0974] A means for transmitting and presenting the generated proposal to the device,

[0975] A system that includes this.

[0976] (Claim 2)

[0977] The system according to claim 1, further comprising means for removing outliers and normalizing the data in order to improve the accuracy of the collected data.

[0978] (Claim 3)

[0979] The system according to claim 1, wherein the machine learning model can adjust the suggested content in accordance with the user's emotional state. [Explanation of Symbols]

[0980] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of obtaining information on learning progress, educational organization events, and sports activities from electronic devices into which information is entered. A means of automatically collecting local exam information and statistical data, A means for integrating and preprocessing acquired and collected numerical data, A means of applying artificial intelligence to identify problem areas using preprocessed numerical data, A means of generating educational plans and career guidance suggestions based on results derived by artificial intelligence, A means of transmitting and presenting the generated proposals to an electronic device, Means for incorporating functions into home automation devices that monitor learners' knowledge acquisition and assist in suggesting educational policies, A system that includes this.

2. The system according to claim 1, further comprising means for removing outliers and normalizing the numerical data in order to improve the accuracy of the collected numerical data.

3. The system according to claim 1, wherein the artificial intelligence model can adapt to the user and adjust the suggested content, and has a function to present information using visual effects through an automated device in the home.

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