system

Abstract

We provide the system. [Solution] Means for collecting attribute information of individuals receiving individual support, A means for analyzing the attribute information and generating an optimized support plan, Means for notifying the supporter and supervisor of the aforementioned support plan, Means for providing assistance through a visual information output device carried by the supporter, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 the educational field where individual support for learners who require special assistance is demanded, the burden on educators is increasing. Also, there is a problem that it is difficult to appropriately grasp the behavior patterns and learning progress of learners and to strengthen cooperation with guardians and experts. As a result, it has become difficult to provide an optimal educational environment for learners.

Means for Solving the Problems

[0005] This invention provides a system for collecting learners' personal characteristics information and generating personalized educational plans based on this information. The system includes means for notifying educators and parents of the educational plans, as well as means for recording learners' behavioral patterns to identify problematic behaviors, and means for providing information to parents through a parent portal. This makes it possible to provide appropriate support to learners while reducing the burden on educators.

[0006] "Learner's personal characteristics information" refers to information about a learner's individual abilities, learning history, behavioral patterns, and other characteristics.

[0007] "Analysis" refers to the evaluation and analysis of collected personal characteristic information using statistical or algorithmic methods.

[0008] An "individualized education plan" is a plan that specifies the optimal learning process and materials based on the characteristics and needs of each individual learner.

[0009] An "educator" refers to a person or institution whose role is to transmit knowledge and provide guidance to learners.

[0010] "Notification" refers to the act of informing educators and parents about the generated educational plans and analysis results.

[0011] "Behavioral patterns" refer to the tendencies in actions and reactions that learners exhibit on a daily basis.

[0012] "Problem behavior" refers to learner actions or attitudes that hinder learning.

[0013] A "parent portal" refers to a dedicated information system designed to allow parents to easily access and manage information about their children. [Brief explanation of the drawing]

[0014] [Figure 1]It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It 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.

MODE FOR CARRYING OUT THE INVENTION

[0015] 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.

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

[0017] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0021] 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."

[0022] [First Embodiment]

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

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0029] 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.

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

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a system aimed at providing effective and efficient educational support to learners who require special assistance. The system mainly consists of a server, terminals, and users (educators and parents).

[0036] First, the server centrally collects learners' personal characteristics information and stores it in a database. This information includes learners' past performance, learning style, and behavioral history. This makes it possible to accurately understand individual educational needs.

[0037] Next, the server analyzes the collected data and generates a personalized learning plan optimized for the learner. This plan includes specific learning objectives and recommended learning materials. Machine learning and statistical analysis techniques are used for the analysis.

[0038] The generated individualized learning plans are provided to educators via the device. This allows educators to provide optimal instruction to each learner. Furthermore, the server monitors the learners' daily behavioral patterns and immediately analyzes the causes of any problematic behavior.

[0039] To facilitate information sharing, users (parents) are provided with a function to report on their child's progress through a dedicated parent portal. This allows parents to stay informed about their child's learning progress and necessary support in a timely manner.

[0040] Furthermore, users (educators) can request that experts be contacted on behalf of learners as needed. The server will arrange for the transmission of appropriate data to facilitate information sharing with experts.

[0041] For example, if a learner has difficulty with reading and writing, the system analyzes this information and suggests special materials and training plans to improve their reading and writing skills. This plan is communicated to the educator, and the learner's progress is regularly reported to the parents. Through this process, the system helps educators, learners, and parents collaborate to create the optimal learning environment.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server collects learners' personal characteristics information and stores it in a database. This information may include past performance, learning style, and behavioral history. This provides a foundation for a comprehensive understanding of the learners' situation.

[0045] Step 2:

[0046] The server analyzes the collected data. This analysis uses machine learning algorithms to identify learners' strengths and weaknesses and generate individually optimized learning plans. These plans include setting learning objectives and recommending learning materials.

[0047] Step 3:

[0048] The terminal notifies the educator of the individualized learning plan provided by the server. The educator can then review the plan displayed on the terminal and prepare to provide optimal instruction to the learner.

[0049] Step 4:

[0050] The server continuously monitors learners' behavioral data and analyzes their behavioral patterns. If any abnormalities or problematic behaviors are detected, it immediately identifies the cause and prepares information to report to educators and parents.

[0051] Step 5:

[0052] Users (parents) can access the latest information on their child's learning progress and behavioral patterns through a dedicated portal. Parents can use this information to support their child's learning at home.

[0053] Step 6:

[0054] If necessary, users (educators) can request contact with experts through the system when they determine that learners require further assistance. The server prepares the relevant data for the experts and facilitates the collaboration.

[0055] (Example 1)

[0056] 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."

[0057] In today's learning environment, there is a need to efficiently provide appropriate educational support to learners who require special assistance. Traditional systems have struggled to understand the individual needs of learners and provide effective support based on those needs. Furthermore, there has been a lack of information sharing to strengthen collaboration with educators and parents and to support learners' growth. As a result, learning effectiveness has not been maximized, and learners have been unable to fully realize their potential.

[0058] 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.

[0059] In this invention, the server includes means for collecting individual information of learners, means for analyzing the individual information and generating an individualized educational plan, and means for providing the educational plan to educators and guardians. This enables optimal educational support tailored to the characteristics and needs of each learner. Furthermore, by supporting efficient learning instruction through a terminal dedicated to educators and enabling rapid information sharing through a portal dedicated to guardians, learners, educators, and guardians can work together to improve the learning process and maximize learning effectiveness.

[0060] "Individual learner information" refers to information that shows the individual characteristics of each learner, and includes data such as past performance, learning style, and behavioral history.

[0061] An "individualized learning plan" is a specific instructional plan that includes learning objectives and recommended materials, formulated based on the individual needs and characteristics of each learner.

[0062] A "device for educators" is an electronic device used by educators to efficiently instruct learners, and is a device that can receive and utilize individualized educational plans.

[0063] A "parent-only portal" is an online platform that allows parents to monitor and check details of their child's learning progress and the support provided.

[0064] "Collaboration with experts" refers to the process by which educators share information and cooperate with external experts when seeking specialized support.

[0065] This invention provides a system that offers individually optimized educational support to learners who require special assistance. The system primarily consists of a server, terminals, and users (educators and parents).

[0066] The server collects individual learner information and stores it in a database. The data collected is based on information such as the learner's past performance, learning style, and behavioral history. This information is obtained through the school's information management system and a dedicated input form. The server is equipped with a machine learning model to analyze this information and processes the data using libraries such as TENSORFLOW® and PyTorch. Through analysis, a personalized learning plan tailored to each learner is generated, which includes learning objectives and recommended materials.

[0067] The generated lesson plans are provided to educators via the device. Educators receive this information through a dedicated application, enabling them to implement optimal instruction for their students. The device also serves to provide information to parents, sharing their students' progress and necessary support in real time through a dedicated parent portal.

[0068] Educators, as users, can contact experts on behalf of their students, and the server quickly provides the necessary information to the experts. This facilitates smooth collaboration between educators and experts, enabling appropriate support for learners.

[0069] For example, if a student has difficulty with mathematics, the server collects and analyzes this information to generate learning materials and a personalized curriculum to help them understand mathematics. Educators then use this to provide instruction to the student, and the student's progress can be monitored through the parent portal. This allows educators, students, and parents to collaborate and create an optimal learning environment.

[0070] An example of a prompt to input into the generating AI model is: "For learner A, who requires special support, create an individualized learning plan based on past learning data and suggest materials to improve their mathematical skills."

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] The server collects individual learner information. Specifically, it automatically retrieves data from online forms and the school's information management system. Input at this stage includes information such as the learner's grades, learning style, and behavioral history. This data is stored in a database. The output is the organized learner data stored in the database.

[0074] Step 2:

[0075] The server performs data analysis based on the collected individual information. It uses machine learning models and processes the data via libraries such as TensorFlow and PyTorch. The input is the learner characteristic data collected in step 1. Through pattern recognition and trend analysis using machine learning, it identifies learning needs corresponding to each learner's characteristics. The output is a customized learning plan for each learner.

[0076] Step 3:

[0077] The lesson plan generated by the server is provided to the educator via a terminal. The terminal has a dedicated application for educators, through which they can review the generated plan. The input is the lesson plan output in step 2. The application converts the received lesson plan into a format easily understood by the educator and displays it on the screen. The output is the specific lesson plan provided to the educator.

[0078] Step 4:

[0079] The device reports the content of the educational plan to parents via a dedicated parent portal. The input is the specific lesson plan provided to the educator. The device allows parents to access their account to view learner progress and support information. The output is a detailed report to parents, provided through the portal.

[0080] Step 5:

[0081] The server continuously monitors learner behavior data and, if an anomaly is detected, analyzes the cause of the problematic behavior. This uses data such as behavior logs as input. Machine learning algorithms are used for anomaly detection and trend analysis. The output is feedback and suggested solutions provided to educators and users.

[0082] Step 6:

[0083] When an educator (user) needs to contact an expert, the server arranges for the necessary information to be compiled and sent to the expert. Inputs include, for example, learning plans and behavioral data compiled in text format. The server converts this data into a format easily understood by the expert and sends it via communication tools. Outputs are the detailed information provided to the expert.

[0084] (Application Example 1)

[0085] 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."

[0086] To provide optimized support plans for individuals requiring special assistance, a framework is needed to accurately grasp attribute information and promptly and appropriately notify support providers of that information. However, the current system lacks the ability to monitor the diverse behavioral tendencies of individual support recipients in real time and provide immediate support through the devices used by support providers.

[0087] 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.

[0088] In this invention, the server includes means for collecting attribute information of an individual receiving support, means for analyzing the attribute information and generating an optimized support plan, means for notifying the supporter and supervisor of the support plan, and means for assisting the support through a visual information output device carried by the supporter. This enables the provision of rapid and accurate support to the individual receiving support, as well as the detection and immediate response to abnormal behavior.

[0089] "Individualized support recipients" refer to individuals who require specific support and are eligible to receive support plans optimized based on their attribute information.

[0090] "Attribute information" refers to data that indicates the characteristics of individuals receiving individual support, and consists of information such as health status, behavioral tendencies, and historical information.

[0091] A "support plan" is a plan that includes specific action guidelines and procedures that support providers should implement, generated by analyzing the attribute information of the individual receiving support.

[0092] A "supporter" refers to a person or organization that plays a role in providing support to an individual receiving support.

[0093] A "visual information output device" is a device that a support worker can carry and use to visually display information, and is used to assist in the implementation of support plans.

[0094] "Abnormal behavior" refers to actions that deviate from the normal behavioral patterns of the individual receiving support and require special attention.

[0095] To implement this invention, it is crucial to build a system that provides optimized support plans to individual support recipients who require specific assistance. The server collects attribute information of individual support recipients and generates an optimized support plan by analyzing this data. The server uses machine learning algorithms to perform statistical analysis of the data. Specifically, it utilizes generative AI models such as TensorFlow to predict the behavioral tendencies of individual support recipients and propose effective interventions to support providers.

[0096] The server notifies the support provider and supervisor of the generated support plan. During the notification process, information is provided in real time through a visual information output device carried by the support provider. For example, when a support provider uses smart glasses to recognize the elderly person's face, the corresponding support procedure is visually displayed. Such visual information is displayed through an application built into the visual device using Flutter® or React Native.

[0097] Furthermore, the server can perform continuous monitoring and respond quickly when abnormal behavior is detected. For example, if an elderly person unexpectedly gets out of bed at night, the application can immediately sound an alarm and notify the caregiver.

[0098] As an example of a specific prompt, a text message such as, "Ms. / Mr. XX's blood sugar levels tend to drop around 3 PM. Offer them a snack and check their blood sugar levels," would appear on the caregiver's smart device. Based on this prompt, the caregiver can immediately take appropriate action.

[0099] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0100] Step 1:

[0101] The server collects attribute information of individuals receiving individual support. Inputs include health status and behavioral data of these individuals, obtained through sensors or manual input. This data is stored in a database for later analysis. The output is a categorized attribute dataset.

[0102] Step 2:

[0103] The server analyzes the collected attribute information. The input is the attribute dataset obtained in Step 1. Using a generative AI model, it predicts the behavioral tendencies and health risks of the target individuals. Here, statistical analysis and pattern recognition of the data are performed to generate an optimized support plan. The output is a specific support plan.

[0104] Step 3:

[0105] The server notifies the support provider and supervisor of the generated support plan. The input is the support plan generated in step 2. The plan is displayed in real time on a visual information output device via an app developed with Flutter or React Native. The output is the specific support procedure displayed on the support provider's smart device.

[0106] Step 4:

[0107] The server continuously monitors the behavior of individual support recipients and detects abnormal behavior. The input is behavioral data acquired in real time. Anomalies are identified by comparing the data to pre-defined criteria, and warnings are generated. The output consists of the detected abnormal behavior and warning notifications.

[0108] Step 5:

[0109] The terminal notifies the caregiver of any warnings that have occurred and prompts them to take the necessary action. The input is the warning data generated in step 4. Specifically, the visual information output device displays the warning message and instructions on how to respond. The output is the warning message displayed on the caregiver's visual device.

[0110] 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.

[0111] This invention combines a system that supports the education of learners with special needs with an emotion engine that recognizes and analyzes user emotions. The system aims to utilize emotion data to build a more effective educational system.

[0112] First, the server collects learner personal characteristics information as before and records it in a database. This includes the learner's past learning records, characteristics information, and behavioral history. Simultaneously, a newly integrated emotion engine acquires emotional information from the learner's facial expressions and voice.

[0113] The server comprehensively analyzes the acquired personal characteristics and emotional information. The emotion engine recognizes the learner's emotional state in real time and uses that data to dynamically adjust the parameters of the teaching plan. For example, if a learner shows signs of frustration, the system will either reduce the difficulty of the plan or select a new teaching method.

[0114] The device notifies educators of personalized learning plans received from the server. Educators can review the latest plan and emotional feedback displayed on the device and use it to guide learners. Information on learning plans and emotional changes is also provided to parents through a parent portal, enabling appropriate support at home.

[0115] Users (educators and parents) can refer to real-time feedback on emotional information and changes to educational plans, using this information to improve learner support strategies. Furthermore, long-term analysis of emotional data can be used to evaluate learners' emotional growth and coping with challenges, and to collaborate with experts to optimize individualized support methods.

[0116] For example, if a learner shows a clear stress response to a complex task, the system immediately analyzes the emotional information and generates a new plan with adjusted difficulty. This plan is notified to the educator and reported to the parents, ensuring an optimal learning environment for the learner. This invention makes it possible to provide nuanced educational support that utilizes learners' emotions.

[0117] The following describes the processing flow.

[0118] Step 1:

[0119] The server collects learners' personal characteristics information and stores it in a database. This data includes learning history, learning style, and various evaluation data. In addition, an emotion engine analyzes learners' facial expressions and voice tone in real time to acquire emotional data.

[0120] Step 2:

[0121] The server analyzes collected personal characteristics information and emotional data. Machine learning algorithms are used for this analysis, generating personalized learning plans based on the learner's learning needs and emotional state. These plans include setting learning objectives and adjusting them based on emotional data.

[0122] Step 3:

[0123] The terminal notifies the educator of the educational plan and sentiment analysis results provided by the server. The educator can then access the latest information through the terminal and select the most appropriate teaching method for each learner.

[0124] Step 4:

[0125] The server continuously monitors emotional data during learning and dynamically adjusts the plan if the learner shows signs of stress or frustration. This adjusted plan is then communicated to the educator via the terminal.

[0126] Step 5:

[0127] Users (parents) receive reports based on the learner's educational plan and sentiment analysis through a dedicated portal. This enables parents to appropriately support their child's learning at home.

[0128] Step 6:

[0129] The server analyzes emotional data collected over a long period to identify long-term trends in learners' emotional growth and challenges. This information is used in collaboration with educators and professionals to develop further support strategies.

[0130] (Example 2)

[0131] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0132] In educational settings for learners with special needs, there is a challenge in providing education that appropriately reflects individual emotional factors and characteristics that cannot be adequately addressed by conventional educational plans. Furthermore, there is a lack of flexible educational support tools that can respond to real-time emotional changes in the classroom.

[0133] 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.

[0134] In this invention, the server includes means for collecting learner's personal characteristics information and emotional information, means for integrating and analyzing the personal characteristics information and emotional information and generating an individualized educational plan using a generative AI model, and means for notifying educators and guardians of the educational plan. This enables dynamically adjusted educational support based on the learner's real-time emotional changes.

[0135] "Learner's personal characteristics information" refers to information specific to an individual, such as their learning history, characteristics, and behavioral history.

[0136] "Emotional information" refers to data that indicates the learner's real-time emotional state, obtained from their facial expressions and voice.

[0137] A "generative AI model" refers to a model that uses machine learning techniques to make specific judgments or predictions from input data.

[0138] An "educational plan" refers to a plan that includes individualized teaching policies and learning content tailored to the characteristics and emotions of the learners.

[0139] "Means of notification" refers to the process of using devices and communication technologies to convey educational plans and related information to educators and parents.

[0140] This invention is a system that effectively supports the education of learners who require special assistance. Specifically, it has the function of dynamically adjusting the educational plan using the learner's personal characteristics information and emotional information.

[0141] The server collects learners' personal characteristics and emotional information. For this information collection, it uses databases such as MySQL® and MongoDB. For emotional information, it utilizes the OpenCV library for facial expression analysis via camera and the Google® Cloud Speech-to-Text service for speech analysis. The server then analyzes the data acquired using these methods with generative AI models such as TensorFlow and PyTorch to generate personalized learning plans.

[0142] The device receives the educational plan from the server and notifies the educator. This is done using "ANDROID®" and "iOS" apps. The educator reviews the plan through the device and incorporates it into their instruction of the students. This information is also provided to parents through a web portal. The portal is built using frameworks such as "React" and "Vue.js".

[0143] Users (educators and parents) can provide guidance and support to learners based on the notified educational plan. In particular, by considering real-time emotional feedback, it becomes possible to provide optimal education tailored to the learner's emotional state.

[0144] For example, if a student struggling with a difficult math problem shows signs of stress, the server analyzes the emotional information and immediately generates an adjusted lesson plan. Educators can then review this plan and continue teaching in a way that is less burdensome for the student. Parents can also access this information through their devices and adjust their support at home accordingly.

[0145] Example of a prompt:

[0146] "When tackling new and complex tasks, how do you identify emotions from learners' facial expressions and voice, and adjust your plan accordingly?"

[0147] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0148] Step 1:

[0149] The server collects learner personal characteristics and emotional information. Inputs include learner facial video, audio data, and existing learning history. Specifically, it analyzes video footage acquired from the camera using the "OpenCV" library to extract facial information. It also uses the "Google Cloud Speech-to-Text" service to convert audio data into text and infer emotions. The output is a unified dataset for each learner.

[0150] Step 2:

[0151] The server integrates and analyzes collected personal characteristics and emotional information, and uses a generative AI model to generate personalized educational plans. The input is the integrated dataset obtained in Step 1. Specifically, it runs a generative AI model using "TensorFlow" or "PyTorch" to analyze emotional patterns and learning tendencies, thereby designing the optimal educational approach for each learner. The output is a dynamically adjusted educational plan for each individual learner.

[0152] Step 3:

[0153] The device receives the lesson plan sent from the server and notifies the educator. The input is the lesson plan from the server. Specifically, it displays lesson content and strategies tailored to the learner through the iOS or Android app used by the educator. The output is information that the educator can use for specific instruction.

[0154] Step 4:

[0155] Users (educators and parents) guide and support learners based on the notified educational plan. Inputs include the educational plan and recommended teaching methods viewed on the device. Specifically, educators incorporate this plan into their lessons and improve teaching methods as needed. Parents also utilize this information for support at home. Outputs are the learners' learning experiences, which are adjusted in real time.

[0156] Step 5:

[0157] The server receives feedback and additional information from users and updates the system to improve the accuracy of the educational plan. Inputs include feedback from educators and parents, as well as learner progress data. This allows the system to adjust the parameters of the generating AI model and incorporate them into future educational plans. Outputs are the updated educational plan and the sentiment analysis model.

[0158] (Application Example 2)

[0159] 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".

[0160] A problem exists where individuals with specific needs receive inadequate care because their emotional state is not taken into consideration when providing support. Furthermore, it is difficult to dynamically adjust care plans in response to changes in an individual's emotions and behavior. Therefore, it is necessary to develop a system that allows stakeholders to effectively provide support tailored to each individual.

[0161] 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.

[0162] In this invention, the server includes means for collecting individual characteristic information and emotional state; means for analyzing the characteristic information and emotional state and generating an individualized support plan; means for notifying supporters and relevant parties of the support plan; means for acquiring and analyzing emotional data in real time; and means for dynamically adjusting the plan based on the emotional state. This enables flexible support tailored to the individual's emotional state.

[0163] "Characteristic information" is a general term for information necessary for identification and understanding, such as an individual's personality, habits, and physical characteristics.

[0164] "Emotional state" refers to a temporary emotional or moodal state exhibited by an individual, which is usually recognized through facial expressions, voice, and behavior.

[0165] A "support plan" is a plan created to propose services and care methods suitable for an individual, and is personalized based on the individual's characteristics and condition.

[0166] A "server" refers to a device or software system connected to a network that processes data and provides applications.

[0167] A "supporter" refers to an individual or group that plays a role in providing care and support to an individual.

[0168] "Stakeholders" refers to individuals or organizations that directly or indirectly affect the care or life of an individual.

[0169] "Real-time" refers to a situation where data and information are processed or updated almost instantly with virtually no delay.

[0170] This invention is a system that provides personalized support plans to individuals who require specific assistance, based on their characteristic information and emotional state. The server collects individual characteristic information and emotional state in real time through devices equipped with cameras and microphones, such as smartphones and tablets. This information is processed using an emotion analysis engine to generate personalized support plans.

[0171] The software used will consist of a frontend built with React Native for the application interface and a backend server environment using Python's Flask. TensorFlow and OpenCV will be used for emotion analysis. This will allow for the analysis of facial expressions from the camera and audio data from the microphone to identify emotional states.

[0172] The server generates an optimized support plan based on the analyzed information and notifies support providers and stakeholders. This notification is made through specific applications or stakeholder portals, allowing support providers to review the plan as needed and provide the most appropriate support for each individual.

[0173] For example, if an elderly person shows signs of stress during a daily activity, the system will detect this and suggest a relaxing environment. An example of this prompt might be, "If the elderly person is feeling stressed, generate suggestions to help them relax."

[0174] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0175] Step 1:

[0176] The device uses the camera and microphone of a smartphone or tablet to capture the individual's facial expressions and voice data. During this process, facial expression information is input as image data, and voice tone is input as voice data. This data is then prepared for transmission to the server in real time.

[0177] Step 2:

[0178] The server receives facial and audio data transmitted from the terminal in real time. Next, it analyzes the received data using TensorFlow and OpenCV to process it and estimate the emotional state. This results in the output of the current emotional state (e.g., joy, surprise, sadness).

[0179] Step 3:

[0180] The server integrates the analyzed emotional state with pre-collected feature information and generates an optimized support plan using a generative AI model. The inputs are emotional states and feature information, while the output is a personalized support plan.

[0181] Step 4:

[0182] The server notifies the terminal of the generated support plan and provides it to supporters and stakeholders via the stakeholders portal. Notification data is entered, and based on that, guidance information on how supporters should respond is output.

[0183] Step 5:

[0184] The user reviews the support plan provided via the device and applies it to actual care. The device assists in providing more appropriate care by clearly indicating the actions required for the caregiver. At this time, the user receives the proposed plan as input and outputs the care methods as actual actions.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Second Embodiment]

[0189] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0190] 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.

[0191] 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).

[0192] 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.

[0193] 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.

[0194] 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).

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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".

[0201] This invention is a system aimed at providing effective and efficient educational support to learners who require special assistance. The system mainly consists of a server, terminals, and users (educators and parents).

[0202] First, the server centrally collects learners' personal characteristics information and stores it in a database. This information includes learners' past performance, learning style, and behavioral history. This makes it possible to accurately understand individual educational needs.

[0203] Next, the server analyzes the collected data and generates a personalized learning plan optimized for the learner. This plan includes specific learning objectives and recommended learning materials. Machine learning and statistical analysis techniques are used for the analysis.

[0204] The generated individualized learning plans are provided to educators via the device. This allows educators to provide optimal instruction for each learner. Furthermore, the server monitors the learners' daily behavioral patterns and immediately analyzes the causes of any problematic behavior.

[0205] To facilitate information sharing, users (parents) are provided with a function to report on their child's progress through a dedicated parent portal. This allows parents to stay informed about their child's learning progress and any support needed in a timely manner.

[0206] Furthermore, users (educators) can request that experts be contacted on behalf of learners as needed. The server will arrange for the transmission of appropriate data to facilitate information sharing with experts.

[0207] For example, if a learner has difficulty with reading and writing, the system analyzes this information and suggests special materials and training plans to improve their reading and writing skills. This plan is communicated to the educator, and the learner's progress is regularly reported to the parents. Through this process, the system helps educators, learners, and parents collaborate to create the optimal learning environment.

[0208] The following describes the processing flow.

[0209] Step 1:

[0210] The server collects learners' personal characteristics information and stores it in a database. This information may include past performance, learning style, and behavioral history. This provides a foundation for a comprehensive understanding of the learners' situation.

[0211] Step 2:

[0212] The server analyzes the collected data. This analysis uses machine learning algorithms to identify learners' strengths and weaknesses and generate individually optimized learning plans. These plans include setting learning objectives and recommending learning materials.

[0213] Step 3:

[0214] The terminal notifies the educator of the individualized learning plan provided by the server. The educator can then review the plan displayed on the terminal and prepare to provide optimal instruction to the learner.

[0215] Step 4:

[0216] The server continuously monitors learners' behavioral data and analyzes their behavioral patterns. If any abnormalities or problematic behaviors are detected, it immediately identifies the cause and prepares information to report to educators and parents.

[0217] Step 5:

[0218] Users (parents) can access the latest information on their child's learning progress and behavioral patterns through a dedicated portal. Parents can use this information to support their child's learning at home.

[0219] Step 6:

[0220] If necessary, users (educators) can request contact with experts through the system when they determine that learners require further assistance. The server prepares the relevant data for the experts and facilitates the collaboration.

[0221] (Example 1)

[0222] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0223] In today's learning environment, there is a need to efficiently provide appropriate educational support to learners who require special assistance. Traditional systems have struggled to understand the individual needs of learners and provide effective support based on those needs. Furthermore, there has been a lack of information sharing to strengthen collaboration with educators and parents and to support learners' growth. As a result, learning effectiveness has not been maximized, and learners have been unable to fully realize their potential.

[0224] 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.

[0225] In this invention, the server includes means for collecting individual information of learners, means for analyzing the individual information and generating an individualized educational plan, and means for providing the educational plan to educators and guardians. This enables optimal educational support tailored to the characteristics and needs of each learner. Furthermore, by supporting efficient learning instruction through a terminal dedicated to educators and enabling rapid information sharing through a portal dedicated to guardians, learners, educators, and guardians can work together to improve the learning process and maximize learning effectiveness.

[0226] "Individual learner information" refers to information that shows the individual characteristics of each learner, and includes data such as past performance, learning style, and behavioral history.

[0227] An "individualized learning plan" is a specific instructional plan that includes learning objectives and recommended materials, formulated based on the individual needs and characteristics of each learner.

[0228] A "device for educators" is an electronic device used by educators to efficiently instruct learners, and is a device that can receive and utilize individualized educational plans.

[0229] A "parent-only portal" is an online platform that allows parents to monitor and check details of their child's learning progress and the support provided.

[0230] "Collaboration with experts" refers to the process by which educators share information and cooperate with external experts when seeking specialized support.

[0231] This invention provides a system that offers individually optimized educational support to learners who require special assistance. The system primarily consists of a server, terminals, and users (educators and parents).

[0232] The server collects individual learner information and stores it in a database. The data collected is based on information such as the learner's past performance, learning style, and behavioral history. This information is obtained through the school's information management system and a dedicated input form. The server is equipped with a machine learning model to analyze this information and processes the data using libraries such as TensorFlow and PyTorch. Through analysis, a personalized learning plan tailored to each learner is generated, which includes learning objectives and recommended materials.

[0233] The generated lesson plans are provided to educators via the device. Educators receive this information through a dedicated application, enabling them to implement optimal instruction for their students. The device also serves to provide information to parents, sharing their students' progress and necessary support in real time through a dedicated parent portal.

[0234] Educators, as users, can contact experts on behalf of their students, and the server quickly provides the necessary information to the experts. This facilitates smooth collaboration between educators and experts, enabling appropriate support for learners.

[0235] For example, if a student has difficulty with mathematics, the server collects and analyzes this information to generate learning materials and a personalized curriculum to help them understand mathematics. Educators then use this to provide instruction to the student, and the student's progress can be monitored through the parent portal. This allows educators, students, and parents to collaborate and create an optimal learning environment.

[0236] An example of a prompt to input into the generating AI model is: "For learner A, who requires special support, create an individualized learning plan based on past learning data and suggest materials to improve their mathematical skills."

[0237] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0238] Step 1:

[0239] The server collects individual learner information. Specifically, it automatically retrieves data from online forms and the school's information management system. Input at this stage includes information such as the learner's grades, learning style, and behavioral history. This data is stored in a database. The output is the organized learner data stored in the database.

[0240] Step 2:

[0241] The server performs data analysis based on the collected individual information. It uses machine learning models and processes the data via libraries such as TensorFlow and PyTorch. The input is the learner characteristic data collected in step 1. Through pattern recognition and trend analysis using machine learning, it identifies learning needs corresponding to each learner's characteristics. The output is a customized learning plan for each learner.

[0242] Step 3:

[0243] The lesson plan generated by the server is provided to the educator via a terminal. The terminal has a dedicated application for educators, through which they can review the generated plan. The input is the lesson plan output in step 2. The application converts the received lesson plan into a format easily understood by the educator and displays it on the screen. The output is the specific lesson plan provided to the educator.

[0244] Step 4:

[0245] The device reports the content of the educational plan to parents via a dedicated parent portal. The input is the specific lesson plan provided to the educator. The device allows parents to access their account to view learner progress and support information. The output is a detailed report to parents, provided through the portal.

[0246] Step 5:

[0247] The server continuously monitors learner behavior data and, if an anomaly is detected, analyzes the cause of the problematic behavior. This uses data such as behavior logs as input. Machine learning algorithms are used for anomaly detection and trend analysis. The output is feedback and suggested solutions provided to educators and users.

[0248] Step 6:

[0249] When an educator (user) needs to contact an expert, the server arranges for the necessary information to be compiled and sent to the expert. Inputs include, for example, learning plans and behavioral data compiled in text format. The server converts this data into a format easily understood by the expert and sends it via communication tools. Outputs are the detailed information provided to the expert.

[0250] (Application Example 1)

[0251] 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."

[0252] To provide optimized support plans for individuals requiring special assistance, a framework is needed to accurately grasp attribute information and promptly and appropriately notify support providers of that information. However, the current system lacks the ability to monitor the diverse behavioral tendencies of individual support recipients in real time and provide immediate support through the devices used by support providers.

[0253] 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.

[0254] In this invention, the server includes means for collecting attribute information of an individual receiving support, means for analyzing the attribute information and generating an optimized support plan, means for notifying the supporter and supervisor of the support plan, and means for assisting the support through a visual information output device carried by the supporter. This enables the provision of rapid and accurate support to the individual receiving support, as well as the detection and immediate response to abnormal behavior.

[0255] "Individualized support recipients" refer to individuals who require specific support and are eligible to receive support plans optimized based on their attribute information.

[0256] "Attribute information" refers to data that indicates the characteristics of individuals receiving individual support, and consists of information such as health status, behavioral tendencies, and historical information.

[0257] A "support plan" is a plan that includes specific action guidelines and procedures that support providers should implement, generated by analyzing the attribute information of the individual receiving support.

[0258] A "supporter" refers to a person or organization that plays a role in providing support to an individual receiving support.

[0259] A "visual information output device" is a device that a support worker can carry and use to visually display information, and is used to assist in the implementation of support plans.

[0260] "Abnormal behavior" refers to actions that deviate from the normal behavioral patterns of the individual receiving support and require special attention.

[0261] To implement this invention, it is crucial to build a system that provides optimized support plans to individual support recipients who require specific assistance. The server collects attribute information of individual support recipients and generates an optimized support plan by analyzing this data. The server uses machine learning algorithms to perform statistical analysis of the data. Specifically, it utilizes generative AI models such as TensorFlow to predict the behavioral tendencies of individual support recipients and propose effective interventions to support providers.

[0262] The server notifies the support provider and supervisor of the generated support plan. During the notification process, information is provided in real time through a visual information output device carried by the support provider. For example, using smart glasses, when the support provider recognizes the elderly person's face, the corresponding support procedure is visually displayed. This visual information is displayed through an application built into the visual device using Flutter or React Native.

[0263] Furthermore, the server can perform continuous monitoring and respond quickly when abnormal behavior is detected. For example, if an elderly person unexpectedly gets out of bed at night, the application can immediately sound an alarm and notify the caregiver.

[0264] As an example of a specific prompt, a text message such as, "Ms. / Mr. XX's blood sugar levels tend to drop around 3 PM. Offer them a snack and check their blood sugar levels," would appear on the caregiver's smart device. Based on this prompt, the caregiver can immediately take appropriate action.

[0265] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0266] Step 1:

[0267] The server collects attribute information of individuals receiving individual support. Inputs include health status and behavioral data of these individuals, obtained through sensors or manual input. This data is stored in a database for later analysis. The output is a categorized attribute dataset.

[0268] Step 2:

[0269] The server analyzes the collected attribute information. The input is the attribute dataset obtained in Step 1. Using a generative AI model, it predicts the behavioral tendencies and health risks of the target individuals. Here, statistical analysis and pattern recognition of the data are performed to generate an optimized support plan. The output is a specific support plan.

[0270] Step 3:

[0271] The server notifies the support provider and supervisor of the generated support plan. The input is the support plan generated in step 2. The plan is displayed in real time on a visual information output device via an app developed with Flutter or React Native. The output is the specific support procedure displayed on the support provider's smart device.

[0272] Step 4:

[0273] The server continuously monitors the behavior of individual support recipients and detects abnormal behavior. The input is behavioral data acquired in real time. Anomalies are identified by comparing the data to pre-defined criteria, and warnings are generated. The output consists of the detected abnormal behavior and warning notifications.

[0274] Step 5:

[0275] The terminal notifies the caregiver of any warnings that have occurred and prompts them to take the necessary action. The input is the warning data generated in step 4. Specifically, the visual information output device displays the warning message and instructions on how to respond. The output is the warning message displayed on the caregiver's visual device.

[0276] 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.

[0277] This invention combines a system that supports the education of learners with special needs with an emotion engine that recognizes and analyzes user emotions. The system aims to utilize emotion data to build a more effective educational system.

[0278] First, the server collects learner personal characteristics information as before and records it in a database. This includes the learner's past learning records, characteristics information, and behavioral history. Simultaneously, a newly integrated emotion engine acquires emotional information from the learner's facial expressions and voice.

[0279] The server comprehensively analyzes the acquired personal characteristics and emotional information. The emotion engine recognizes the learner's emotional state in real time and uses that data to dynamically adjust the parameters of the teaching plan. For example, if a learner shows signs of frustration, the system will either reduce the difficulty of the plan or select a new teaching method.

[0280] The device notifies educators of personalized learning plans received from the server. Educators can review the latest plan and emotional feedback displayed on the device and use it to guide learners. Information on learning plans and emotional changes is also provided to parents through a parent portal, enabling appropriate support at home.

[0281] Users (educators or guardians) can refer to the real-time feedback on emotional information and changes to the education plan, which can be used as materials to improve the support strategies for learners. Additionally, the long-term analysis results of emotional data are also used as information when collaborating with experts to evaluate the emotional growth of learners and their responses to challenges, and to optimize individual support methods.

[0282] For example, if a learner shows an obvious stress response to a complex task, the system immediately analyzes the emotional information and generates a new plan with adjusted difficulty levels. This plan is notified to the educator and also reported to the guardian, thus creating an optimal educational environment for the learner. The present invention enables detailed educational support that utilizes the emotions of learners.

[0283] The following describes the processing flow.

[0284] Step 1:

[0285] The server collects the personal characteristic information of the learner and stores it in the database. These data include learning history, learning style, and various evaluation data. Additionally, the emotion engine analyzes the learner's facial expressions and vocal tones in real time to obtain emotional data.

[0286] Step 2:

[0287] The server analyzes the collected personal characteristic information and emotional data. As an analysis method, a machine learning algorithm is used to generate an individualized education plan based on the learning needs and emotional state of the learner. This plan includes setting learning goals and adjusting the plan based on emotional data.

[0288] Step 3:

[0289] The terminal notifies the educator of the education plan and the emotional analysis results provided by the server. The educator can check the latest information through the terminal and select the optimal guidance method for the learner.

[0290] Step 4:

[0291] The server continuously monitors emotional data during learning and dynamically adjusts the plan if the learner shows signs of stress or frustration. This adjusted plan is then communicated to the educator via the terminal.

[0292] Step 5:

[0293] Users (parents) receive reports based on the learner's educational plan and sentiment analysis through a dedicated portal. This enables parents to appropriately support their child's learning at home.

[0294] Step 6:

[0295] The server analyzes emotional data collected over a long period to identify long-term trends in learners' emotional growth and challenges. This information is used in collaboration with educators and professionals to develop further support strategies.

[0296] (Example 2)

[0297] 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".

[0298] In educational settings for learners with special needs, there is a challenge in providing education that appropriately reflects individual emotional factors and characteristics that cannot be adequately addressed by conventional educational plans. Furthermore, there is a lack of flexible educational support tools that can respond to real-time emotional changes in the classroom.

[0299] 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.

[0300] In this invention, the server includes means for collecting learner's personal characteristics information and emotional information, means for integrating and analyzing the personal characteristics information and emotional information and generating an individualized educational plan using a generative AI model, and means for notifying educators and guardians of the educational plan. This enables dynamically adjusted educational support based on the learner's real-time emotional changes.

[0301] "Learner's personal characteristics information" refers to information specific to an individual, such as their learning history, characteristics, and behavioral history.

[0302] "Emotional information" refers to data that indicates the learner's real-time emotional state, obtained from their facial expressions and voice.

[0303] A "generative AI model" refers to a model that uses machine learning techniques to make specific judgments or predictions from input data.

[0304] An "educational plan" refers to a plan that includes individualized teaching policies and learning content tailored to the characteristics and emotions of the learners.

[0305] "Means of notification" refers to the process of using devices and communication technologies to convey educational plans and related information to educators and parents.

[0306] This invention is a system that effectively supports the education of learners who require special assistance. Specifically, it has the function of dynamically adjusting the educational plan using the learner's personal characteristics information and emotional information.

[0307] The server collects learners' personal characteristics and emotional information. MySQL and MongoDB are used as databases for this information collection. Emotional information is analyzed using the OpenCV library for facial expression analysis via camera, and the Google Cloud Speech-to-Text service is used for speech analysis. The server then analyzes the data acquired using generative AI models such as TensorFlow and PyTorch to generate personalized learning plans.

[0308] The terminal receives an education plan from the server and notifies the educator. For this, "Android" or "iOS" apps are used. The educator checks the content of the plan through the terminal and reflects it in the guidance for the learner. This information is also provided to the guardians through a web portal for guardians. The portal is built using frameworks such as "React" and "Vue.js".

[0309] Users (educators and guardians) can provide guidance and support to learners based on the notified education plan. In particular, by considering real-time emotional feedback, it becomes possible to provide optimal education according to the emotional state of the learner.

[0310] As a specific example, when a learner who is working on a difficult math problem shows stress, the server analyzes the emotional information and immediately generates an education plan with adjusted difficulty. The educator can confirm this and continue the guidance in a way that places less burden on the learner. The guardians can also adjust the support within the family by obtaining this information through the terminal.

[0311] Example of a prompt sentence:

[0312] "When dealing with a new and complex task, how can you identify emotions from the learner's expression and voice and appropriately adjust the plan?"

[0313] The flow of the specific process in Example 2 will be described using FIG. 13.

[0314] Step 1:

[0315] The server collects learner personal characteristics and emotional information. Inputs include learner facial video, audio data, and existing learning history. Specifically, it analyzes video footage acquired from the camera using the "OpenCV" library to extract facial information. It also uses the "Google Cloud Speech-to-Text" service to convert audio data into text and infer emotions. The output is a unified dataset for each learner.

[0316] Step 2:

[0317] The server integrates and analyzes collected personal characteristics and emotional information, and uses a generative AI model to generate personalized educational plans. The input is the integrated dataset obtained in Step 1. Specifically, it runs a generative AI model using "TensorFlow" or "PyTorch" to analyze emotional patterns and learning tendencies, thereby designing the optimal educational approach for each learner. The output is a dynamically adjusted educational plan for each individual learner.

[0318] Step 3:

[0319] The device receives the lesson plan sent from the server and notifies the educator. The input is the lesson plan from the server. Specifically, it displays lesson content and strategies tailored to the learner through the iOS or Android app used by the educator. The output is information that the educator can use for specific instruction.

[0320] Step 4:

[0321] Users (educators and parents) guide and support learners based on the notified educational plan. Inputs include the educational plan and recommended teaching methods viewed on the device. Specifically, educators incorporate this plan into their lessons and improve teaching methods as needed. Parents also utilize this information for support at home. Outputs are the learners' learning experiences, which are adjusted in real time.

[0322] Step 5:

[0323] The server receives feedback and additional information from users and updates the system to improve the accuracy of the educational plan. Inputs include feedback from educators and parents, as well as learner progress data. This allows the system to adjust the parameters of the generating AI model and incorporate them into future educational plans. Outputs are the updated educational plan and the sentiment analysis model.

[0324] (Application Example 2)

[0325] 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".

[0326] A problem exists where individuals with specific needs receive inadequate care because their emotional state is not taken into consideration when providing support. Furthermore, it is difficult to dynamically adjust care plans in response to changes in an individual's emotions and behavior. Therefore, it is necessary to develop a system that allows stakeholders to effectively provide support tailored to each individual.

[0327] 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.

[0328] In this invention, the server includes means for collecting individual characteristic information and emotional state; means for analyzing the characteristic information and emotional state and generating an individualized support plan; means for notifying supporters and relevant parties of the support plan; means for acquiring and analyzing emotional data in real time; and means for dynamically adjusting the plan based on the emotional state. This enables flexible support tailored to the individual's emotional state.

[0329] "Characteristic information" is a general term for information necessary for identification and understanding, such as an individual's personality, habits, and physical characteristics.

[0330] "Emotional state" refers to a temporary emotional or moodal state exhibited by an individual, which is usually recognized through facial expressions, voice, and behavior.

[0331] A "support plan" is a plan created to propose services and care methods suitable for an individual, and is personalized based on the individual's characteristics and condition.

[0332] A "server" refers to a device or software system connected to a network that processes data and provides applications.

[0333] A "supporter" refers to an individual or group that plays a role in providing care and support to an individual.

[0334] "Stakeholders" refers to individuals or organizations that directly or indirectly affect the care or life of an individual.

[0335] "Real-time" refers to a situation where data and information are processed or updated almost instantly with virtually no delay.

[0336] This invention is a system that provides personalized support plans to individuals who require specific assistance, based on their characteristic information and emotional state. The server collects individual characteristic information and emotional state in real time through devices equipped with cameras and microphones, such as smartphones and tablets. This information is processed using an emotion analysis engine to generate personalized support plans.

[0337] The software used will consist of a frontend built with React Native for the application interface and a backend server environment using Python's Flask. TensorFlow and OpenCV will be used for emotion analysis. This will allow for the analysis of facial expressions from the camera and audio data from the microphone to identify emotional states.

[0338] The server generates an optimized support plan based on the analyzed information and notifies support providers and stakeholders. This notification is made through specific applications or stakeholder portals, allowing support providers to review the plan as needed and provide the most appropriate support for each individual.

[0339] For example, if an elderly person shows signs of stress during a daily activity, the system will detect this and suggest a relaxing environment. An example of this prompt might be, "If the elderly person is feeling stressed, generate suggestions to help them relax."

[0340] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0341] Step 1:

[0342] The device uses the camera and microphone of a smartphone or tablet to capture the individual's facial expressions and voice data. During this process, facial expression information is input as image data, and voice tone is input as voice data. This data is then prepared for transmission to the server in real time.

[0343] Step 2:

[0344] The server receives facial and audio data transmitted from the terminal in real time. Next, it analyzes the received data using TensorFlow and OpenCV to process it and estimate the emotional state. This results in the output of the current emotional state (e.g., joy, surprise, sadness).

[0345] Step 3:

[0346] The server integrates the analyzed emotional state with pre-collected feature information and generates an optimized support plan using a generative AI model. The inputs are emotional states and feature information, while the output is a personalized support plan.

[0347] Step 4:

[0348] The server notifies the terminal of the generated support plan and provides it to supporters and stakeholders via the stakeholders portal. Notification data is entered, and based on that, guidance information on how supporters should respond is output.

[0349] Step 5:

[0350] The user reviews the support plan provided via the device and applies it to actual care. The device assists in providing more appropriate care by clearly indicating the actions required for the caregiver. At this time, the user receives the proposed plan as input and outputs the care methods as actual actions.

[0351] 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.

[0352] 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.

[0353] 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.

[0354] [Third Embodiment]

[0355] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0356] 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.

[0357] 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).

[0358] 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.

[0359] 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.

[0360] 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).

[0361] 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.

[0362] 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.

[0363] 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.

[0364] 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.

[0365] 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.

[0366] 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".

[0367] This invention is a system aimed at providing effective and efficient educational support to learners who require special assistance. The system mainly consists of a server, terminals, and users (educators and parents).

[0368] First, the server centrally collects learners' personal characteristics information and stores it in a database. This information includes learners' past performance, learning style, and behavioral history. This makes it possible to accurately understand individual educational needs.

[0369] Next, the server analyzes the collected data and generates a personalized learning plan optimized for the learner. This plan includes specific learning objectives and recommended learning materials. Machine learning and statistical analysis techniques are used for the analysis.

[0370] The generated individualized learning plans are provided to educators via the device. This allows educators to provide optimal instruction for each learner. Furthermore, the server monitors the learners' daily behavioral patterns and immediately analyzes the causes of any problematic behavior.

[0371] To facilitate information sharing, users (parents) are provided with a function to report on their child's progress through a dedicated parent portal. This allows parents to stay informed about their child's learning progress and any support needed in a timely manner.

[0372] Furthermore, users (educators) can request that experts be contacted on behalf of learners as needed. The server will arrange for the transmission of appropriate data to facilitate information sharing with experts.

[0373] For example, if a learner has difficulty with reading and writing, the system analyzes this information and suggests special materials and training plans to improve their reading and writing skills. This plan is communicated to the educator, and the learner's progress is regularly reported to the parents. Through this process, the system helps educators, learners, and parents collaborate to create the optimal learning environment.

[0374] The following describes the processing flow.

[0375] Step 1:

[0376] The server collects learners' personal characteristics information and stores it in a database. This information may include past performance, learning style, and behavioral history. This provides a foundation for a comprehensive understanding of the learners' situation.

[0377] Step 2:

[0378] The server analyzes the collected data. This analysis uses machine learning algorithms to identify learners' strengths and weaknesses and generate individually optimized learning plans. These plans include setting learning objectives and recommending learning materials.

[0379] Step 3:

[0380] The terminal notifies the educator of the individualized learning plan provided by the server. The educator can then review the plan displayed on the terminal and prepare to provide optimal instruction to the learner.

[0381] Step 4:

[0382] The server continuously monitors learners' behavioral data and analyzes their behavioral patterns. If any abnormalities or problematic behaviors are detected, it immediately identifies the cause and prepares information to report to educators and parents.

[0383] Step 5:

[0384] Users (parents) can access the latest information on their child's learning progress and behavioral patterns through a dedicated portal. Parents can use this information to support their child's learning at home.

[0385] Step 6:

[0386] If necessary, users (educators) can request contact with experts through the system when they determine that learners require further assistance. The server prepares the relevant data for the experts and facilitates the collaboration.

[0387] (Example 1)

[0388] 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."

[0389] In today's learning environment, there is a need to efficiently provide appropriate educational support to learners who require special assistance. Traditional systems have struggled to understand the individual needs of learners and provide effective support based on those needs. Furthermore, there has been a lack of information sharing to strengthen collaboration with educators and parents and to support learners' growth. As a result, learning effectiveness has not been maximized, and learners have been unable to fully realize their potential.

[0390] 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.

[0391] In this invention, the server includes means for collecting individual information of learners, means for analyzing the individual information and generating an individualized educational plan, and means for providing the educational plan to educators and guardians. This enables optimal educational support tailored to the characteristics and needs of each learner. Furthermore, by supporting efficient learning instruction through a terminal dedicated to educators and enabling rapid information sharing through a portal dedicated to guardians, learners, educators, and guardians can work together to improve the learning process and maximize learning effectiveness.

[0392] "Individual learner information" refers to information that shows the individual characteristics of each learner, and includes data such as past performance, learning style, and behavioral history.

[0393] An "individualized learning plan" is a specific instructional plan that includes learning objectives and recommended materials, formulated based on the individual needs and characteristics of each learner.

[0394] A "device for educators" is an electronic device used by educators to efficiently instruct learners, and is a device that can receive and utilize individualized educational plans.

[0395] A "parent-only portal" is an online platform that allows parents to monitor and check details of their child's learning progress and the support provided.

[0396] "Collaboration with experts" refers to the process by which educators share information and cooperate with external experts when seeking specialized support.

[0397] This invention provides a system that offers individually optimized educational support to learners who require special assistance. The system primarily consists of a server, terminals, and users (educators and parents).

[0398] The server collects individual learner information and stores it in a database. The data collected is based on information such as the learner's past performance, learning style, and behavioral history. This information is obtained through the school's information management system and a dedicated input form. The server is equipped with a machine learning model to analyze this information and processes the data using libraries such as TensorFlow and PyTorch. Through analysis, a personalized learning plan tailored to each learner is generated, which includes learning objectives and recommended materials.

[0399] The generated lesson plans are provided to educators via the device. Educators receive this information through a dedicated application, enabling them to implement optimal instruction for their students. The device also serves to provide information to parents, sharing their students' progress and necessary support in real time through a dedicated parent portal.

[0400] Educators, as users, can contact experts on behalf of their students, and the server quickly provides the necessary information to the experts. This facilitates smooth collaboration between educators and experts, enabling appropriate support for learners.

[0401] For example, if a student has difficulty with mathematics, the server collects and analyzes this information to generate learning materials and a personalized curriculum to help them understand mathematics. Educators then use this to provide instruction to the student, and the student's progress can be monitored through the parent portal. This allows educators, students, and parents to collaborate and create an optimal learning environment.

[0402] An example of a prompt to input into the generating AI model is: "For learner A, who requires special support, create an individualized learning plan based on past learning data and suggest materials to improve their mathematical skills."

[0403] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0404] Step 1:

[0405] The server collects individual learner information. Specifically, it automatically retrieves data from online forms and the school's information management system. Input at this stage includes information such as the learner's grades, learning style, and behavioral history. This data is stored in a database. The output is the organized learner data stored in the database.

[0406] Step 2:

[0407] The server performs data analysis based on the collected individual information. It uses machine learning models and processes the data via libraries such as TensorFlow and PyTorch. The input is the learner characteristic data collected in step 1. Through pattern recognition and trend analysis using machine learning, it identifies learning needs corresponding to each learner's characteristics. The output is a customized learning plan for each learner.

[0408] Step 3:

[0409] The lesson plan generated by the server is provided to the educator via a terminal. The terminal has a dedicated application for educators, through which they can review the generated plan. The input is the lesson plan output in step 2. The application converts the received lesson plan into a format easily understood by the educator and displays it on the screen. The output is the specific lesson plan provided to the educator.

[0410] Step 4:

[0411] The device reports the content of the educational plan to parents via a dedicated parent portal. The input is the specific lesson plan provided to the educator. The device allows parents to access their account to view learner progress and support information. The output is a detailed report to parents, provided through the portal.

[0412] Step 5:

[0413] The server continuously monitors learner behavior data and, if an anomaly is detected, analyzes the cause of the problematic behavior. This uses data such as behavior logs as input. Machine learning algorithms are used for anomaly detection and trend analysis. The output is feedback and suggested solutions provided to educators and users.

[0414] Step 6:

[0415] When an educator (user) needs to contact an expert, the server arranges for the necessary information to be compiled and sent to the expert. Inputs include, for example, learning plans and behavioral data compiled in text format. The server converts this data into a format easily understood by the expert and sends it via communication tools. Outputs are the detailed information provided to the expert.

[0416] (Application Example 1)

[0417] 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."

[0418] To provide optimized support plans for individuals requiring special assistance, a framework is needed to accurately grasp attribute information and promptly and appropriately notify support providers of that information. However, the current system lacks the ability to monitor the diverse behavioral tendencies of individual support recipients in real time and provide immediate support through the devices used by support providers.

[0419] 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.

[0420] In this invention, the server includes means for collecting attribute information of an individual receiving support, means for analyzing the attribute information and generating an optimized support plan, means for notifying the supporter and supervisor of the support plan, and means for assisting the support through a visual information output device carried by the supporter. This enables the provision of rapid and accurate support to the individual receiving support, as well as the detection and immediate response to abnormal behavior.

[0421] "Individualized support recipients" refer to individuals who require specific support and are eligible to receive support plans optimized based on their attribute information.

[0422] "Attribute information" refers to data that indicates the characteristics of individuals receiving individual support, and consists of information such as health status, behavioral tendencies, and historical information.

[0423] A "support plan" is a plan that includes specific action guidelines and procedures that support providers should implement, generated by analyzing the attribute information of the individual receiving support.

[0424] A "supporter" refers to a person or organization that plays a role in providing support to an individual receiving support.

[0425] A "visual information output device" is a device that a support worker can carry and use to visually display information, and is used to assist in the implementation of support plans.

[0426] "Abnormal behavior" refers to actions that deviate from the normal behavioral patterns of the individual receiving support and require special attention.

[0427] To implement this invention, it is crucial to build a system that provides optimized support plans to individual support recipients who require specific assistance. The server collects attribute information of individual support recipients and generates an optimized support plan by analyzing this data. The server uses machine learning algorithms to perform statistical analysis of the data. Specifically, it utilizes generative AI models such as TensorFlow to predict the behavioral tendencies of individual support recipients and propose effective interventions to support providers.

[0428] The server notifies the support provider and supervisor of the generated support plan. During the notification process, information is provided in real time through a visual information output device carried by the support provider. For example, using smart glasses, when the support provider recognizes the elderly person's face, the corresponding support procedure is visually displayed. This visual information is displayed through an application built into the visual device using Flutter or React Native.

[0429] Furthermore, the server can perform continuous monitoring and respond quickly when abnormal behavior is detected. For example, if an elderly person unexpectedly gets out of bed at night, the application can immediately sound an alarm and notify the caregiver.

[0430] As an example of a specific prompt, a text message such as, "Ms. / Mr. XX's blood sugar levels tend to drop around 3 PM. Offer them a snack and check their blood sugar levels," would appear on the caregiver's smart device. Based on this prompt, the caregiver can immediately take appropriate action.

[0431] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0432] Step 1:

[0433] The server collects attribute information of individuals receiving individual support. Inputs include health status and behavioral data of these individuals, obtained through sensors or manual input. This data is stored in a database for later analysis. The output is a categorized attribute dataset.

[0434] Step 2:

[0435] The server analyzes the collected attribute information. The input is the attribute dataset obtained in Step 1. Using a generative AI model, it predicts the behavioral tendencies and health risks of the target individuals. Here, statistical analysis and pattern recognition of the data are performed to generate an optimized support plan. The output is a specific support plan.

[0436] Step 3:

[0437] The server notifies the support provider and supervisor of the generated support plan. The input is the support plan generated in step 2. The plan is displayed in real time on a visual information output device via an app developed with Flutter or React Native. The output is the specific support procedure displayed on the support provider's smart device.

[0438] Step 4:

[0439] The server continuously monitors the behavior of individual support recipients and detects abnormal behavior. The input is behavioral data acquired in real time. Anomalies are identified by comparing the data to pre-defined criteria, and warnings are generated. The output consists of the detected abnormal behavior and warning notifications.

[0440] Step 5:

[0441] The terminal notifies the caregiver of any warnings that have occurred and prompts them to take the necessary action. The input is the warning data generated in step 4. Specifically, the visual information output device displays the warning message and instructions on how to respond. The output is the warning message displayed on the caregiver's visual device.

[0442] 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.

[0443] This invention combines a system that supports the education of learners with special needs with an emotion engine that recognizes and analyzes user emotions. The system aims to utilize emotion data to build a more effective educational system.

[0444] First, the server collects learner personal characteristics information as before and records it in a database. This includes the learner's past learning records, characteristics information, and behavioral history. Simultaneously, a newly integrated emotion engine acquires emotional information from the learner's facial expressions and voice.

[0445] The server comprehensively analyzes the acquired personal characteristics and emotional information. The emotion engine recognizes the learner's emotional state in real time and uses that data to dynamically adjust the parameters of the teaching plan. For example, if a learner shows signs of frustration, the system will either reduce the difficulty of the plan or select a new teaching method.

[0446] The device notifies educators of personalized learning plans received from the server. Educators can review the latest plan and emotional feedback displayed on the device and use it to guide learners. Information on learning plans and emotional changes is also provided to parents through a parent portal, enabling appropriate support at home.

[0447] Users (educators and parents) can refer to real-time feedback on emotional information and changes to educational plans, using this information to improve learner support strategies. Furthermore, long-term analysis of emotional data can be used to evaluate learners' emotional growth and coping with challenges, and to collaborate with experts to optimize individualized support methods.

[0448] For example, if a learner shows a clear stress response to a complex task, the system immediately analyzes the emotional information and generates a new plan with adjusted difficulty. This plan is notified to the educator and reported to the parents, ensuring an optimal learning environment for the learner. This invention makes it possible to provide nuanced educational support that utilizes learners' emotions.

[0449] The following describes the processing flow.

[0450] Step 1:

[0451] The server collects learners' personal characteristics information and stores it in a database. This data includes learning history, learning style, and various evaluation data. In addition, an emotion engine analyzes learners' facial expressions and voice tone in real time to acquire emotional data.

[0452] Step 2:

[0453] The server analyzes collected personal characteristics information and emotional data. Machine learning algorithms are used for this analysis, generating personalized learning plans based on the learner's learning needs and emotional state. These plans include setting learning objectives and adjusting them based on emotional data.

[0454] Step 3:

[0455] The terminal notifies the educator of the educational plan and sentiment analysis results provided by the server. The educator can then access the latest information through the terminal and select the most appropriate teaching method for each learner.

[0456] Step 4:

[0457] The server continuously monitors emotional data during learning and dynamically adjusts the plan if the learner shows signs of stress or frustration. This adjusted plan is then communicated to the educator via the terminal.

[0458] Step 5:

[0459] Users (parents) receive reports based on the learner's educational plan and sentiment analysis through a dedicated portal. This enables parents to appropriately support their child's learning at home.

[0460] Step 6:

[0461] The server analyzes emotional data collected over a long period to identify long-term trends in learners' emotional growth and challenges. This information is used in collaboration with educators and professionals to develop further support strategies.

[0462] (Example 2)

[0463] 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."

[0464] In educational settings for learners with special needs, there is a challenge in providing education that appropriately reflects individual emotional factors and characteristics that cannot be adequately addressed by conventional educational plans. Furthermore, there is a lack of flexible educational support tools that can respond to real-time emotional changes in the classroom.

[0465] 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.

[0466] In this invention, the server includes means for collecting learner's personal characteristics information and emotional information, means for integrating and analyzing the personal characteristics information and emotional information and generating an individualized educational plan using a generative AI model, and means for notifying educators and guardians of the educational plan. This enables dynamically adjusted educational support based on the learner's real-time emotional changes.

[0467] "Learner's personal characteristics information" refers to information specific to an individual, such as their learning history, characteristics, and behavioral history.

[0468] "Emotional information" refers to data that indicates the learner's real-time emotional state, obtained from their facial expressions and voice.

[0469] A "generative AI model" refers to a model that uses machine learning techniques to make specific judgments or predictions from input data.

[0470] An "educational plan" refers to a plan that includes individualized teaching policies and learning content tailored to the characteristics and emotions of the learners.

[0471] "Means of notification" refers to the process of using devices and communication technologies to convey educational plans and related information to educators and parents.

[0472] This invention is a system that effectively supports the education of learners who require special assistance. Specifically, it has the function of dynamically adjusting the educational plan using the learner's personal characteristics information and emotional information.

[0473] The server collects learners' personal characteristics and emotional information. MySQL and MongoDB are used as databases for this information collection. Emotional information is analyzed using the OpenCV library for facial expression analysis via camera, and the Google Cloud Speech-to-Text service is used for speech analysis. The server then analyzes the data acquired using generative AI models such as TensorFlow and PyTorch to generate personalized learning plans.

[0474] The device receives the lesson plan from the server and notifies the educator. Android and iOS apps are used for this purpose. Educators review the plan through the device and incorporate it into their instruction of students. This information is also provided to parents through a web portal. The portal is built using frameworks such as React and Vue.js.

[0475] Users (educators and parents) can provide guidance and support to learners based on the notified educational plan. In particular, by considering real-time emotional feedback, it becomes possible to provide optimal education tailored to the learner's emotional state.

[0476] For example, if a student struggling with a difficult math problem shows signs of stress, the server analyzes the emotional information and immediately generates an adjusted lesson plan. Educators can then review this plan and continue teaching in a way that is less burdensome for the student. Parents can also access this information through their devices and adjust their support at home accordingly.

[0477] Example of a prompt:

[0478] "When tackling new and complex tasks, how do you identify emotions from learners' facial expressions and voice, and adjust your plan accordingly?"

[0479] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0480] Step 1:

[0481] The server collects learner personal characteristics and emotional information. Inputs include learner facial video, audio data, and existing learning history. Specifically, it analyzes video footage acquired from the camera using the "OpenCV" library to extract facial information. It also uses the "Google Cloud Speech-to-Text" service to convert audio data into text and infer emotions. The output is a unified dataset for each learner.

[0482] Step 2:

[0483] The server integrates and analyzes collected personal characteristics and emotional information, and uses a generative AI model to generate personalized educational plans. The input is the integrated dataset obtained in Step 1. Specifically, it runs a generative AI model using "TensorFlow" or "PyTorch" to analyze emotional patterns and learning tendencies, thereby designing the optimal educational approach for each learner. The output is a dynamically adjusted educational plan for each individual learner.

[0484] Step 3:

[0485] The device receives the lesson plan sent from the server and notifies the educator. The input is the lesson plan from the server. Specifically, it displays lesson content and strategies tailored to the learner through the iOS or Android app used by the educator. The output is information that the educator can use for specific instruction.

[0486] Step 4:

[0487] Users (educators and parents) guide and support learners based on the notified educational plan. Inputs include the educational plan and recommended teaching methods viewed on the device. Specifically, educators incorporate this plan into their lessons and improve teaching methods as needed. Parents also utilize this information for support at home. Outputs are the learners' learning experiences, which are adjusted in real time.

[0488] Step 5:

[0489] The server receives feedback and additional information from users and updates the system to improve the accuracy of the educational plan. Inputs include feedback from educators and parents, as well as learner progress data. This allows the system to adjust the parameters of the generating AI model and incorporate them into future educational plans. Outputs are the updated educational plan and the sentiment analysis model.

[0490] (Application Example 2)

[0491] 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."

[0492] A problem exists where individuals with specific needs receive inadequate care because their emotional state is not taken into consideration when providing support. Furthermore, it is difficult to dynamically adjust care plans in response to changes in an individual's emotions and behavior. Therefore, it is necessary to develop a system that allows stakeholders to effectively provide support tailored to each individual.

[0493] 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.

[0494] In this invention, the server includes means for collecting individual characteristic information and emotional state; means for analyzing the characteristic information and emotional state and generating an individualized support plan; means for notifying supporters and relevant parties of the support plan; means for acquiring and analyzing emotional data in real time; and means for dynamically adjusting the plan based on the emotional state. This enables flexible support tailored to the individual's emotional state.

[0495] "Characteristic information" is a general term for information necessary for identification and understanding, such as an individual's personality, habits, and physical characteristics.

[0496] "Emotional state" refers to a temporary emotional or moodal state exhibited by an individual, which is usually recognized through facial expressions, voice, and behavior.

[0497] A "support plan" is a plan created to propose services and care methods suitable for an individual, and is personalized based on the individual's characteristics and condition.

[0498] A "server" refers to a device or software system connected to a network that processes data and provides applications.

[0499] A "supporter" refers to an individual or group that plays a role in providing care and support to an individual.

[0500] "Stakeholders" refers to individuals or organizations that directly or indirectly affect the care or life of an individual.

[0501] "Real-time" refers to a situation where data and information are processed or updated almost instantly with virtually no delay.

[0502] This invention is a system that provides personalized support plans to individuals who require specific assistance, based on their characteristic information and emotional state. The server collects individual characteristic information and emotional state in real time through devices equipped with cameras and microphones, such as smartphones and tablets. This information is processed using an emotion analysis engine to generate personalized support plans.

[0503] The software used will consist of a frontend built with React Native for the application interface and a backend server environment using Python's Flask. TensorFlow and OpenCV will be used for emotion analysis. This will allow for the analysis of facial expressions from the camera and audio data from the microphone to identify emotional states.

[0504] The server generates an optimized support plan based on the analyzed information and notifies support providers and stakeholders. This notification is made through specific applications or stakeholder portals, allowing support providers to review the plan as needed and provide the most appropriate support for each individual.

[0505] For example, if an elderly person shows signs of stress during a daily activity, the system will detect this and suggest a relaxing environment. An example of this prompt might be, "If an elderly person is feeling stressed, generate suggestions to help them relax."

[0506] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0507] Step 1:

[0508] The device uses the camera and microphone of a smartphone or tablet to capture the individual's facial expressions and voice data. During this process, facial expression information is input as image data, and voice tone is input as voice data. This data is then prepared for transmission to the server in real time.

[0509] Step 2:

[0510] The server receives facial and audio data transmitted from the terminal in real time. Next, it analyzes the received data using TensorFlow and OpenCV to process it for data processing to estimate the emotional state. As a result, the current emotional state (e.g., joy, surprise, sadness) is output.

[0511] Step 3:

[0512] The server integrates the analyzed emotional state with pre-collected feature information and generates an optimized support plan using a generative AI model. The inputs are emotional states and feature information, while the output is a personalized support plan.

[0513] Step 4:

[0514] The server notifies the terminal of the generated support plan and provides it to supporters and stakeholders via the stakeholders portal. Notification data is entered, and based on that, guidance information on how supporters should respond is output.

[0515] Step 5:

[0516] The user reviews the support plan provided via the device and applies it to actual care. The device assists in providing more appropriate care by clearly indicating the actions required for the caregiver. At this time, the user receives the proposed plan as input and outputs the care methods as actual actions.

[0517] 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.

[0518] 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.

[0519] 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.

[0520] [Fourth Embodiment]

[0521] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0522] 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.

[0523] 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).

[0524] 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.

[0525] 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.

[0526] 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).

[0527] 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.

[0528] 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.

[0529] 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.

[0530] 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.

[0531] 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.

[0532] 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.

[0533] 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".

[0534] This invention is a system aimed at providing effective and efficient educational support to learners who require special assistance. The system mainly consists of a server, terminals, and users (educators and parents).

[0535] First, the server centrally collects learners' personal characteristics information and stores it in a database. This information includes learners' past performance, learning style, and behavioral history. This makes it possible to accurately understand individual educational needs.

[0536] Next, the server analyzes the collected data and generates a personalized learning plan optimized for the learner. This plan includes specific learning objectives and recommended learning materials. Machine learning and statistical analysis techniques are used for the analysis.

[0537] The generated individualized learning plans are provided to educators via the device. This allows educators to provide optimal instruction for each learner. Furthermore, the server monitors the learners' daily behavioral patterns and immediately analyzes the causes of any problematic behavior.

[0538] To facilitate information sharing, users (parents) are provided with a function to report on their child's progress through a dedicated parent portal. This allows parents to stay informed about their child's learning progress and any support needed in a timely manner.

[0539] Furthermore, users (educators) can request that experts be contacted on behalf of learners as needed. The server will arrange for the transmission of appropriate data to facilitate information sharing with experts.

[0540] For example, if a learner has difficulty with reading and writing, the system analyzes this information and suggests special materials and training plans to improve their reading and writing skills. This plan is communicated to the educator, and the learner's progress is regularly reported to the parents. Through this process, the system helps educators, learners, and parents collaborate to create the optimal learning environment.

[0541] The following describes the processing flow.

[0542] Step 1:

[0543] The server collects learners' personal characteristics information and stores it in a database. This information may include past performance, learning style, and behavioral history. This provides a foundation for a comprehensive understanding of the learners' situation.

[0544] Step 2:

[0545] The server analyzes the collected data. This analysis uses machine learning algorithms to identify learners' strengths and weaknesses and generate individually optimized learning plans. These plans include setting learning objectives and recommending learning materials.

[0546] Step 3:

[0547] The terminal notifies the educator of the individualized learning plan provided by the server. The educator can then review the plan displayed on the terminal and prepare to provide optimal instruction to the learner.

[0548] Step 4:

[0549] The server continuously monitors learners' behavioral data and analyzes their behavioral patterns. If any abnormalities or problematic behaviors are detected, it immediately identifies the cause and prepares information to report to educators and parents.

[0550] Step 5:

[0551] Users (parents) can access the latest information on their child's learning progress and behavioral patterns through a dedicated portal. Parents can use this information to support their child's learning at home.

[0552] Step 6:

[0553] If necessary, users (educators) can request contact with experts through the system when they determine that learners require further assistance. The server prepares the relevant data for the experts and facilitates the collaboration.

[0554] (Example 1)

[0555] 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".

[0556] In today's learning environment, there is a need to efficiently provide appropriate educational support to learners who require special assistance. Traditional systems have struggled to understand the individual needs of learners and provide effective support based on those needs. Furthermore, there has been a lack of information sharing to strengthen collaboration with educators and parents and to support learners' growth. As a result, learning effectiveness has not been maximized, and learners have been unable to fully realize their potential.

[0557] 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.

[0558] In this invention, the server includes means for collecting individual information of learners, means for analyzing the individual information and generating an individualized educational plan, and means for providing the educational plan to educators and guardians. This enables optimal educational support tailored to the characteristics and needs of each learner. Furthermore, by supporting efficient learning instruction through a terminal dedicated to educators and enabling rapid information sharing through a portal dedicated to guardians, learners, educators, and guardians can work together to improve the learning process and maximize learning effectiveness.

[0559] "Individual learner information" refers to information that shows the individual characteristics of each learner, and includes data such as past performance, learning style, and behavioral history.

[0560] An "individualized learning plan" is a specific instructional plan that includes learning objectives and recommended materials, formulated based on the individual needs and characteristics of each learner.

[0561] A "device for educators" is an electronic device used by educators to efficiently instruct learners, and is a device that can receive and utilize individualized educational plans.

[0562] A "parent-only portal" is an online platform that allows parents to monitor and check details of their child's learning progress and the support provided.

[0563] "Collaboration with experts" refers to the process by which educators share information and cooperate with external experts when seeking specialized support.

[0564] This invention provides a system that offers individually optimized educational support to learners who require special assistance. The system primarily consists of a server, terminals, and users (educators and parents).

[0565] The server collects individual learner information and stores it in a database. The data collected is based on information such as the learner's past performance, learning style, and behavioral history. This information is obtained through the school's information management system and a dedicated input form. The server is equipped with a machine learning model to analyze this information and processes the data using libraries such as TensorFlow and PyTorch. Through analysis, a personalized learning plan tailored to each learner is generated, which includes learning objectives and recommended materials.

[0566] The generated lesson plans are provided to educators via the device. Educators receive this information through a dedicated application, enabling them to implement optimal instruction for their students. The device also serves to provide information to parents, sharing their students' progress and necessary support in real time through a dedicated parent portal.

[0567] Educators, as users, can contact experts on behalf of their students, and the server quickly provides the necessary information to the experts. This facilitates smooth collaboration between educators and experts, enabling appropriate support for learners.

[0568] For example, if a student has difficulty with mathematics, the server collects and analyzes this information to generate learning materials and a personalized curriculum to help them understand mathematics. Educators then use this to provide instruction to the student, and the student's progress can be monitored through the parent portal. This allows educators, students, and parents to collaborate and create an optimal learning environment.

[0569] An example of a prompt to input into the generating AI model is: "For learner A, who requires special support, create an individualized learning plan based on past learning data and suggest materials to improve their mathematical skills."

[0570] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0571] Step 1:

[0572] The server collects individual learner information. Specifically, it automatically retrieves data from online forms and the school's information management system. Input at this stage includes information such as the learner's grades, learning style, and behavioral history. This data is stored in a database. The output is the organized learner data stored in the database.

[0573] Step 2:

[0574] The server performs data analysis based on the collected individual information. It uses machine learning models and processes the data via libraries such as TensorFlow and PyTorch. The input is the learner characteristic data collected in step 1. Through pattern recognition and trend analysis using machine learning, it identifies learning needs corresponding to each learner's characteristics. The output is a customized learning plan for each learner.

[0575] Step 3:

[0576] The lesson plan generated by the server is provided to the educator via a terminal. The terminal has a dedicated application for educators, through which they can review the generated plan. The input is the lesson plan output in step 2. The application converts the received lesson plan into a format easily understood by the educator and displays it on the screen. The output is the specific lesson plan provided to the educator.

[0577] Step 4:

[0578] The device reports the content of the educational plan to parents via a dedicated parent portal. The input is the specific lesson plan provided to the educator. The device allows parents to access their account to view learner progress and support information. The output is a detailed report to parents, provided through the portal.

[0579] Step 5:

[0580] The server continuously monitors learner behavior data and, if an anomaly is detected, analyzes the cause of the problematic behavior. This uses data such as behavior logs as input. Machine learning algorithms are used for anomaly detection and trend analysis. The output is feedback and suggested solutions provided to educators and users.

[0581] Step 6:

[0582] When an educator (user) needs to contact an expert, the server arranges for the necessary information to be compiled and sent to the expert. Inputs include, for example, learning plans and behavioral data compiled in text format. The server converts this data into a format easily understood by the expert and sends it via communication tools. Outputs are the detailed information provided to the expert.

[0583] (Application Example 1)

[0584] 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".

[0585] To provide optimized support plans for individuals requiring special assistance, a framework is needed to accurately grasp attribute information and promptly and appropriately notify support providers of that information. However, the current system lacks the ability to monitor the diverse behavioral tendencies of individual support recipients in real time and provide immediate support through the devices used by support providers.

[0586] 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.

[0587] In this invention, the server includes means for collecting attribute information of an individual receiving support, means for analyzing the attribute information and generating an optimized support plan, means for notifying the supporter and supervisor of the support plan, and means for assisting the support through a visual information output device carried by the supporter. This enables the provision of rapid and accurate support to the individual receiving support, as well as the detection and immediate response to abnormal behavior.

[0588] "Individualized support recipients" refer to individuals who require specific support and are eligible to receive support plans optimized based on their attribute information.

[0589] "Attribute information" refers to data that indicates the characteristics of individuals receiving individual support, and consists of information such as health status, behavioral tendencies, and historical information.

[0590] A "support plan" is a plan that includes specific action guidelines and procedures that support providers should implement, generated by analyzing the attribute information of the individual receiving support.

[0591] A "supporter" refers to a person or organization that plays a role in providing support to an individual receiving support.

[0592] A "visual information output device" is a device that a support worker can carry and use to visually display information, and is used to assist in the implementation of support plans.

[0593] "Abnormal behavior" refers to actions that deviate from the normal behavioral patterns of the individual receiving support and require special attention.

[0594] To implement this invention, it is crucial to build a system that provides optimized support plans to individual support recipients who require specific assistance. The server collects attribute information of individual support recipients and generates an optimized support plan by analyzing this data. The server uses machine learning algorithms to perform statistical analysis of the data. Specifically, it utilizes generative AI models such as TensorFlow to predict the behavioral tendencies of individual support recipients and propose effective interventions to support providers.

[0595] The server notifies the support provider and supervisor of the generated support plan. During the notification process, information is provided in real time through a visual information output device carried by the support provider. For example, using smart glasses, when the support provider recognizes the elderly person's face, the corresponding support procedure is visually displayed. This visual information is displayed through an application built into the visual device using Flutter or React Native.

[0596] Furthermore, the server can perform continuous monitoring and respond quickly when abnormal behavior is detected. For example, if an elderly person unexpectedly gets out of bed at night, the application can immediately sound an alarm and notify the caregiver.

[0597] As an example of a specific prompt, a text message such as, "Ms. / Mr. XX's blood sugar levels tend to drop around 3 PM. Offer them a snack and check their blood sugar levels," would appear on the caregiver's smart device. Based on this prompt, the caregiver can immediately take appropriate action.

[0598] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0599] Step 1:

[0600] The server collects attribute information of individuals receiving individual support. Inputs include health status and behavioral data of these individuals, obtained through sensors or manual input. This data is stored in a database for later analysis. The output is a categorized attribute dataset.

[0601] Step 2:

[0602] The server analyzes the collected attribute information. The input is the attribute dataset obtained in Step 1. Using a generative AI model, it predicts the behavioral tendencies and health risks of the target individuals. Here, statistical analysis and pattern recognition of the data are performed to generate an optimized support plan. The output is a specific support plan.

[0603] Step 3:

[0604] The server notifies the support provider and supervisor of the generated support plan. The input is the support plan generated in step 2. The plan is displayed in real time on a visual information output device via an app developed with Flutter or React Native. The output is the specific support procedure displayed on the support provider's smart device.

[0605] Step 4:

[0606] The server continuously monitors the behavior of individual support recipients and detects abnormal behavior. The input is behavioral data acquired in real time. Anomalies are identified by comparing the data to pre-defined criteria, and warnings are generated. The output consists of the detected abnormal behavior and warning notifications.

[0607] Step 5:

[0608] The terminal notifies the caregiver of any warnings that have occurred and prompts them to take the necessary action. The input is the warning data generated in step 4. Specifically, the visual information output device displays the warning message and instructions on how to respond. The output is the warning message displayed on the caregiver's visual device.

[0609] 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.

[0610] This invention combines a system that supports the education of learners with special needs with an emotion engine that recognizes and analyzes user emotions. The system aims to utilize emotion data to build a more effective educational system.

[0611] First, the server collects learner personal characteristics information as before and records it in a database. This includes the learner's past learning records, characteristics information, and behavioral history. Simultaneously, a newly integrated emotion engine acquires emotional information from the learner's facial expressions and voice.

[0612] The server comprehensively analyzes the acquired personal characteristics and emotional information. The emotion engine recognizes the learner's emotional state in real time and uses that data to dynamically adjust the parameters of the teaching plan. For example, if a learner shows signs of frustration, the system will either reduce the difficulty of the plan or select a new teaching method.

[0613] The device notifies educators of personalized learning plans received from the server. Educators can review the latest plan and emotional feedback displayed on the device and use it to guide learners. Information on learning plans and emotional changes is also provided to parents through a parent portal, enabling appropriate support at home.

[0614] Users (educators and parents) can refer to real-time feedback on emotional information and changes to educational plans, using this information to improve learner support strategies. Furthermore, long-term analysis of emotional data can be used to evaluate learners' emotional growth and coping with challenges, and to collaborate with experts to optimize individualized support methods.

[0615] For example, if a learner shows a clear stress response to a complex task, the system immediately analyzes the emotional information and generates a new plan with adjusted difficulty. This plan is notified to the educator and reported to the parents, ensuring an optimal learning environment for the learner. This invention makes it possible to provide nuanced educational support that utilizes learners' emotions.

[0616] The following describes the processing flow.

[0617] Step 1:

[0618] The server collects learners' personal characteristics information and stores it in a database. This data includes learning history, learning style, and various evaluation data. In addition, an emotion engine analyzes learners' facial expressions and voice tone in real time to acquire emotional data.

[0619] Step 2:

[0620] The server analyzes collected personal characteristics information and emotional data. Machine learning algorithms are used for this analysis, generating personalized learning plans based on the learner's learning needs and emotional state. These plans include setting learning objectives and adjusting them based on emotional data.

[0621] Step 3:

[0622] The terminal notifies the educator of the educational plan and sentiment analysis results provided by the server. The educator can then access the latest information through the terminal and select the most appropriate teaching method for each learner.

[0623] Step 4:

[0624] The server continuously monitors emotional data during learning and dynamically adjusts the plan if the learner shows signs of stress or frustration. This adjusted plan is then communicated to the educator via the terminal.

[0625] Step 5:

[0626] Users (parents) receive reports based on the learner's educational plan and sentiment analysis through a dedicated portal. This enables parents to appropriately support their child's learning at home.

[0627] Step 6:

[0628] The server analyzes emotional data collected over a long period to identify long-term trends in learners' emotional growth and challenges. This information is used in collaboration with educators and professionals to develop further support strategies.

[0629] (Example 2)

[0630] 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".

[0631] In educational settings for learners with special needs, there is a challenge in providing education that appropriately reflects individual emotional factors and characteristics that cannot be adequately addressed by conventional educational plans. Furthermore, there is a lack of flexible educational support tools that can respond to real-time emotional changes in the classroom.

[0632] 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.

[0633] In this invention, the server includes means for collecting learner's personal characteristics information and emotional information, means for integrating and analyzing the personal characteristics information and emotional information and generating an individualized educational plan using a generative AI model, and means for notifying educators and guardians of the educational plan. This enables dynamically adjusted educational support based on the learner's real-time emotional changes.

[0634] "Learner's personal characteristics information" refers to information specific to an individual, such as their learning history, characteristics, and behavioral history.

[0635] "Emotional information" refers to data that indicates the learner's real-time emotional state, obtained from their facial expressions and voice.

[0636] A "generative AI model" refers to a model that uses machine learning techniques to make specific judgments or predictions from input data.

[0637] An "educational plan" refers to a plan that includes individualized teaching policies and learning content tailored to the characteristics and emotions of the learners.

[0638] "Means of notification" refers to the process of using devices and communication technologies to convey educational plans and related information to educators and parents.

[0639] This invention is a system that effectively supports the education of learners who require special assistance. Specifically, it has the function of dynamically adjusting the educational plan using the learner's personal characteristics information and emotional information.

[0640] The server collects learners' personal characteristics and emotional information. MySQL and MongoDB are used as databases for this information collection. Emotional information is analyzed using the OpenCV library for facial expression analysis via camera, and the Google Cloud Speech-to-Text service is used for speech analysis. The server then analyzes the data acquired using generative AI models such as TensorFlow and PyTorch to generate personalized learning plans.

[0641] The device receives the lesson plan from the server and notifies the educator. Android and iOS apps are used for this purpose. Educators review the plan through the device and incorporate it into their instruction of students. This information is also provided to parents through a web portal. The portal is built using frameworks such as React and Vue.js.

[0642] Users (educators and parents) can provide guidance and support to learners based on the notified educational plan. In particular, by considering real-time emotional feedback, it becomes possible to provide optimal education tailored to the learner's emotional state.

[0643] For example, if a student struggling with a difficult math problem shows signs of stress, the server analyzes the emotional information and immediately generates an adjusted lesson plan. Educators can then review this plan and continue teaching in a way that is less burdensome for the student. Parents can also access this information through their devices and adjust their support at home accordingly.

[0644] Example of a prompt:

[0645] "When tackling new and complex tasks, how do you identify emotions from learners' facial expressions and voice, and adjust your plan accordingly?"

[0646] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0647] Step 1:

[0648] The server collects learner personal characteristics and emotional information. Inputs include learner facial video, audio data, and existing learning history. Specifically, it analyzes video footage acquired from the camera using the "OpenCV" library to extract facial information. It also uses the "Google Cloud Speech-to-Text" service to convert audio data into text and infer emotions. The output is a unified dataset for each learner.

[0649] Step 2:

[0650] The server integrates and analyzes collected personal characteristics and emotional information, and uses a generative AI model to generate personalized educational plans. The input is the integrated dataset obtained in Step 1. Specifically, it runs a generative AI model using "TensorFlow" or "PyTorch" to analyze emotional patterns and learning tendencies, thereby designing the optimal educational approach for each learner. The output is a dynamically adjusted educational plan for each individual learner.

[0651] Step 3:

[0652] The device receives the lesson plan sent from the server and notifies the educator. The input is the lesson plan from the server. Specifically, it displays lesson content and strategies tailored to the learner through the iOS or Android app used by the educator. The output is information that the educator can use for specific instruction.

[0653] Step 4:

[0654] Users (educators and parents) guide and support learners based on the notified educational plan. Inputs include the educational plan and recommended teaching methods viewed on the device. Specifically, educators incorporate this plan into their lessons and improve teaching methods as needed. Parents also utilize this information for support at home. Outputs are the learners' learning experiences, which are adjusted in real time.

[0655] Step 5:

[0656] The server receives feedback and additional information from users and updates the system to improve the accuracy of the educational plan. Inputs include feedback from educators and parents, as well as learner progress data. This allows the system to adjust the parameters of the generating AI model and incorporate them into future educational plans. Outputs are the updated educational plan and the sentiment analysis model.

[0657] (Application Example 2)

[0658] 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".

[0659] A problem exists where individuals with specific needs receive inadequate care because their emotional state is not taken into consideration when providing support. Furthermore, it is difficult to dynamically adjust care plans in response to changes in an individual's emotions and behavior. Therefore, it is necessary to develop a system that allows stakeholders to effectively provide support tailored to each individual.

[0660] 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.

[0661] In this invention, the server includes means for collecting individual characteristic information and emotional state; means for analyzing the characteristic information and emotional state and generating an individualized support plan; means for notifying supporters and relevant parties of the support plan; means for acquiring and analyzing emotional data in real time; and means for dynamically adjusting the plan based on the emotional state. This enables flexible support tailored to the individual's emotional state.

[0662] "Characteristic information" is a general term for information necessary for identification and understanding, such as an individual's personality, habits, and physical characteristics.

[0663] "Emotional state" refers to a temporary emotional or moodal state exhibited by an individual, which is usually recognized through facial expressions, voice, and behavior.

[0664] A "support plan" is a plan created to propose services and care methods suitable for an individual, and is personalized based on the individual's characteristics and condition.

[0665] A "server" refers to a device or software system connected to a network that processes data and provides applications.

[0666] A "supporter" refers to an individual or group that plays a role in providing care and support to an individual.

[0667] "Stakeholders" refers to individuals or organizations that directly or indirectly affect the care or life of an individual.

[0668] "Real-time" refers to a situation where data and information are processed or updated almost instantly with virtually no delay.

[0669] This invention is a system that provides personalized support plans to individuals who require specific assistance, based on their characteristic information and emotional state. The server collects individual characteristic information and emotional state in real time through devices equipped with cameras and microphones, such as smartphones and tablets. This information is processed using an emotion analysis engine to generate personalized support plans.

[0670] The software used will consist of a frontend built with React Native for the application interface and a backend server environment using Python's Flask. TensorFlow and OpenCV will be used for emotion analysis. This will allow for the analysis of facial expressions from the camera and audio data from the microphone to identify emotional states.

[0671] The server generates an optimized support plan based on the analyzed information and notifies support providers and stakeholders. This notification is made through specific applications or stakeholder portals, allowing support providers to review the plan as needed and provide the most appropriate support for each individual.

[0672] For example, if an elderly person shows signs of stress during a daily activity, the system will detect this and suggest a relaxing environment. An example of this prompt might be, "If an elderly person is feeling stressed, generate suggestions to help them relax."

[0673] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0674] Step 1:

[0675] The device uses the camera and microphone of a smartphone or tablet to capture the individual's facial expressions and voice data. During this process, facial expression information is input as image data, and voice tone is input as voice data. This data is then prepared for transmission to the server in real time.

[0676] Step 2:

[0677] The server receives facial and audio data transmitted from the terminal in real time. Next, it analyzes the received data using TensorFlow and OpenCV to process it for data processing to estimate the emotional state. As a result, the current emotional state (e.g., joy, surprise, sadness) is output.

[0678] Step 3:

[0679] The server integrates the analyzed emotional state with pre-collected feature information and generates an optimized support plan using a generative AI model. The inputs are emotional states and feature information, while the output is a personalized support plan.

[0680] Step 4:

[0681] The server notifies the terminal of the generated support plan and provides it to supporters and stakeholders via the stakeholders portal. Notification data is entered, and based on that, guidance information on how supporters should respond is output.

[0682] Step 5:

[0683] The user reviews the support plan provided via the device and applies it to actual care. The device assists in providing more appropriate care by clearly indicating the actions required for the caregiver. At this time, the user receives the proposed plan as input and outputs the care methods as actual actions.

[0684] 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.

[0685] 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.

[0686] 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 robot 414.

[0687] 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.

[0688] 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.

[0689] 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.

[0690] 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.

[0691] 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.

[0692] 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."

[0693] 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.

[0694] 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.

[0695] 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.

[0696] 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.

[0697] 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.

[0698] 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.

[0699] 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.

[0700] 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.

[0701] 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.

[0702] 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.

[0703] 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.

[0704] 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.

[0705] The following is further disclosed regarding the embodiments described above.

[0706] (Claim 1)

[0707] Means for collecting learners' personal characteristics information,

[0708] A means for analyzing the aforementioned personal characteristics information and generating an individualized education plan,

[0709] Means for notifying educators and parents of the aforementioned educational plan,

[0710] A system that includes this.

[0711] (Claim 2)

[0712] The system according to claim 1, characterized in that the analysis means further includes means for recording learner behavior patterns and identifying problematic behaviors.

[0713] (Claim 3)

[0714] The system according to claim 1, wherein the notification means further includes means for providing the education plan and analysis results to the parent through a parent portal.

[0715] "Example 1"

[0716] (Claim 1)

[0717] Means for collecting individual learner information,

[0718] A means for analyzing the aforementioned individual information and generating an individualized education plan,

[0719] Means for providing the aforementioned educational plan to educators and guardians,

[0720] A means of optimizing learning support through a terminal dedicated to educators,

[0721] A means of sharing information through a portal exclusively for parents,

[0722] A system that includes this.

[0723] (Claim 2)

[0724] This further includes means of monitoring learner behavior, identifying problematic behaviors, and providing feedback.

[0725] The system according to claim 1.

[0726] (Claim 3)

[0727] This further includes means for educators to collaborate with experts to enhance learning support.

[0728] The system according to claim 1.

[0729] "Application Example 1"

[0730] (Claim 1)

[0731] Means for collecting attribute information of individuals receiving individual support,

[0732] A means for analyzing the attribute information and generating an optimized support plan,

[0733] Means for notifying the supporter and supervisor of the aforementioned support plan,

[0734] Means for providing assistance through a visual information output device carried by the supporter,

[0735] A system that includes this.

[0736] (Claim 2)

[0737] The system according to claim 1, characterized in that the analysis means further includes means for recording the behavioral tendencies of individuals receiving support and detecting abnormal behavior.

[0738] (Claim 3)

[0739] The system according to claim 1, wherein the notification means further includes means for providing the support plan and analysis results through supervisor data acquisition means.

[0740] "Example 2 of combining an emotion engine"

[0741] (Claim 1)

[0742] Means for collecting learners' personal characteristics and emotional information,

[0743] A means for integrating and analyzing the aforementioned personal characteristics information and emotional information, and generating an individualized educational plan using a generative AI model,

[0744] Means for notifying educators and parents of the aforementioned educational plan,

[0745] Means to support educators' guidance based on the aforementioned notified educational plan,

[0746] A system that includes this.

[0747] (Claim 2)

[0748] The system according to claim 1, further characterized in that the analysis means includes means for analyzing learners' emotional information in real time and dynamically adjusting the difficulty level of the educational plan.

[0749] (Claim 3)

[0750] The system according to claim 1, wherein the notification means further includes means for providing the educational plan and emotional progress information to the parent through a parent portal.

[0751] "Application example 2 when combining with an emotional engine"

[0752] (Claim 1)

[0753] A means of collecting individual characteristic information,

[0754] A means for analyzing the aforementioned characteristic information and emotional state to generate an individualized support plan,

[0755] Means for notifying supporters and related parties of the aforementioned support plan,

[0756] A means of acquiring and analyzing emotional data in real time,

[0757] Means for dynamically adjusting the plan based on the aforementioned emotional state,

[0758] A system that includes this.

[0759] (Claim 2)

[0760] The system according to claim 1, characterized in that the analysis means further includes means for recording the psychological state of an individual and identifying a problem state.

[0761] (Claim 3)

[0762] The system according to claim 1, characterized in that the notification means further includes means for providing the support plan and analysis results to the relevant parties through a stakeholders portal. [Explanation of Symbols]

[0763] 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. Means for collecting attribute information of individuals receiving individual support, A means for analyzing the attribute information and generating an optimized support plan, Means for notifying the supporter and supervisor of the aforementioned support plan, Means for providing assistance through a visual information output device carried by the supporter, A system that includes this.

2. The system according to claim 1, characterized in that the analysis means further includes means for recording the behavioral tendencies of individuals receiving support and detecting abnormal behavior.

3. The system according to claim 1, characterized in that the notification means further includes means for providing the support plan and analysis results through supervisor data acquisition means.

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