system

Abstract

We provide the system. [Solution] A generation means that receives goal setting information and learning progress information and generates a user's learning plan, A means for calculating review timing according to a forgetting curve model based on learning history information, A means of generating and notifying feedback based on the user's progress and understanding, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 Due to the lack of self-management ability faced by modern learners, the difficulty in making a plan optimized for each individual's learning progress, and further the difficulty in determining effective review timing, there is a demand for improving learning efficiency. In addition, there is also a problem that the learning progress is difficult to see and it is difficult to maintain motivation. The purpose of this invention is to comprehensively solve these problems. 【Means for Solving the Problems】 【0005】 This invention is a system comprising a generation means for generating a user's learning plan based on goal setting information and learning progress information, a means for calculating review timing using a forgetting curve model based on learning history information, and a means for generating and notifying feedback according to the user's progress information and level of understanding. Furthermore, it has a function to present individualized learning tasks that take into account the user's learning style, and a function to appropriately notify the user of the generated learning plan and review schedule, thereby providing an efficient and personalized learning experience. 【0006】 "Goal setting information" refers to information that specifically outlines the learning or qualification acquisition goals that the user wishes to achieve. 【0007】 "Learning progress information" refers to information about the learning content and progress made by the user. 【0008】 "Generation method" refers to a function that automatically creates an optimal learning plan based on the user's learning goals and progress information. 【0009】 "Learning history information" refers to information about the learning content and results of a user's past learning activities. 【0010】 The "forgetting curve model" is a theoretical model that shows how human memory declines over time. 【0011】 "Review timing" refers to the optimal time for reviewing learned material to solidify it in memory. 【0012】 "Feedback" refers to information that provides evaluations and advice regarding a user's learning content and progress. 【0013】 "Notification" refers to the action or mechanism of delivering information or messages to a user. 【0014】 "Learning style" is a term that describes the characteristics of a user's preferences and methods when learning. 【0015】 "Learning task" is a term indicating specific learning activities or tasks that a user should perform. 【0016】 "Plan" refers to a summary of the progress and schedule of learning set to achieve learning goals. 【Brief Explanation of Drawings】 【0017】 [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 multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple 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 Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined. 【Mode for Carrying Out the Invention】 【0018】 Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0019】 First, the terms used in the following description will be explained. 【0020】 In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one 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), etc. 【0021】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0022】 In the following embodiments, the numbered 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, etc. 【0023】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0024】 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." 【0025】 [First Embodiment] 【0026】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0027】 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. 【0028】 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). 【0029】 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. 【0030】 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. 【0031】 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. 【0032】 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. 【0033】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0034】 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. 【0035】 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. 【0036】 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. 【0037】 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". 【0038】 This invention is a system in which a server, terminal, and user work together to efficiently personalize and support the user's learning experience. The main elements of the system include goal setting information, learning progress information, generation means, learning history information, forgetting curve model, review timing, feedback, notifications, learning style, learning tasks, and planning. 【0039】 The server receives goal setting information and learning progress information provided by the user and records it in a database. Based on this, the generation device creates a learning plan tailored to the user's characteristics. This plan is customized to support the user in achieving their goals and sets the priorities and schedules of learning tasks. The terminal displays this plan in the user interface, allowing the user to check their daily learning tasks. 【0040】 Furthermore, the server maintains learning history information and uses a forgetting curve model to calculate the optimal review timing. This review timing is designed to ensure that the learned content is retained in long-term memory, and the device notifies the user of the need for review. 【0041】 Furthermore, by collecting and analyzing progress information in real time, the system evaluates the user's learning status and generates feedback based on the user's understanding and progress. This feedback is not only an evaluation based on progress information, but also includes encouraging messages to stimulate the user's motivation to learn, and is notified to the user from their device. 【0042】 For example, if a user aims to pass a foreign language proficiency test, the server sets "Passing a Foreign Language Proficiency Test" as the user's goal and creates a learning plan that includes daily vocabulary study and grammar practice based on the user's available study time and style. At the same time, it uses a forgetting curve model to notify the user of review tasks at appropriate times, thereby ensuring retention of the learned material. If progress is good, it generates a message such as "Great job this week! You're making steady progress!" to support the user's learning. This creates an environment where users can continue learning efficiently and enthusiastically. 【0043】 The following describes the processing flow. 【0044】 Step 1: 【0045】 The server receives goal setting information and learning progress information from the user and records it in the database. The user inputs specific goals, current progress, and available time for learning via their device. 【0046】 Step 2: 【0047】 The server uses a generation mechanism to create a user-specific learning plan based on recorded goal setting information and progress information. This learning plan includes smaller tasks arranged in order of priority and includes an achievable schedule. 【0048】 Step 3: 【0049】 The device displays a generated learning plan to the user, allowing them to visually confirm their daily learning tasks. The user then begins learning according to the plan. 【0050】 Step 4: 【0051】 The server collects learning history information from the user and periodically applies a forgetting curve model to calculate when it's time for review. When it's time to review, the server sends a notification to the device. 【0052】 Step 5: 【0053】 The device displays a review notification to the user and lists the scheduled review tasks. The user then reviews the material based on the notification and reviews the learned content. 【0054】 Step 6: 【0055】 After a user completes a learning task, their progress is recorded on their device. The device then synchronizes this data with a server and saves it to a database as the latest progress. 【0056】 Step 7: 【0057】 The server generates feedback based on the user's new progress information, evaluating their understanding and learning progress. It also generates encouraging messages to help maintain user motivation. 【0058】 Step 8: 【0059】 The device notifies the user of any generated feedback or messages, allowing them to visually confirm them. Based on this, the user then proceeds to the next learning task. 【0060】 (Example 1) 【0061】 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." 【0062】 In today's learning environment, it is difficult to develop learning plans tailored to individual learners and to support efficient learning progress. In particular, there is a need for effective feedback that matches learners' diverse goals and progress, as well as appropriate review timing to encourage repeated learning. Therefore, creating an environment where learners can continue to learn with motivation is a crucial challenge. 【0063】 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. 【0064】 In this invention, the server includes an information processing device that receives goal setting information and learning progress information and generates a learning plan based on user characteristics; a processing device that calculates the optimal review timing according to a forgetting model based on learning history information; and an information processing device that generates an evaluation based on the user's progress and level of understanding and notifies the user through an output device. This enables the formulation of an optimal learning plan for achieving individual learner goals, as well as effective feedback and notification of review timing. 【0065】 "Goal setting information" refers to information about the specific learning goals that the user wishes to achieve. 【0066】 "Learning progress information" refers to information that shows how far a user has progressed towards their goal. 【0067】 "User characteristics" refer to individual features of each user, including their learning style, preferences, and time management abilities. 【0068】 "Learning history information" refers to records of learning activities a user has undertaken and the results thereof. 【0069】 A "forgetting model" is a mathematical model that describes how learned material is forgotten over time. 【0070】 The "review period" is the ideal time for learners to reconfirm what they have learned in the past. 【0071】 An "information processing device" is hardware or software used for collecting, analyzing, and processing data. 【0072】 An "output device" is hardware or software used to convey calculation results or information to the user. 【0073】 "Evaluation" is a judgment made based on the user's learning progress and level of understanding. 【0074】 "Notification" refers to the act of informing a user of information, or a function within a system that does so. 【0075】 This invention is an information processing system for effectively supporting user learning. The system primarily functions through the coordinated operation of three elements: a server, a terminal, and a user. 【0076】 The server receives goal setting information and learning progress information from the user. Specifically, it stores information entered by the user via their device in a database. This allows for the accumulation of data based on the user's learning status and goals. The server analyzes this information and uses a generative AI model to generate an optimal learning plan for the user. This learning plan includes personalized learning tasks and their implementation schedules. 【0077】 The device displays the learning plan sent from the server on the user interface. This user interface serves as a guide for the user to check their daily learning tasks and proceed with their studies accordingly. The device also receives and displays notifications from the server to the user. These include calculations of review timing based on forgetting models and feedback messages according to progress. 【0078】 A key feature of this system is that the server uses a forgetting model based on learning history information to calculate the optimal review timing. This review timing is then notified to the user via their device. This allows users to efficiently retain the learned material in their memory. 【0079】 As a concrete example, consider a user aiming to pass a foreign language proficiency test. The user sets "Passing a foreign language proficiency test" as their goal on their device, and the server generates a learning plan based on that goal, such as "Spending a certain amount of time each day studying vocabulary and grammar." Furthermore, the server uses a forgetting model to calculate specific review timings, such as "Review the words you learned in one week," and notifies the user. Depending on the progress, it is also possible to generate encouraging messages such as, "You're doing great! Keep it up!" 【0080】 An example of a prompt to input into a generative AI model would be: "Analyze the user's foreign language learning progress and suggest the next learning steps. For example, tell me the vocabulary and grammar points the user learned this week." This prompt allows the AI ​​model to provide appropriate advice and feedback. 【0081】 This invention provides a mechanism for efficiently supporting user learning using hardware and software. 【0082】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0083】 Step 1: 【0084】 Users use a terminal to input goal setting information and learning progress information. During this process, users meticulously record their learning goals (e.g., passing an exam) and current learning status, and send this information to the server via the terminal. The input information includes the user's specific learning style and available learning time. The output is the storage of the user information sent to the server into a database. 【0085】 Step 2: 【0086】 The server records the received goal setting information and learning progress information in a database. Based on this, the generative AI model is activated, analyzes the input information, and generates an optimal learning plan tailored to the user's characteristics. This data analysis takes into account the user's past learning history and trends. The output is a learning plan optimized for each individual user. 【0087】 Step 3: 【0088】 The terminal displays the learning plan sent from the server in the user interface. The user receives this information, checks the daily learning tasks, and performs them accordingly. The displayed plan includes specific learning content and its priority. The output is a learning task list that can be visually reviewed by the learner. 【0089】 Step 4: 【0090】 The server uses a forgetting model based on learning history information to calculate the optimal review timing. The server analyzes the input learning history data to determine the most appropriate review time for the user to retain what they have learned over a long period. The output is the calculated review timing. 【0091】 Step 5: 【0092】 The device receives notifications from the server regarding review timing and informs the user. Based on these notifications, the user can review at the appropriate time. Notifications are provided to the user as pop-ups or alert messages. The output is a new notification that can be viewed on the user interface. 【0093】 Step 6: 【0094】 The server monitors the user's progress in real time and uses a generative AI model to build feedback. Input data includes the user's learning speed and comprehension level. The server analyzes this data and generates appropriate feedback and encouragement messages. The output is the feedback message delivered to the user via their device. 【0095】 Step 7: 【0096】 The terminal notifies the user of feedback generated by the server, supporting increased learning motivation. The feedback is displayed in the user interface, providing the user with encouraging messages and suggestions for improvement. The output is a display of motivating feedback for the user. 【0097】 (Application Example 1) 【0098】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0099】 There is a need to efficiently provide personalized learning support tailored to each learner's progress and level of understanding, but conventional systems have the challenge of not being able to reflect learner responses and feedback in real time. Furthermore, there is a lack of interactive notification methods to maintain learner motivation, so a method is needed to enhance sustained learning enthusiasm. 【0100】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0101】 In this invention, the server includes a generation means for receiving goal setting information and learning progress information and generating a user's learning plan; a means for calculating review timing according to a forgetting curve model based on learning history information; a means for generating and notifying feedback based on the user's progress information and level of understanding; and a means for providing interactive notifications using the learner's voice and visual input. This enables the provision of information tailored to each learner and the maintenance of their motivation. 【0102】 "Goal-setting information" refers to information that specifies the learning goals that the user wants to achieve. 【0103】 "Learning progress information" refers to data that shows the user's current learning status. 【0104】 A "learning plan" is a set of specific learning schedules and tasks designed to help the user achieve their goals. 【0105】 "Generation method" refers to the process of creating an individualized learning plan based on the user's learning goals and progress. 【0106】 "Learning history information" refers to a record of the learning activities a user has undertaken in the past. 【0107】 The "forgetting curve model" is a theoretical model that shows how human memory is lost over time. 【0108】 "Review timing" refers to the optimal time to review what you have learned in order to effectively memorize it. 【0109】 "Feedback" refers to evaluations and comments that reflect the user's level of understanding and progress in their learning. 【0110】 "Interactive notifications" refer to notifications that dynamically convey information to learners using audio and visual means. 【0111】 This invention is a system for efficiently individualizing and supporting the user's learning experience. This system operates in cooperation with a server, terminals, and users. Each of the means provided in this form is described in detail below. 【0112】 The server receives goal-setting information and learning progress information from the user and stores it in a database (e.g., MySQL®). Based on this data, the server uses a generation method to generate a learning plan that takes into account the user's learning characteristics. This plan includes the optimal tasks and their implementation schedule for achieving the user's goals. 【0113】 The device visually presents the generated learning plan to the user. For this purpose, the device is equipped with a display and provides a user interface (e.g., an Android® or iOS application). The device also uses a microphone to recognize user voice input and a speaker for audio output. 【0114】 The server analyzes learning history information and calculates review timing based on a forgetting curve model. This allows users to receive review notifications at the optimal time to solidify learned content into long-term memory. An AI algorithm (e.g., SciKit-Learn in Python) is used to calculate this review timing. 【0115】 Furthermore, the server collects user progress information in real time and generates progress evaluations and feedback. Using a generative AI model, it creates and notifies users of feedback tailored to their level of understanding. This notification also includes encouraging messages to motivate learners. 【0116】 For example, if a learner is preparing for a language exam, the server can generate a message such as, "There are grammar questions to work on in the next session. Please review them aloud," and interactively notify the learner through their device. An example of a prompt to input into the generation AI model is, "Your progress yesterday was good. Please generate today's feedback message." 【0117】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0118】 Step 1: 【0119】 The user enters their learning goals and current progress into the terminal. The terminal sends this information to the server. The entered information includes weekly study time and the name of the target exam. The server receives this information and stores it in a database. 【0120】 Step 2: 【0121】 The server analyzes the received goal setting and progress information and uses a generation mechanism to create an individualized learning plan. In this process, a generative AI model is used to generate optimal learning tasks and schedules, taking into account the input learning goals. This result is then sent to the terminal. The output information includes a list of learning tasks and a schedule for their execution. 【0122】 Step 3: 【0123】 The device displays the learning plan sent from the server to the user. Learning tasks and schedules are displayed as visual output through the user interface. The user reviews this to help them begin their daily learning activities. 【0124】 Step 4: 【0125】 The server analyzes learning history information and calculates review timing using a forgetting curve model. The input is a record of past learning activities. Based on this data, an AI algorithm is used to output the optimal review timing for the user. This information will later be used for notifications. 【0126】 Step 5: 【0127】 The server collects user progress information in real time and evaluates the progress. To generate feedback tailored to the user's understanding, the progress data is input into an AI model, and feedback is output as a generated response. This feedback includes encouraging messages, among other things. 【0128】 Step 6: 【0129】 The device interactively communicates feedback and review notifications from the server to the user. It uses the speaker for audio notifications and also displays messages on the screen. Specific examples of notifications include "You have a review task for today" and "Let's focus on this in your next study session." 【0130】 Step 7: 【0131】 Users receive notifications from their devices and adjust their learning based on that feedback. For example, they might review new vocabulary or review questions they answered incorrectly. This process is important for incorporating progress information into the next plan. 【0132】 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. 【0133】 This invention is a system for supporting a user's learning activities, which combines an emotion engine to provide personalized learning support based on the user's emotional state. The system includes a server, a terminal, and a user, and comprises the following elements: goal setting information, learning progress information, generation means, learning history information, forgetting curve model, review timing, feedback, notifications, learning style, learning tasks, plan, and emotion engine. 【0134】 The server collects information entered by the user through the terminal and records goal setting information and learning progress information in a database. Based on this information, the generation device creates a learning plan tailored to the user. The learning plan includes individualized learning tasks and is optimized considering the user's learning style and available time. The terminal presents the generated learning plan to the user and supports their daily learning. 【0135】 Furthermore, the server maintains learning history information and calculates the optimal review timing using a forgetting curve model. Based on this timing, the device sends a review notification to the user. The user then performs regular reviews according to this notification to solidify the learned content into long-term memory. 【0136】 The emotion engine, a key feature of this invention, recognizes the user's emotional state in real time. This engine accurately captures the user's emotions by combining facial recognition technology, voice analysis technology, and other techniques. Emotional information is used to generate feedback and is analyzed on the server along with the user's progress. If the user exhibits positive emotions during learning, the feedback is enhanced accordingly. Conversely, if negative emotions are detected, encouragement and special learning support are provided to improve motivation. 【0137】 For example, if the server detects that a user is experiencing stress during learning, it uses an emotion engine to analyze the cause and generate advice such as, "Take a short break to relax." In this way, learning plans and feedback are dynamically adjusted according to the user's emotional state, resulting in optimal learning support. 【0138】 The following describes the processing flow. 【0139】 Step 1: 【0140】 Users input their learning goals and current progress via their device. Goals include specific passing targets and learning completion deadlines. 【0141】 Step 2: 【0142】 The device sends collected goal setting information and learning progress information to the server. The server records this information in a database. 【0143】 Step 3: 【0144】 The server uses a generation mechanism to create a user-specific learning plan. The plan includes learning tasks and a schedule based on priority. 【0145】 Step 4: 【0146】 The device receives planning information from the server and presents it visually to the user. This allows the user to check their learning tasks. 【0147】 Step 5: 【0148】 The server applies a forgetting curve model based on learning history information to calculate the timing for review. It then sends a notification to the device at the appropriate time. 【0149】 Step 6: 【0150】 The terminal displays a review notification from the server to the user and updates the review task list. The user then reviews the material accordingly. 【0151】 Step 7: 【0152】 The emotion engine analyzes the user's facial expressions and voice in real time to recognize the user's emotional state. 【0153】 Step 8: 【0154】 Based on information from the emotion engine, the server analyzes it along with the user's progress information and dynamically adjusts the learning plan and feedback. 【0155】 Step 9: 【0156】 The device displays feedback from the server and provides encouragement and advice to the user. For example, if stress is detected, it might advise, "Try taking a short break." 【0157】 Step 10: 【0158】 The user enters the results of completed learning tasks into the device. The device synchronizes this information with the server and updates the database as progress information. 【0159】 These processing steps optimize the user's learning experience individually, enabling dynamic feedback and learning support that takes emotional states into account. 【0160】 (Example 2) 【0161】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0162】 Modern learning support systems generally fail to adequately address the individual emotional states and learning styles that students face. In particular, the lack of dynamic learning support based on learners' emotional fluctuations and individual time allocations is a challenge that impairs learning efficiency and motivation. 【0163】 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. 【0164】 In this invention, the server includes a calculation means for receiving goal setting information and progress information and generating a user's learning plan; a calculation means for calculating review timing according to a forgetting pattern model based on past learning information; a communication means for generating and notifying feedback based on the user's progress information and level of understanding; and a means for identifying the user's emotional state using emotion analysis technology and dynamically adjusting learning support based on it. This enables optimal learning support tailored to the user's individual learning style and emotions. 【0165】 "Goal setting information" refers to information about the learning goals that users wish to achieve. 【0166】 "Progress information" refers to information about the user's level of achievement and the amount of learning they have done as they progress through their studies. 【0167】 "Computational means" refers to computing devices or algorithms used to perform specific data processing. 【0168】 A "forgetting pattern model" refers to a mathematical model used to predict the retention rate of learned information over time. 【0169】 "Review timing" refers to the appropriate period for reviewing material to prevent forgetting. 【0170】 "Communication means" refers to the configuration of hardware or software used to transmit information. 【0171】 "Emotional analysis technology" refers to technical methods used to identify a user's emotional state. 【0172】 A "learning plan" refers to a plan that systematically arranges the learning tasks and schedules necessary to achieve the goals set by the user. 【0173】 "Feedback" refers to the responses and advice provided to users based on their learning progress. 【0174】 This invention is an advanced system for supporting users' learning activities, and is particularly characterized by the generation of personalized learning plans and the provision of dynamic feedback based on emotional states. 【0175】 The system primarily consists of servers, terminals, and users. The servers function as the central hub for information processing, utilizing high-performance computing devices and cloud services. Specifically, they employ data management software commonly used as databases and execute algorithms using programming languages ​​such as Python. 【0176】 The server receives goal setting information and progress information entered by the user through their terminal, and generates a learning plan based on this information. This learning plan is optimized for the user's learning style and available time. 【0177】 The terminal is primarily a user interface device, displaying generated learning plans and feedback. This includes mobile devices and personal computers, allowing users to intuitively manage their learning. The terminal is equipped with software to display the user interface and can receive notifications from the server in real time. 【0178】 The server also uses a forgetting curve model to calculate the optimal timing for review. This allows it to provide users with efficient review notifications. Furthermore, it incorporates an emotion engine that combines facial recognition and voice analysis technologies to analyze the user's emotional state. This automatically generates appropriate feedback based on positive or negative emotions. 【0179】 For example, if a user is experiencing stress while learning, the server analyzes the situation and provides advice through the device, such as "Take a short break to relax." This kind of adaptive learning support allows learners to continue learning efficiently in a way that suits their own pace and style. 【0180】 An example of a prompt for a generative AI model is, "If the user is feeling stressed, generate and present specific advice to improve learning efficiency." By using this prompt, the system can dynamically generate feedback that is appropriate to the situation. 【0181】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0182】 Step 1: 【0183】 The server receives goal setting information and progress information entered by the user from the terminal. Specifically, the terminal sends data about the user's goals and progress to the server via the network. The input data includes the user's learning goals and current achievement status. The server stores this data in a database, which forms the basis for subsequent calculations. 【0184】 Step 2: 【0185】 The server uses the received goal setting and progress information to generate a learning plan, taking into account available resources. A Python program runs here, applying algorithms based on the collected data. The output is a personalized learning plan, which includes learning tasks and schedules tailored to each user. 【0186】 Step 3: 【0187】 The server retrieves past learning history information and uses a forgetting curve model to calculate the optimal review timing. Past learning data is used as input, and the script calculates the optimal review period based on this data. The server outputs the calculation result and notifies the user in the next step. The specific operation involves computational processing utilizing mathematical models. 【0188】 Step 4: 【0189】 The server sends the generated study plan and review timings to the device. The device receives this information and notifies the user using the appropriate tool. The input data is the plan and timing information from the server, and the device's output displays the study schedule and reminders on the user interface. Specifically, the device's user interface is designed to receive notifications. 【0190】 Step 5: 【0191】 The server uses emotion analysis technology to identify the user's emotional state in real time. Input information includes voice data and facial image data collected from the device. Based on this information, the server applies an emotion analysis algorithm to determine the user's emotional state. The output is evaluation information based on the user's emotions. 【0192】 Step 6: 【0193】 The server generates feedback for the user based on the analyzed emotional state. It uses emotional assessment information and learning progress information as input data. A generative AI model automatically generates appropriate feedback, and prompts such as "If the user is experiencing stress, generate and present specific advice to improve learning efficiency" are applied. The generated feedback is sent to the user's device as a notification. 【0194】 (Application Example 2) 【0195】 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". 【0196】 For learners to sustain their learning, not only is an optimized learning plan necessary, but support tailored to their individual emotional states is also required. However, conventional systems have struggled to adequately capture changes in users' emotions, making it difficult to provide immediate support. Dynamic learning support based on emotions is needed to improve learner motivation and enhance learning efficiency. 【0197】 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. 【0198】 In this invention, the server includes a generation means for receiving goal setting information and learning progress information and generating a user learning plan; a means for calculating review timing according to a forgetting curve model based on learning history information; a means for generating and notifying feedback based on the user's progress information and level of understanding; and a means for analyzing the emotional state using emotion recognition means and dynamically adjusting the learning plan and feedback accordingly. This enables efficient learning support that takes into account the user's emotional state. 【0199】 "Goal-setting information" refers to information related to the learning objectives and goals that the user wants to achieve. 【0200】 "Learning progress information" refers to information that shows the progress and results of a user's ongoing learning activities. 【0201】 A "generation means" is a device or system that has the function of creating a learning plan tailored to individual users based on goal setting information and learning progress information. 【0202】 "Learning history information" refers to information that records the learning history and past activities that a user has undertaken so far. 【0203】 A "forgetting curve model" is a model that mathematically represents the time it takes for a user to forget information they have learned, as well as the patterns involved. 【0204】 A "means for calculating review timing" refers to a device or system that uses learning history information and a forgetting curve model to calculate the optimal timing for review for the user. 【0205】 "Means for generating and notifying feedback" refers to devices or systems that provide learning evaluations and next steps based on the user's progress and understanding, and notify the user accordingly. 【0206】 "Emotion recognition means" refers to technologies and devices for identifying and analyzing emotions from a user's facial expressions and voice. 【0207】 "Means for analyzing emotional states and dynamically adjusting learning plans and feedback based on them" refers to devices and systems that evaluate a user's emotions in real time and quickly adapt and modify individual learning plans and feedback based on the results. 【0208】 To implement this invention, the learning support system is centered around a server, a terminal, and a user. The server manages goal-setting information and learning progress information obtained from the user and uses a generation means to create a personalized learning plan for the user. This plan takes into account the user's learning style and available time. The server also maintains learning history information, calculates the optimal review timing using a forgetting curve model, and notifies the user of this timing via the terminal. 【0209】 The emotion recognition feature is implemented in a device equipped with a camera to analyze the user's facial expressions and a microphone to analyze their voice. For facial recognition, for example, the Face++ API can be used, and for voice analysis, Google® Cloud Speech-to-Text API can be used. This emotion data is sent to a server in real time to help generate feedback based on learning progress and comprehension. Specifically, if the emotional state is negative, special learning tasks are suggested to increase motivation, and if it is positive, feedback is provided to further reinforce that state. 【0210】 For example, when elementary school students are studying at home using smart devices, an emotion recognition system evaluates the user's level of concentration and, if it determines that the user is temporarily feeling tired, displays advice such as, "Let's take a short break." This allows the user to refresh and resume studying efficiently. 【0211】 Examples of prompts used when utilizing generative AI models include: "If the user has a distracted expression, what words of encouragement should the robot offer?" or "If the user's voice contains signs of stress, what is an appropriate learning task to help restore the user's motivation?" 【0212】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0213】 Step 1: 【0214】 The server receives goal setting information and learning progress information from the user as input. Based on this information, the generation mechanism creates a learning plan optimized for each individual user and outputs the results to the database. 【0215】 Step 2: 【0216】 The device uses its camera and microphone to capture the user's facial expressions and voice as input. This data is then analyzed using facial recognition technology (e.g., Face++ API) and voice analysis technology (e.g., Google Cloud Speech-to-Text API) and output as emotion data. 【0217】 Step 3: 【0218】 The server analyzes emotional data and compares it with learning history information. It applies a forgetting curve model to calculate the optimal review timing and sends a review notification to the user. 【0219】 Step 4: 【0220】 The terminal displays the learning plan sent from the server to the user, providing daily learning support. As the user engages in learning through the terminal, progress information is updated and output to the server. 【0221】 Step 5: 【0222】 The server comprehensively analyzes the user's progress, understanding, and emotional state to generate appropriate feedback. This feedback is then sent to the device and notified to the user. 【0223】 Step 6: 【0224】 If a user is experiencing stress, the server analyzes the contributing factors and uses a generative AI model to generate recommendations to improve their motivation. These recommendations are then output to the terminal as prompts. A concrete example of such advice might be, "Take a short break." 【0225】 The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data. 【0226】 Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0227】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0228】 [Second Embodiment] 【0229】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0230】 As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server. 【0231】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0232】 The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52. 【0233】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0234】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0235】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0236】 Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0237】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0238】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0239】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0240】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0241】 This invention is a system in which a server, terminal, and user work together to efficiently personalize and support the user's learning experience. The main elements of the system include goal setting information, learning progress information, generation means, learning history information, forgetting curve model, review timing, feedback, notifications, learning style, learning tasks, and planning. 【0242】 The server receives goal setting information and learning progress information provided by the user and records it in a database. Based on this, the generation device creates a learning plan tailored to the user's characteristics. This plan is customized to support the user in achieving their goals and sets the priorities and schedules of learning tasks. The terminal displays this plan in the user interface, allowing the user to check their daily learning tasks. 【0243】 Furthermore, the server maintains learning history information and uses a forgetting curve model to calculate the optimal review timing. This review timing is designed to ensure that the learned content is retained in long-term memory, and the device notifies the user of the need for review. 【0244】 Furthermore, by collecting and analyzing progress information in real time, the system evaluates the user's learning status and generates feedback based on the user's understanding and progress. This feedback is not only an evaluation based on progress information, but also includes encouraging messages to stimulate the user's motivation to learn, and is notified to the user from their device. 【0245】 For example, if a user aims to pass a foreign language proficiency test, the server sets "Passing a Foreign Language Proficiency Test" as the user's goal and creates a learning plan that includes daily vocabulary study and grammar practice based on the user's available study time and style. At the same time, it uses a forgetting curve model to notify the user of review tasks at appropriate times, thereby ensuring retention of the learned material. If progress is good, it generates a message such as "Great job this week! You're making steady progress!" to support the user's learning. This creates an environment where users can continue learning efficiently and enthusiastically. 【0246】 The following describes the processing flow. 【0247】 Step 1: 【0248】 The server receives goal setting information and learning progress information from the user and records it in the database. The user inputs specific goals, current progress, and available time for learning via their device. 【0249】 Step 2: 【0250】 The server uses a generation mechanism to create a user-specific learning plan based on recorded goal setting information and progress information. This learning plan includes smaller tasks arranged in order of priority and includes an achievable schedule. 【0251】 Step 3: 【0252】 The device displays a generated learning plan to the user, allowing them to visually confirm their daily learning tasks. The user then begins learning according to the plan. 【0253】 Step 4: 【0254】 The server collects learning history information from the user and periodically applies a forgetting curve model to calculate when it's time for review. When it's time to review, the server sends a notification to the device. 【0255】 Step 5: 【0256】 The device displays a review notification to the user and lists the scheduled review tasks. The user then reviews the material based on the notification and reviews the learned content. 【0257】 Step 6: 【0258】 After a user completes a learning task, their progress is recorded on their device. The device then synchronizes this data with a server and saves it to a database as the latest progress. 【0259】 Step 7: 【0260】 The server generates feedback based on the user's new progress information, evaluating their understanding and learning progress. It also generates encouraging messages to help maintain user motivation. 【0261】 Step 8: 【0262】 The device notifies the user of any generated feedback or messages, allowing them to visually confirm them. Based on this, the user then proceeds to the next learning task. 【0263】 (Example 1) 【0264】 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." 【0265】 In today's learning environment, it is difficult to develop learning plans tailored to individual learners and to support efficient learning progress. In particular, there is a need for effective feedback that matches learners' diverse goals and progress, as well as appropriate review timing to encourage repeated learning. Therefore, creating an environment where learners can continue to learn with motivation is a crucial challenge. 【0266】 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. 【0267】 In this invention, the server includes an information processing device that receives goal setting information and learning progress information and generates a learning plan based on user characteristics; a processing device that calculates the optimal review timing according to a forgetting model based on learning history information; and an information processing device that generates an evaluation based on the user's progress and level of understanding and notifies the user through an output device. This enables the formulation of an optimal learning plan for achieving individual learner goals, as well as effective feedback and notification of review timing. 【0268】 "Goal setting information" refers to information about the specific learning goals that the user wishes to achieve. 【0269】 "Learning progress information" refers to information that shows how far a user has progressed towards their goal. 【0270】 "User characteristics" refer to individual features of each user, including their learning style, preferences, and time management abilities. 【0271】 "Learning history information" refers to records of learning activities a user has undertaken and the results thereof. 【0272】 A "forgetting model" is a mathematical model that describes how learned material is forgotten over time. 【0273】 The "review period" is the ideal time for learners to reconfirm what they have learned in the past. 【0274】 An "information processing device" is hardware or software used for collecting, analyzing, and processing data. 【0275】 An "output device" is hardware or software used to convey calculation results or information to the user. 【0276】 "Evaluation" is a judgment made based on the user's learning progress and level of understanding. 【0277】 "Notification" refers to the act of informing a user of information, or a function within a system that does so. 【0278】 This invention is an information processing system for effectively supporting user learning. The system primarily functions through the coordinated operation of three elements: a server, a terminal, and a user. 【0279】 The server receives goal setting information and learning progress information from the user. Specifically, it stores information entered by the user via their device in a database. This allows for the accumulation of data based on the user's learning status and goals. The server analyzes this information and uses a generative AI model to generate an optimal learning plan for the user. This learning plan includes personalized learning tasks and their implementation schedules. 【0280】 The device displays the learning plan sent from the server on the user interface. This user interface serves as a guide for the user to check their daily learning tasks and proceed with their studies accordingly. The device also receives and displays notifications from the server to the user. These include calculations of review timing based on forgetting models and feedback messages according to progress. 【0281】 A key feature of this system is that the server uses a forgetting model based on learning history information to calculate the optimal review timing. This review timing is then notified to the user via their device. This allows users to efficiently retain the learned material in their memory. 【0282】 As a concrete example, consider a user aiming to pass a foreign language proficiency test. The user sets "Passing a foreign language proficiency test" as their goal on their device, and the server generates a learning plan based on that goal, such as "Spending a certain amount of time each day studying vocabulary and grammar." Furthermore, the server uses a forgetting model to calculate specific review timings, such as "Review the words you learned in one week," and notifies the user. Depending on the progress, it is also possible to generate encouraging messages such as, "You're doing great! Keep it up!" 【0283】 As an example of a prompt sentence to be input into the generation AI model, there is one such as "Analyze the progress of the user's foreign language learning and propose the next learning step. As a specific example, please tell me the vocabulary and grammar items that the user has learned this week." With this prompt, the AI model can provide appropriate advice and feedback. 【0284】 This invention provides a mechanism for efficiently assisting the user's learning using hardware and software. 【0285】 The flow of the specific process in Example 1 will be described using FIG. 11. 【0286】 Step 1: 【0287】 The user uses the terminal to input target setting information and learning progress information. At this time, the user records in detail the learning goal (e.g., passing an exam) and the current learning situation, and transmits it to the server through the terminal. The information to be input includes the user-specific learning style and available learning time. The output is the storage of the user information sent to the server in the database. 【0288】 Step 2: 【0289】 The server records the received target setting information and learning progress information in the database. Based on this, the generation AI model is activated, analyzes the input information, and generates an optimal learning plan according to the user characteristics. In this data analysis, the user's past learning history and its trends are considered. The output is a learning plan optimized for each individual user. 【0290】 Step 3: 【0291】 The terminal displays the learning plan sent from the server in the user interface. The user receives this information, checks the daily learning tasks, and performs them accordingly. The displayed plan includes specific learning content and its priority. The output is a learning task list that can be visually reviewed by the learner. 【0292】 Step 4: 【0293】 The server uses a forgetting model based on learning history information to calculate the optimal review timing. The server analyzes the input learning history data to determine the most appropriate review time for the user to retain what they have learned over a long period. The output is the calculated review timing. 【0294】 Step 5: 【0295】 The device receives notifications from the server regarding review timing and informs the user. Based on these notifications, the user can review at the appropriate time. Notifications are provided to the user as pop-ups or alert messages. The output is a new notification that can be viewed on the user interface. 【0296】 Step 6: 【0297】 The server monitors the user's progress in real time and uses a generative AI model to build feedback. Input data includes the user's learning speed and comprehension level. The server analyzes this data and generates appropriate feedback and encouragement messages. The output is the feedback message delivered to the user via their device. 【0298】 Step 7: 【0299】 The terminal notifies the user of feedback generated by the server, supporting increased learning motivation. The feedback is displayed in the user interface, providing the user with encouraging messages and suggestions for improvement. The output is a display of motivating feedback for the user. 【0300】 (Application Example 1) 【0301】 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." 【0302】 There is a need to efficiently provide personalized learning support tailored to each learner's progress and level of understanding, but conventional systems have the challenge of not being able to reflect learner responses and feedback in real time. Furthermore, there is a lack of interactive notification methods to maintain learner motivation, so a method is needed to enhance sustained learning enthusiasm. 【0303】 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. 【0304】 In this invention, the server includes a generation means for receiving goal setting information and learning progress information and generating a user's learning plan; a means for calculating review timing according to a forgetting curve model based on learning history information; a means for generating and notifying feedback based on the user's progress information and level of understanding; and a means for providing interactive notifications using the learner's voice and visual input. This enables the provision of information tailored to each learner and the maintenance of their motivation. 【0305】 "Goal-setting information" refers to information that specifies the learning goals that the user wants to achieve. 【0306】 "Learning progress information" refers to data that shows the user's current learning status. 【0307】 A "learning plan" is a set of specific learning schedules and tasks designed to achieve the user's goals. 【0308】 The "generation means" refers to the process for creating an individualized learning plan based on the user's learning goals and progress. 【0309】 "Learning history information" is a record of the user's past learning activities. 【0310】 The "forgetting curve model" is a theoretical model that shows how human memory is lost over time. 【0311】 "Review timing" refers to the optimal time for review to effectively memorize the learned content. 【0312】 "Feedback" refers to evaluations and comments according to the user's learning comprehension and progress. 【0313】 "Interactive notification" means a notification that dynamically conveys information to the learner using voice or vision. 【0314】 [[ID=Q28]]This invention is a system for efficiently individualizing and supporting the user's learning experience. This system operates in cooperation with a server, a terminal, and a user. Each means provided in this form will be specifically described below. 【0315】 The server receives the goal setting information and learning progress information sent from the user and stores it in a database (e.g., MySQL). Based on this data, the server uses the generation means to generate a learning plan considering the user's learning characteristics. This plan includes the optimal tasks for achieving the user's goals and their implementation schedule. 【0316】 The device visually presents the generated learning plan to the user. For this purpose, the device is equipped with a display and provides a user interface (e.g., an Android or iOS application). Additionally, the device uses a microphone to recognize user voice input and a speaker for audio output. 【0317】 The server analyzes learning history information and calculates review timing based on a forgetting curve model. This allows users to receive review notifications at the optimal time to solidify learned content into long-term memory. An AI algorithm (e.g., SciKit-Learn in Python) is used to calculate this review timing. 【0318】 Furthermore, the server collects user progress information in real time and generates progress evaluations and feedback. Using a generative AI model, it creates and notifies users of feedback tailored to their level of understanding. This notification also includes encouraging messages to motivate learners. 【0319】 For example, if a learner is preparing for a language exam, the server can generate a message such as, "There are grammar questions to work on in the next session. Please review them aloud," and interactively notify the learner through their device. An example of a prompt to input into the generation AI model is, "Your progress yesterday was good. Please generate today's feedback message." 【0320】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0321】 Step 1: 【0322】 The user enters their learning goals and current progress into the terminal. The terminal sends this information to the server. The entered information includes weekly study time and the name of the target exam. The server receives this information and stores it in a database. 【0323】 Step 2: 【0324】 The server analyzes the received goal setting and progress information and uses a generation mechanism to create an individualized learning plan. In this process, a generative AI model is used to generate optimal learning tasks and schedules, taking into account the input learning goals. This result is then sent to the terminal. The output information includes a list of learning tasks and a schedule for their execution. 【0325】 Step 3: 【0326】 The device displays the learning plan sent from the server to the user. Learning tasks and schedules are displayed as visual output through the user interface. The user reviews this to help them begin their daily learning activities. 【0327】 Step 4: 【0328】 The server analyzes learning history information and calculates review timing using a forgetting curve model. The input is a record of past learning activities. Based on this data, an AI algorithm is used to output the optimal review timing for the user. This information will later be used for notifications. 【0329】 Step 5: 【0330】 The server collects user progress information in real time and evaluates the progress. To generate feedback tailored to the user's understanding, the progress data is input into an AI model, and feedback is output as a generated response. This feedback includes encouraging messages, among other things. 【0331】 Step 6: 【0332】 The device interactively communicates feedback and review notifications from the server to the user. It uses the speaker for audio notifications and also displays messages on the screen. Specific examples of notifications include "You have a review task for today" and "Let's focus on this in your next study session." 【0333】 Step 7: 【0334】 Users receive notifications from their devices and adjust their learning based on that feedback. For example, they might review new vocabulary or review questions they answered incorrectly. This process is important for incorporating progress information into the next plan. 【0335】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0336】 This invention is a system for supporting a user's learning activities, which combines an emotion engine to provide personalized learning support based on the user's emotional state. The system includes a server, a terminal, and a user, and comprises the following elements: goal setting information, learning progress information, generation means, learning history information, forgetting curve model, review timing, feedback, notifications, learning style, learning tasks, plan, and emotion engine. 【0337】 The server collects information entered by the user through the terminal and records goal setting information and learning progress information in a database. Based on this information, the generation device creates a learning plan tailored to the user. The learning plan includes individualized learning tasks and is optimized considering the user's learning style and available time. The terminal presents the generated learning plan to the user and supports their daily learning. 【0338】 Furthermore, the server maintains learning history information and calculates the optimal review timing using a forgetting curve model. Based on this timing, the device sends a review notification to the user. The user then performs regular reviews according to this notification to solidify the learned content into long-term memory. 【0339】 The emotion engine, a key feature of this invention, recognizes the user's emotional state in real time. This engine accurately captures the user's emotions by combining facial recognition technology, voice analysis technology, and other techniques. Emotional information is used to generate feedback and is analyzed on the server along with the user's progress. If the user exhibits positive emotions during learning, the feedback is enhanced accordingly. Conversely, if negative emotions are detected, encouragement and special learning support are provided to improve motivation. 【0340】 For example, if the server detects that a user is experiencing stress during learning, it uses an emotion engine to analyze the cause and generate advice such as, "Take a short break to relax." In this way, learning plans and feedback are dynamically adjusted according to the user's emotional state, resulting in optimal learning support. 【0341】 The following describes the processing flow. 【0342】 Step 1: 【0343】 Users input their learning goals and current progress via their device. Goals include specific passing targets and learning completion deadlines. 【0344】 Step 2: 【0345】 The device sends collected goal setting information and learning progress information to the server. The server records this information in a database. 【0346】 Step 3: 【0347】 The server uses a generation mechanism to create a user-specific learning plan. The plan includes learning tasks and a schedule based on priority. 【0348】 Step 4: 【0349】 The device receives planning information from the server and presents it visually to the user. This allows the user to check their learning tasks. 【0350】 Step 5: 【0351】 The server applies a forgetting curve model based on learning history information to calculate the timing for review. It then sends a notification to the device at the appropriate time. 【0352】 Step 6: 【0353】 The terminal displays a review notification from the server to the user and updates the review task list. The user then reviews the material accordingly. 【0354】 Step 7: 【0355】 The emotion engine analyzes the user's facial expressions and voice in real time to recognize the user's emotional state. 【0356】 Step 8: 【0357】 Based on information from the emotion engine, the server analyzes it along with the user's progress information and dynamically adjusts the learning plan and feedback. 【0358】 Step 9: 【0359】 The device displays feedback from the server and provides encouragement and advice to the user. For example, if stress is detected, it might advise, "Try taking a short break." 【0360】 Step 10: 【0361】 The user enters the results of completed learning tasks into the device. The device synchronizes this information with the server and updates the database as progress information. 【0362】 These processing steps optimize the user's learning experience individually, enabling dynamic feedback and learning support that takes emotional states into account. 【0363】 (Example 2) 【0364】 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". 【0365】 Modern learning support systems generally fail to adequately address the individual emotional states and learning styles that students face. In particular, the lack of dynamic learning support based on learners' emotional fluctuations and individual time allocations is a challenge that impairs learning efficiency and motivation. 【0366】 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. 【0367】 In this invention, the server includes a calculation means for receiving goal setting information and progress information and generating a user's learning plan; a calculation means for calculating review timing according to a forgetting pattern model based on past learning information; a communication means for generating and notifying feedback based on the user's progress information and level of understanding; and a means for identifying the user's emotional state using emotion analysis technology and dynamically adjusting learning support based on it. This enables optimal learning support tailored to the user's individual learning style and emotions. 【0368】 "Goal setting information" refers to information about the learning goals that users wish to achieve. 【0369】 "Progress information" refers to information about the user's level of achievement and the amount of learning they have done as they progress through their studies. 【0370】 "Computational means" refers to computing devices or algorithms used to perform specific data processing. 【0371】 A "forgetting pattern model" refers to a mathematical model used to predict the retention rate of learned information over time. 【0372】 "Review timing" refers to the appropriate period for reviewing material to prevent forgetting. 【0373】 "Communication means" refers to the configuration of hardware or software used to transmit information. 【0374】 "Emotional analysis technology" refers to technical methods used to identify a user's emotional state. 【0375】 A "learning plan" refers to a plan that systematically arranges the learning tasks and schedules necessary to achieve the goals set by the user. 【0376】 "Feedback" refers to the responses and advice provided to users based on their learning progress. 【0377】 This invention is an advanced system for supporting users' learning activities, and is particularly characterized by the generation of personalized learning plans and the provision of dynamic feedback based on emotional states. 【0378】 The system primarily consists of servers, terminals, and users. The servers function as the central hub for information processing, utilizing high-performance computing devices and cloud services. Specifically, they employ data management software commonly used as databases and execute algorithms using programming languages ​​such as Python. 【0379】 The server receives goal setting information and progress information entered by the user through their terminal, and generates a learning plan based on this information. This learning plan is optimized for the user's learning style and available time. 【0380】 The terminal is primarily a user interface device, displaying generated learning plans and feedback. This includes mobile devices and personal computers, allowing users to intuitively manage their learning. The terminal is equipped with software to display the user interface and can receive notifications from the server in real time. 【0381】 The server also uses a forgetting curve model to calculate the optimal timing for review. This allows it to provide users with efficient review notifications. Furthermore, it incorporates an emotion engine that combines facial recognition and voice analysis technologies to analyze the user's emotional state. This automatically generates appropriate feedback based on positive or negative emotions. 【0382】 For example, if a user is experiencing stress while learning, the server analyzes the situation and provides advice through the device, such as "Take a short break to relax." This kind of adaptive learning support allows learners to continue learning efficiently in a way that suits their own pace and style. 【0383】 An example of a prompt for a generative AI model is, "If the user is feeling stressed, generate and present specific advice to improve learning efficiency." By using this prompt, the system can dynamically generate feedback that is appropriate to the situation. 【0384】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0385】 Step 1: 【0386】 The server receives goal setting information and progress information entered by the user from the terminal. Specifically, the terminal sends data about the user's goals and progress to the server via the network. The input data includes the user's learning goals and current achievement status. The server stores this data in a database, which forms the basis for subsequent calculations. 【0387】 Step 2: 【0388】 The server uses the received goal setting and progress information to generate a learning plan, taking into account available resources. A Python program runs here, applying algorithms based on the collected data. The output is a personalized learning plan, which includes learning tasks and schedules tailored to each user. 【0389】 Step 3: 【0390】 The server retrieves past learning history information and uses a forgetting curve model to calculate the optimal review timing. Past learning data is used as input, and the script calculates the optimal review period based on this data. The server outputs the calculation result and notifies the user in the next step. The specific operation involves computational processing utilizing mathematical models. 【0391】 Step 4: 【0392】 The server sends the generated study plan and review timings to the device. The device receives this information and notifies the user using the appropriate tool. The input data is the plan and timing information from the server, and the device's output displays the study schedule and reminders on the user interface. Specifically, the device's user interface is designed to receive notifications. 【0393】 Step 5: 【0394】 The server uses emotion analysis technology to identify the user's emotional state in real time. Input information includes voice data and facial image data collected from the device. Based on this information, the server applies an emotion analysis algorithm to determine the user's emotional state. The output is evaluation information based on the user's emotions. 【0395】 Step 6: 【0396】 The server generates feedback for the user based on the analyzed emotional state. It uses emotional assessment information and learning progress information as input data. A generative AI model automatically generates appropriate feedback, and prompts such as "If the user is experiencing stress, generate and present specific advice to improve learning efficiency" are applied. The generated feedback is sent to the user's device as a notification. 【0397】 (Application Example 2) 【0398】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0399】 For learners to sustain their learning, not only is an optimized learning plan necessary, but support tailored to their individual emotional states is also required. However, conventional systems have struggled to adequately capture changes in users' emotions, making it difficult to provide immediate support. Dynamic learning support based on emotions is needed to improve learner motivation and enhance learning efficiency. 【0400】 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. 【0401】 In this invention, the server includes a generation means for receiving goal setting information and learning progress information and generating a user learning plan; a means for calculating review timing according to a forgetting curve model based on learning history information; a means for generating and notifying feedback based on the user's progress information and level of understanding; and a means for analyzing the emotional state using emotion recognition means and dynamically adjusting the learning plan and feedback accordingly. This enables efficient learning support that takes into account the user's emotional state. 【0402】 "Goal-setting information" refers to information related to the learning objectives and goals that the user wants to achieve. 【0403】 "Learning progress information" refers to information that shows the progress and results of a user's ongoing learning activities. 【0404】 A "generation means" is a device or system that has the function of creating a learning plan tailored to individual users based on goal setting information and learning progress information. 【0405】 "Learning history information" refers to information that records the learning history and past activities that a user has undertaken so far. 【0406】 A "forgetting curve model" is a model that mathematically represents the time it takes for a user to forget information they have learned, as well as the patterns involved. 【0407】 A "means for calculating review timing" refers to a device or system that uses learning history information and a forgetting curve model to calculate the optimal timing for review for the user. 【0408】 "Means for generating and notifying feedback" refers to devices or systems that provide learning evaluations and next steps based on the user's progress and understanding, and notify the user accordingly. 【0409】 "Emotion recognition means" refers to technologies and devices for identifying and analyzing emotions from a user's facial expressions and voice. 【0410】 "Means for analyzing emotional states and dynamically adjusting learning plans and feedback based on them" refers to devices and systems that evaluate a user's emotions in real time and quickly adapt and modify individual learning plans and feedback based on the results. 【0411】 To implement this invention, the learning support system is centered around a server, a terminal, and a user. The server manages goal-setting information and learning progress information obtained from the user and uses a generation means to create a personalized learning plan for the user. This plan takes into account the user's learning style and available time. The server also maintains learning history information, calculates the optimal review timing using a forgetting curve model, and notifies the user of this timing via the terminal. 【0412】 The emotion recognition feature is implemented in a device equipped with a camera to analyze the user's facial expressions and a microphone to analyze their voice. For facial recognition, for example, the Face++ API can be used, and for voice analysis, the Google Cloud Speech-to-Text API can be used. This emotion data is sent to a server in real time and helps generate feedback based on learning progress and comprehension. In particular, if the emotional state is negative, special learning tasks are suggested to increase motivation, and if it is positive, feedback is provided to further reinforce that state. 【0413】 For example, when elementary school students are studying at home using smart devices, an emotion recognition system evaluates the user's level of concentration and, if it determines that the user is temporarily feeling tired, displays advice such as, "Let's take a short break." This allows the user to refresh and resume studying efficiently. 【0414】 Examples of prompts used when utilizing generative AI models include: "If the user has a distracted expression, what words of encouragement should the robot offer?" or "If the user's voice contains signs of stress, what is an appropriate learning task to help restore the user's motivation?" 【0415】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0416】 Step 1: 【0417】 The server receives goal setting information and learning progress information from the user as input. Based on this information, the generation mechanism creates a learning plan optimized for each individual user and outputs the results to the database. 【0418】 Step 2: 【0419】 The device uses its camera and microphone to capture the user's facial expressions and voice as input. This data is then analyzed using facial recognition technology (e.g., Face++ API) and voice analysis technology (e.g., Google Cloud Speech-to-Text API) and output as emotion data. 【0420】 Step 3: 【0421】 The server analyzes emotional data and compares it with learning history information. It applies a forgetting curve model to calculate the optimal review timing and sends a review notification to the user. 【0422】 Step 4: 【0423】 The terminal displays the learning plan sent from the server to the user, providing daily learning support. As the user engages in learning through the terminal, progress information is updated and output to the server. 【0424】 Step 5: 【0425】 The server comprehensively analyzes the user's progress, understanding, and emotional state to generate appropriate feedback. This feedback is then sent to the device and notified to the user. 【0426】 Step 6: 【0427】 If a user is experiencing stress, the server analyzes the contributing factors and uses a generative AI model to generate recommendations to improve their motivation. These recommendations are then output to the terminal as prompts. A concrete example of such advice might be, "Take a short break." 【0428】 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. 【0429】 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. 【0430】 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. 【0431】 [Third Embodiment] 【0432】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0433】 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. 【0434】 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). 【0435】 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. 【0436】 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. 【0437】 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). 【0438】 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. 【0439】 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. 【0440】 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. 【0441】 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. 【0442】 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. 【0443】 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". 【0444】 This invention is a system in which a server, terminal, and user work together to efficiently personalize and support the user's learning experience. The main elements of the system include goal setting information, learning progress information, generation means, learning history information, forgetting curve model, review timing, feedback, notifications, learning style, learning tasks, and planning. 【0445】 The server receives goal setting information and learning progress information provided by the user and records it in a database. Based on this, the generation device creates a learning plan tailored to the user's characteristics. This plan is customized to support the user in achieving their goals and sets the priorities and schedules of learning tasks. The terminal displays this plan in the user interface, allowing the user to check their daily learning tasks. 【0446】 Furthermore, the server maintains learning history information and uses a forgetting curve model to calculate the optimal review timing. This review timing is designed to ensure that the learned content is retained in long-term memory, and the device notifies the user of the need for review. 【0447】 Furthermore, by collecting and analyzing progress information in real time, the system evaluates the user's learning status and generates feedback based on the user's understanding and progress. This feedback is not only an evaluation based on progress information, but also includes encouraging messages to stimulate the user's motivation to learn, and is notified to the user from their device. 【0448】 For example, if a user aims to pass a foreign language proficiency test, the server sets "Passing a Foreign Language Proficiency Test" as the user's goal and creates a learning plan that includes daily vocabulary study and grammar practice based on the user's available study time and style. At the same time, it uses a forgetting curve model to notify the user of review tasks at appropriate times, thereby ensuring retention of the learned material. If progress is good, it generates a message such as "Great job this week! You're making steady progress!" to support the user's learning. This creates an environment where users can continue learning efficiently and enthusiastically. 【0449】 The following describes the processing flow. 【0450】 Step 1: 【0451】 The server receives goal setting information and learning progress information from the user and records it in the database. The user inputs specific goals, current progress, and available time for learning via their device. 【0452】 Step 2: 【0453】 The server uses a generation mechanism to create a user-specific learning plan based on recorded goal setting information and progress information. This learning plan includes smaller tasks arranged in order of priority and includes an achievable schedule. 【0454】 Step 3: 【0455】 The device displays a generated learning plan to the user, allowing them to visually confirm their daily learning tasks. The user then begins learning according to the plan. 【0456】 Step 4: 【0457】 The server collects learning history information from the user and periodically applies a forgetting curve model to calculate when it's time for review. When it's time to review, the server sends a notification to the device. 【0458】 Step 5: 【0459】 The device displays a review notification to the user and lists the scheduled review tasks. The user then reviews the material based on the notification and reviews the learned content. 【0460】 Step 6: 【0461】 After a user completes a learning task, their progress is recorded on their device. The device then synchronizes this data with a server and saves it to a database as the latest progress. 【0462】 Step 7: 【0463】 The server generates feedback based on the user's new progress information, evaluating their understanding and learning progress. It also generates encouraging messages to help maintain user motivation. 【0464】 Step 8: 【0465】 The device notifies the user of any generated feedback or messages, allowing them to visually confirm them. Based on this, the user then proceeds to the next learning task. 【0466】 (Example 1) 【0467】 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." 【0468】 In today's learning environment, it is difficult to develop learning plans tailored to individual learners and to support efficient learning progress. In particular, there is a need for effective feedback that matches learners' diverse goals and progress, as well as appropriate review timing to encourage repeated learning. Therefore, creating an environment where learners can continue to learn with motivation is a crucial challenge. 【0469】 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. 【0470】 In this invention, the server includes an information processing device that receives goal setting information and learning progress information and generates a learning plan based on user characteristics; a processing device that calculates the optimal review timing according to a forgetting model based on learning history information; and an information processing device that generates an evaluation based on the user's progress and level of understanding and notifies the user through an output device. This enables the formulation of an optimal learning plan for achieving individual learner goals, as well as effective feedback and notification of review timing. 【0471】 "Goal setting information" refers to information about the specific learning goals that the user wishes to achieve. 【0472】 "Learning progress information" refers to information that shows how far a user has progressed towards their goal. 【0473】 "User characteristics" refer to individual features of each user, including their learning style, preferences, and time management abilities. 【0474】 "Learning history information" refers to records of learning activities a user has undertaken and the results thereof. 【0475】 A "forgetting model" is a mathematical model that describes how learned material is forgotten over time. 【0476】 The "review period" is the ideal time for learners to reconfirm what they have learned in the past. 【0477】 An "information processing device" is hardware or software used for collecting, analyzing, and processing data. 【0478】 An "output device" is hardware or software used to convey calculation results or information to the user. 【0479】 "Evaluation" is a judgment made based on the user's learning progress and level of understanding. 【0480】 "Notification" refers to the act of informing a user of information, or a function within a system that does so. 【0481】 This invention is an information processing system for effectively supporting user learning. The system primarily functions through the coordinated operation of three elements: a server, a terminal, and a user. 【0482】 The server receives goal setting information and learning progress information from the user. Specifically, it stores information entered by the user via their device in a database. This allows for the accumulation of data based on the user's learning status and goals. The server analyzes this information and uses a generative AI model to generate an optimal learning plan for the user. This learning plan includes personalized learning tasks and their implementation schedules. 【0483】 The device displays the learning plan sent from the server on the user interface. This user interface serves as a guide for the user to check their daily learning tasks and proceed with their studies accordingly. The device also receives and displays notifications from the server to the user. These include calculations of review timing based on forgetting models and feedback messages according to progress. 【0484】 A key feature of this system is that the server uses a forgetting model based on learning history information to calculate the optimal review timing. This review timing is then notified to the user via their device. This allows users to efficiently retain the learned material in their memory. 【0485】 As a concrete example, consider a user aiming to pass a foreign language proficiency test. The user sets "Passing a foreign language proficiency test" as their goal on their device, and the server generates a learning plan based on that goal, such as "Spending a certain amount of time each day studying vocabulary and grammar." Furthermore, the server uses a forgetting model to calculate specific review timings, such as "Review the words you learned in one week," and notifies the user. Depending on the progress, it is also possible to generate encouraging messages such as, "You're doing great! Keep it up!" 【0486】 An example of a prompt to input into a generative AI model would be: "Analyze the user's foreign language learning progress and suggest the next learning steps. For example, tell me the vocabulary and grammar points the user learned this week." This prompt allows the AI ​​model to provide appropriate advice and feedback. 【0487】 This invention provides a mechanism for efficiently supporting user learning using hardware and software. 【0488】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0489】 Step 1: 【0490】 Users use a terminal to input goal setting information and learning progress information. During this process, users meticulously record their learning goals (e.g., passing an exam) and current learning status, and send this information to the server via the terminal. The input information includes the user's specific learning style and available learning time. The output is the storage of the user information sent to the server into a database. 【0491】 Step 2: 【0492】 The server records the received goal setting information and learning progress information in a database. Based on this, the generative AI model is activated, analyzes the input information, and generates an optimal learning plan tailored to the user's characteristics. This data analysis takes into account the user's past learning history and trends. The output is a learning plan optimized for each individual user. 【0493】 Step 3: 【0494】 The terminal displays the learning plan sent from the server in the user interface. The user receives this information, checks the daily learning tasks, and performs them accordingly. The displayed plan includes specific learning content and its priority. The output is a learning task list that can be visually reviewed by the learner. 【0495】 Step 4: 【0496】 The server uses a forgetting model based on learning history information to calculate the optimal review timing. The server analyzes the input learning history data to determine the most appropriate review time for the user to retain what they have learned over a long period. The output is the calculated review timing. 【0497】 Step 5: 【0498】 The device receives notifications from the server regarding review timing and informs the user. Based on these notifications, the user can review at the appropriate time. Notifications are provided to the user as pop-ups or alert messages. The output is a new notification that can be viewed on the user interface. 【0499】 Step 6: 【0500】 The server monitors the user's progress in real time and uses a generative AI model to build feedback. Input data includes the user's learning speed and comprehension level. The server analyzes this data and generates appropriate feedback and encouragement messages. The output is the feedback message delivered to the user via their device. 【0501】 Step 7: 【0502】 The terminal notifies the user of feedback generated by the server, supporting increased learning motivation. The feedback is displayed in the user interface, providing the user with encouraging messages and suggestions for improvement. The output is a display of motivating feedback for the user. 【0503】 (Application Example 1) 【0504】 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." 【0505】 There is a need to efficiently provide personalized learning support tailored to each learner's progress and level of understanding, but conventional systems have the challenge of not being able to reflect learner responses and feedback in real time. Furthermore, there is a lack of interactive notification methods to maintain learner motivation, so a method is needed to enhance sustained learning enthusiasm. 【0506】 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. 【0507】 In this invention, the server includes a generation means for receiving goal setting information and learning progress information and generating a user's learning plan; a means for calculating review timing according to a forgetting curve model based on learning history information; a means for generating and notifying feedback based on the user's progress information and level of understanding; and a means for providing interactive notifications using the learner's voice and visual input. This enables the provision of information tailored to each learner and the maintenance of their motivation. 【0508】 "Goal-setting information" refers to information that specifies the learning goals that the user wants to achieve. 【0509】 "Learning progress information" refers to data that shows the user's current learning status. 【0510】 A "learning plan" is a set of specific learning schedules and tasks designed to help the user achieve their goals. 【0511】 "Generation method" refers to the process of creating an individualized learning plan based on the user's learning goals and progress. 【0512】 "Learning history information" refers to a record of the learning activities a user has undertaken in the past. 【0513】 The "forgetting curve model" is a theoretical model that shows how human memory is lost over time. 【0514】 "Review timing" refers to the optimal time to review what you have learned in order to effectively memorize it. 【0515】 "Feedback" refers to evaluations and comments that reflect the user's level of understanding and progress in their learning. 【0516】 "Interactive notifications" refer to notifications that dynamically convey information to learners using audio and visual means. 【0517】 This invention is a system for efficiently individualizing and supporting the user's learning experience. This system operates in cooperation with a server, terminals, and users. Each of the means provided in this form is described in detail below. 【0518】 The server receives goal-setting information and learning progress information from the user and stores it in a database (e.g., MySQL). Based on this data, the server uses a generation mechanism to create a learning plan that takes into account the user's learning characteristics. This plan includes the optimal tasks and their implementation schedule for achieving the user's goals. 【0519】 The device visually presents the generated learning plan to the user. For this purpose, the device is equipped with a display and provides a user interface (e.g., an Android or iOS application). Additionally, the device uses a microphone to recognize user voice input and a speaker for audio output. 【0520】 The server analyzes learning history information and calculates review timing based on a forgetting curve model. This allows users to receive review notifications at the optimal time to solidify learned content into long-term memory. An AI algorithm (e.g., SciKit-Learn in Python) is used to calculate this review timing. 【0521】 Furthermore, the server collects user progress information in real time and generates progress evaluations and feedback. Using a generative AI model, it creates and notifies users of feedback tailored to their level of understanding. This notification also includes encouraging messages to motivate learners. 【0522】 For example, if a learner is preparing for a language exam, the server can generate a message such as, "There are grammar questions to work on in the next session. Please review them aloud," and interactively notify the learner through their device. An example of a prompt to input into the generation AI model is, "Your progress yesterday was good. Please generate today's feedback message." 【0523】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0524】 Step 1: 【0525】 The user enters their learning goals and current progress into the terminal. The terminal sends this information to the server. The entered information includes weekly study time and the name of the target exam. The server receives this information and stores it in a database. 【0526】 Step 2: 【0527】 The server analyzes the received goal setting and progress information and uses a generation mechanism to create an individualized learning plan. In this process, a generative AI model is used to generate optimal learning tasks and schedules, taking into account the input learning goals. This result is then sent to the terminal. The output information includes a list of learning tasks and a schedule for their execution. 【0528】 Step 3: 【0529】 The device displays the learning plan sent from the server to the user. Learning tasks and schedules are displayed as visual output through the user interface. The user reviews this to help them begin their daily learning activities. 【0530】 Step 4: 【0531】 The server analyzes learning history information and calculates review timing using a forgetting curve model. The input is a record of past learning activities. Based on this data, an AI algorithm is used to output the optimal review timing for the user. This information will later be used for notifications. 【0532】 Step 5: 【0533】 The server collects user progress information in real time and evaluates the progress. To generate feedback tailored to the user's understanding, the progress data is input into an AI model, and feedback is output as a generated response. This feedback includes encouraging messages, among other things. 【0534】 Step 6: 【0535】 The device interactively communicates feedback and review notifications from the server to the user. It uses the speaker for audio notifications and also displays messages on the screen. Specific examples of notifications include "You have a review task for today" and "Let's focus on this in your next study session." 【0536】 Step 7: 【0537】 Users receive notifications from their devices and adjust their learning based on that feedback. For example, they might review new vocabulary or review questions they answered incorrectly. This process is important for incorporating progress information into the next plan. 【0538】 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. 【0539】 This invention is a system for supporting a user's learning activities, which combines an emotion engine to provide personalized learning support based on the user's emotional state. The system includes a server, a terminal, and a user, and comprises the following elements: goal setting information, learning progress information, generation means, learning history information, forgetting curve model, review timing, feedback, notifications, learning style, learning tasks, plan, and emotion engine. 【0540】 The server collects information entered by the user through the terminal and records goal setting information and learning progress information in a database. Based on this information, the generation device creates a learning plan tailored to the user. The learning plan includes individualized learning tasks and is optimized considering the user's learning style and available time. The terminal presents the generated learning plan to the user and supports their daily learning. 【0541】 Furthermore, the server maintains learning history information and calculates the optimal review timing using a forgetting curve model. Based on this timing, the device sends a review notification to the user. The user then performs regular reviews according to this notification to solidify the learned content into long-term memory. 【0542】 The emotion engine, a key feature of this invention, recognizes the user's emotional state in real time. This engine accurately captures the user's emotions by combining facial recognition technology, voice analysis technology, and other techniques. Emotional information is used to generate feedback and is analyzed on the server along with the user's progress. If the user exhibits positive emotions during learning, the feedback is enhanced accordingly. Conversely, if negative emotions are detected, encouragement and special learning support are provided to improve motivation. 【0543】 For example, if the server detects that a user is experiencing stress during learning, it uses an emotion engine to analyze the cause and generate advice such as, "Take a short break to relax." In this way, learning plans and feedback are dynamically adjusted according to the user's emotional state, resulting in optimal learning support. 【0544】 The following describes the processing flow. 【0545】 Step 1: 【0546】 Users input their learning goals and current progress via their device. Goals include specific passing targets and learning completion deadlines. 【0547】 Step 2: 【0548】 The device sends collected goal setting information and learning progress information to the server. The server records this information in a database. 【0549】 Step 3: 【0550】 The server uses a generation mechanism to create a user-specific learning plan. The plan includes learning tasks and a schedule based on priority. 【0551】 Step 4: 【0552】 The device receives planning information from the server and presents it visually to the user. This allows the user to check their learning tasks. 【0553】 Step 5: 【0554】 The server applies a forgetting curve model based on learning history information to calculate the timing for review. It then sends a notification to the device at the appropriate time. 【0555】 Step 6: 【0556】 The terminal displays a review notification from the server to the user and updates the review task list. The user then reviews the material accordingly. 【0557】 Step 7: 【0558】 The emotion engine analyzes the user's facial expressions and voice in real time to recognize the user's emotional state. 【0559】 Step 8: 【0560】 Based on information from the emotion engine, the server analyzes it along with the user's progress information and dynamically adjusts the learning plan and feedback. 【0561】 Step 9: 【0562】 The device displays feedback from the server and provides encouragement and advice to the user. For example, if stress is detected, it might advise, "Try taking a short break." 【0563】 Step 10: 【0564】 The user enters the results of completed learning tasks into the device. The device synchronizes this information with the server and updates the database as progress information. 【0565】 These processing steps optimize the user's learning experience individually, enabling dynamic feedback and learning support that takes emotional states into account. 【0566】 (Example 2) 【0567】 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." 【0568】 Modern learning support systems generally fail to adequately address the individual emotional states and learning styles that students face. In particular, the lack of dynamic learning support based on learners' emotional fluctuations and individual time allocations is a challenge that impairs learning efficiency and motivation. 【0569】 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. 【0570】 In this invention, the server includes a calculation means for receiving goal setting information and progress information and generating a user's learning plan; a calculation means for calculating review timing according to a forgetting pattern model based on past learning information; a communication means for generating and notifying feedback based on the user's progress information and level of understanding; and a means for identifying the user's emotional state using emotion analysis technology and dynamically adjusting learning support based on it. This enables optimal learning support tailored to the user's individual learning style and emotions. 【0571】 "Goal setting information" refers to information about the learning goals that users wish to achieve. 【0572】 "Progress information" refers to information about the user's level of achievement and the amount of learning they have done as they progress through their studies. 【0573】 "Computational means" refers to computing devices or algorithms used to perform specific data processing. 【0574】 A "forgetting pattern model" refers to a mathematical model used to predict the retention rate of learned information over time. 【0575】 "Review timing" refers to the appropriate period for reviewing material to prevent forgetting. 【0576】 "Communication means" refers to the configuration of hardware or software used to transmit information. 【0577】 "Emotional analysis technology" refers to technical methods used to identify a user's emotional state. 【0578】 A "learning plan" refers to a plan that systematically arranges the learning tasks and schedules necessary to achieve the goals set by the user. 【0579】 "Feedback" refers to the responses and advice provided to users based on their learning progress. 【0580】 This invention is an advanced system for supporting users' learning activities, and is particularly characterized by the generation of personalized learning plans and the provision of dynamic feedback based on emotional states. 【0581】 The system primarily consists of servers, terminals, and users. The servers function as the central hub for information processing, utilizing high-performance computing devices and cloud services. Specifically, they employ data management software commonly used as databases and execute algorithms using programming languages ​​such as Python. 【0582】 The server receives goal setting information and progress information entered by the user through their terminal, and generates a learning plan based on this information. This learning plan is optimized for the user's learning style and available time. 【0583】 The terminal is primarily a user interface device, displaying generated learning plans and feedback. This includes mobile devices and personal computers, allowing users to intuitively manage their learning. The terminal is equipped with software to display the user interface and can receive notifications from the server in real time. 【0584】 The server also uses a forgetting curve model to calculate the optimal timing for review. This allows it to provide users with efficient review notifications. Furthermore, it incorporates an emotion engine that combines facial recognition and voice analysis technologies to analyze the user's emotional state. This automatically generates appropriate feedback based on positive or negative emotions. 【0585】 For example, if a user is experiencing stress while learning, the server analyzes the situation and provides advice through the device, such as "Take a short break to relax." This kind of adaptive learning support allows learners to continue learning efficiently in a way that suits their own pace and style. 【0586】 An example of a prompt for a generative AI model is, "If the user is feeling stressed, generate and present specific advice to improve learning efficiency." By using this prompt, the system can dynamically generate feedback that is appropriate to the situation. 【0587】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0588】 Step 1: 【0589】 The server receives goal setting information and progress information entered by the user from the terminal. Specifically, the terminal sends data about the user's goals and progress to the server via the network. The input data includes the user's learning goals and current achievement status. The server stores this data in a database, which forms the basis for subsequent calculations. 【0590】 Step 2: 【0591】 The server uses the received goal setting and progress information to generate a learning plan, taking into account available resources. A Python program runs here, applying algorithms based on the collected data. The output is a personalized learning plan, which includes learning tasks and schedules tailored to each user. 【0592】 Step 3: 【0593】 The server retrieves past learning history information and uses a forgetting curve model to calculate the optimal review timing. Past learning data is used as input, and the script calculates the optimal review period based on this data. The server outputs the calculation result and notifies the user in the next step. The specific operation involves computational processing utilizing mathematical models. 【0594】 Step 4: 【0595】 The server sends the generated study plan and review timings to the device. The device receives this information and notifies the user using the appropriate tool. The input data is the plan and timing information from the server, and the device's output displays the study schedule and reminders on the user interface. Specifically, the device's user interface is designed to receive notifications. 【0596】 Step 5: 【0597】 The server uses emotion analysis technology to identify the user's emotional state in real time. Input information includes voice data and facial image data collected from the device. Based on this information, the server applies an emotion analysis algorithm to determine the user's emotional state. The output is evaluation information based on the user's emotions. 【0598】 Step 6: 【0599】 The server generates feedback for the user based on the analyzed emotional state. It uses emotional assessment information and learning progress information as input data. A generative AI model automatically generates appropriate feedback, and prompts such as "If the user is experiencing stress, generate and present specific advice to improve learning efficiency" are applied. The generated feedback is sent to the user's device as a notification. 【0600】 (Application Example 2) 【0601】 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." 【0602】 For learners to sustain their learning, not only is an optimized learning plan necessary, but support tailored to their individual emotional states is also required. However, conventional systems have struggled to adequately capture changes in users' emotions, making it difficult to provide immediate support. Dynamic learning support based on emotions is needed to improve learner motivation and enhance learning efficiency. 【0603】 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. 【0604】 In this invention, the server includes a generation means for receiving goal setting information and learning progress information and generating a user learning plan; a means for calculating review timing according to a forgetting curve model based on learning history information; a means for generating and notifying feedback based on the user's progress information and level of understanding; and a means for analyzing the emotional state using emotion recognition means and dynamically adjusting the learning plan and feedback accordingly. This enables efficient learning support that takes into account the user's emotional state. 【0605】 "Goal-setting information" refers to information related to the learning objectives and goals that the user wants to achieve. 【0606】 "Learning progress information" refers to information that shows the progress and results of a user's ongoing learning activities. 【0607】 A "generation means" is a device or system that has the function of creating a learning plan tailored to individual users based on goal setting information and learning progress information. 【0608】 "Learning history information" refers to information that records the learning history and past activities that a user has undertaken so far. 【0609】 A "forgetting curve model" is a model that mathematically represents the time it takes for a user to forget information they have learned, as well as the patterns involved. 【0610】 A "means for calculating review timing" refers to a device or system that uses learning history information and a forgetting curve model to calculate the optimal timing for review for the user. 【0611】 "Means for generating and notifying feedback" refers to devices or systems that provide learning evaluations and next steps based on the user's progress and understanding, and notify the user accordingly. 【0612】 "Emotion recognition means" refers to technologies and devices for identifying and analyzing emotions from a user's facial expressions and voice. 【0613】 "Means for analyzing emotional states and dynamically adjusting learning plans and feedback based on them" refers to devices and systems that evaluate a user's emotions in real time and quickly adapt and modify individual learning plans and feedback based on the results. 【0614】 To implement this invention, the learning support system is centered around a server, a terminal, and a user. The server manages goal-setting information and learning progress information obtained from the user and uses a generation means to create a personalized learning plan for the user. This plan takes into account the user's learning style and available time. The server also maintains learning history information, calculates the optimal review timing using a forgetting curve model, and notifies the user of this timing via the terminal. 【0615】 The emotion recognition feature is implemented in a device equipped with a camera to analyze the user's facial expressions and a microphone to analyze their voice. For facial recognition, for example, the Face++ API can be used, and for voice analysis, the Google Cloud Speech-to-Text API can be used. This emotion data is sent to a server in real time and helps generate feedback based on learning progress and comprehension. In particular, if the emotional state is negative, special learning tasks are suggested to increase motivation, and if it is positive, feedback is provided to further reinforce that state. 【0616】 For example, when elementary school students are studying at home using smart devices, an emotion recognition system evaluates the user's level of concentration and, if it determines that the user is temporarily feeling tired, displays advice such as, "Let's take a short break." This allows the user to refresh and resume studying efficiently. 【0617】 Examples of prompts used when utilizing generative AI models include: "If the user has a distracted expression, what words of encouragement should the robot offer?" or "If the user's voice contains signs of stress, what is an appropriate learning task to help restore the user's motivation?" 【0618】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0619】 Step 1: 【0620】 The server receives goal setting information and learning progress information from the user as input. Based on this information, the generation mechanism creates a learning plan optimized for each individual user and outputs the results to the database. 【0621】 Step 2: 【0622】 The device uses its camera and microphone to capture the user's facial expressions and voice as input. This data is then analyzed using facial recognition technology (e.g., Face++ API) and voice analysis technology (e.g., Google Cloud Speech-to-Text API) and output as emotion data. 【0623】 Step 3: 【0624】 The server analyzes emotional data and compares it with learning history information. It applies a forgetting curve model to calculate the optimal review timing and sends a review notification to the user. 【0625】 Step 4: 【0626】 The terminal displays the learning plan sent from the server to the user, providing daily learning support. As the user engages in learning through the terminal, progress information is updated and output to the server. 【0627】 Step 5: 【0628】 The server comprehensively analyzes the user's progress, understanding, and emotional state to generate appropriate feedback. This feedback is then sent to the device and notified to the user. 【0629】 Step 6: 【0630】 If a user is experiencing stress, the server analyzes the contributing factors and uses a generative AI model to generate recommendations to improve their motivation. These recommendations are then output to the terminal as prompts. A concrete example of such advice might be, "Take a short break." 【0631】 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. 【0632】 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. 【0633】 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. 【0634】 [Fourth Embodiment] 【0635】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0636】 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. 【0637】 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). 【0638】 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. 【0639】 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. 【0640】 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). 【0641】 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. 【0642】 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. 【0643】 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. 【0644】 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. 【0645】 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. 【0646】 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. 【0647】 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". 【0648】 This invention is a system in which a server, terminal, and user work together to efficiently personalize and support the user's learning experience. The main elements of the system include goal setting information, learning progress information, generation means, learning history information, forgetting curve model, review timing, feedback, notifications, learning style, learning tasks, and planning. 【0649】 The server receives goal setting information and learning progress information provided by the user and records it in a database. Based on this, the generation device creates a learning plan tailored to the user's characteristics. This plan is customized to support the user in achieving their goals and sets the priorities and schedules of learning tasks. The terminal displays this plan in the user interface, allowing the user to check their daily learning tasks. 【0650】 Furthermore, the server maintains learning history information and uses a forgetting curve model to calculate the optimal review timing. This review timing is designed to ensure that the learned content is retained in long-term memory, and the device notifies the user of the need for review. 【0651】 Furthermore, by collecting and analyzing progress information in real time, the system evaluates the user's learning status and generates feedback based on the user's understanding and progress. This feedback is not only an evaluation based on progress information, but also includes encouraging messages to stimulate the user's motivation to learn, and is notified to the user from their device. 【0652】 For example, if a user aims to pass a foreign language proficiency test, the server sets "Passing a Foreign Language Proficiency Test" as the user's goal and creates a learning plan that includes daily vocabulary study and grammar practice based on the user's available study time and style. At the same time, it uses a forgetting curve model to notify the user of review tasks at appropriate times, thereby ensuring retention of the learned material. If progress is good, it generates a message such as "Great job this week! You're making steady progress!" to support the user's learning. This creates an environment where users can continue learning efficiently and enthusiastically. 【0653】 The following describes the processing flow. 【0654】 Step 1: 【0655】 The server receives goal setting information and learning progress information from the user and records it in the database. The user inputs specific goals, current progress, and available time for learning via their device. 【0656】 Step 2: 【0657】 The server uses a generation mechanism to create a user-specific learning plan based on recorded goal setting information and progress information. This learning plan includes smaller tasks arranged in order of priority and includes an achievable schedule. 【0658】 Step 3: 【0659】 The device displays a generated learning plan to the user, allowing them to visually confirm their daily learning tasks. The user then begins learning according to the plan. 【0660】 Step 4: 【0661】 The server collects learning history information from the user and periodically applies a forgetting curve model to calculate when it's time for review. When it's time to review, the server sends a notification to the device. 【0662】 Step 5: 【0663】 The device displays a review notification to the user and lists the scheduled review tasks. The user then reviews the material based on the notification and reviews the learned content. 【0664】 Step 6: 【0665】 After a user completes a learning task, their progress is recorded on their device. The device then synchronizes this data with a server and saves it to a database as the latest progress. 【0666】 Step 7: 【0667】 The server generates feedback based on the user's new progress information, evaluating their understanding and learning progress. It also generates encouraging messages to help maintain user motivation. 【0668】 Step 8: 【0669】 The device notifies the user of any generated feedback or messages, allowing them to visually confirm them. Based on this, the user then proceeds to the next learning task. 【0670】 (Example 1) 【0671】 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". 【0672】 In today's learning environment, it is difficult to develop learning plans tailored to individual learners and to support efficient learning progress. In particular, there is a need for effective feedback that matches learners' diverse goals and progress, as well as appropriate review timing to encourage repeated learning. Therefore, creating an environment where learners can continue to learn with motivation is a crucial challenge. 【0673】 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. 【0674】 In this invention, the server includes an information processing device that receives goal setting information and learning progress information and generates a learning plan based on user characteristics; a processing device that calculates the optimal review timing according to a forgetting model based on learning history information; and an information processing device that generates an evaluation based on the user's progress and level of understanding and notifies the user through an output device. This enables the formulation of an optimal learning plan for achieving individual learner goals, as well as effective feedback and notification of review timing. 【0675】 "Goal setting information" refers to information about the specific learning goals that the user wishes to achieve. 【0676】 "Learning progress information" refers to information that shows how far a user has progressed towards their goal. 【0677】 "User characteristics" refer to individual features of each user, including their learning style, preferences, and time management abilities. 【0678】 "Learning history information" refers to records of learning activities a user has undertaken and the results thereof. 【0679】 A "forgetting model" is a mathematical model that describes how learned material is forgotten over time. 【0680】 The "review period" is the ideal time for learners to reconfirm what they have learned in the past. 【0681】 An "information processing device" is hardware or software used for collecting, analyzing, and processing data. 【0682】 An "output device" is hardware or software used to convey calculation results or information to the user. 【0683】 "Evaluation" is a judgment made based on the user's learning progress and level of understanding. 【0684】 "Notification" refers to the act of informing a user of information, or a function within a system that does so. 【0685】 This invention is an information processing system for effectively supporting user learning. The system primarily functions through the coordinated operation of three elements: a server, a terminal, and a user. 【0686】 The server receives goal setting information and learning progress information from the user. Specifically, it stores information entered by the user via their device in a database. This allows for the accumulation of data based on the user's learning status and goals. The server analyzes this information and uses a generative AI model to generate an optimal learning plan for the user. This learning plan includes personalized learning tasks and their implementation schedules. 【0687】 The device displays the learning plan sent from the server on the user interface. This user interface serves as a guide for the user to check their daily learning tasks and proceed with their studies accordingly. The device also receives and displays notifications from the server to the user. These include calculations of review timing based on forgetting models and feedback messages according to progress. 【0688】 A key feature of this system is that the server uses a forgetting model based on learning history information to calculate the optimal review timing. This review timing is then notified to the user via their device. This allows users to efficiently retain the learned material in their memory. 【0689】 As a concrete example, consider a user aiming to pass a foreign language proficiency test. The user sets "Passing a foreign language proficiency test" as their goal on their device, and the server generates a learning plan based on that goal, such as "Spending a certain amount of time each day studying vocabulary and grammar." Furthermore, the server uses a forgetting model to calculate specific review timings, such as "Review the words you learned in one week," and notifies the user. Depending on the progress, it is also possible to generate encouraging messages such as, "You're doing great! Keep it up!" 【0690】 An example of a prompt to input into a generative AI model would be: "Analyze the user's foreign language learning progress and suggest the next learning steps. For example, tell me the vocabulary and grammar points the user learned this week." This prompt allows the AI ​​model to provide appropriate advice and feedback. 【0691】 This invention provides a mechanism for efficiently supporting user learning using hardware and software. 【0692】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0693】 Step 1: 【0694】 Users use a terminal to input goal setting information and learning progress information. During this process, users meticulously record their learning goals (e.g., passing an exam) and current learning status, and send this information to the server via the terminal. The input information includes the user's specific learning style and available learning time. The output is the storage of the user information sent to the server into a database. 【0695】 Step 2: 【0696】 The server records the received goal setting information and learning progress information in a database. Based on this, the generative AI model is activated, analyzes the input information, and generates an optimal learning plan tailored to the user's characteristics. This data analysis takes into account the user's past learning history and trends. The output is a learning plan optimized for each individual user. 【0697】 Step 3: 【0698】 The terminal displays the learning plan sent from the server in the user interface. The user receives this information, checks the daily learning tasks, and performs them accordingly. The displayed plan includes specific learning content and its priority. The output is a learning task list that can be visually reviewed by the learner. 【0699】 Step 4: 【0700】 The server uses a forgetting model based on learning history information to calculate the optimal review timing. The server analyzes the input learning history data to determine the most appropriate review time for the user to retain what they have learned over a long period. The output is the calculated review timing. 【0701】 Step 5: 【0702】 The device receives notifications from the server regarding review timing and informs the user. Based on these notifications, the user can review at the appropriate time. Notifications are provided to the user as pop-ups or alert messages. The output is a new notification that can be viewed on the user interface. 【0703】 Step 6: 【0704】 The server monitors the user's progress in real time and uses a generative AI model to build feedback. Input data includes the user's learning speed and comprehension level. The server analyzes this data and generates appropriate feedback and encouragement messages. The output is the feedback message delivered to the user via their device. 【0705】 Step 7: 【0706】 The terminal notifies the user of feedback generated by the server, supporting increased learning motivation. The feedback is displayed in the user interface, providing the user with encouraging messages and suggestions for improvement. The output is a display of motivating feedback for the user. 【0707】 (Application Example 1) 【0708】 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". 【0709】 There is a need to efficiently provide personalized learning support tailored to each learner's progress and level of understanding, but conventional systems have the challenge of not being able to reflect learner responses and feedback in real time. Furthermore, there is a lack of interactive notification methods to maintain learner motivation, so a method is needed to enhance sustained learning enthusiasm. 【0710】 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. 【0711】 In this invention, the server includes a generation means for receiving goal setting information and learning progress information and generating a user's learning plan; a means for calculating review timing according to a forgetting curve model based on learning history information; a means for generating and notifying feedback based on the user's progress information and level of understanding; and a means for providing interactive notifications using the learner's voice and visual input. This enables the provision of information tailored to each learner and the maintenance of their motivation. 【0712】 "Goal-setting information" refers to information that specifies the learning goals that the user wants to achieve. 【0713】 "Learning progress information" refers to data that shows the user's current learning status. 【0714】 A "learning plan" is a set of specific learning schedules and tasks designed to help the user achieve their goals. 【0715】 "Generation method" refers to the process of creating an individualized learning plan based on the user's learning goals and progress. 【0716】 "Learning history information" refers to a record of the learning activities a user has undertaken in the past. 【0717】 The "forgetting curve model" is a theoretical model that shows how human memory is lost over time. 【0718】 "Review timing" refers to the optimal time to review what you have learned in order to effectively memorize it. 【0719】 "Feedback" refers to evaluations and comments that reflect the user's level of understanding and progress in their learning. 【0720】 "Interactive notifications" refer to notifications that dynamically convey information to learners using audio and visual means. 【0721】 This invention is a system for efficiently individualizing and supporting the user's learning experience. This system operates in cooperation with a server, terminals, and users. Each of the means provided in this form is described in detail below. 【0722】 The server receives goal-setting information and learning progress information from the user and stores it in a database (e.g., MySQL). Based on this data, the server uses a generation mechanism to create a learning plan that takes into account the user's learning characteristics. This plan includes the optimal tasks and their implementation schedule for achieving the user's goals. 【0723】 The device visually presents the generated learning plan to the user. For this purpose, the device is equipped with a display and provides a user interface (e.g., an Android or iOS application). Additionally, the device uses a microphone to recognize user voice input and a speaker for audio output. 【0724】 The server analyzes learning history information and calculates review timing based on a forgetting curve model. This allows users to receive review notifications at the optimal time to solidify learned content into long-term memory. An AI algorithm (e.g., SciKit-Learn in Python) is used to calculate this review timing. 【0725】 Furthermore, the server collects user progress information in real time and generates progress evaluations and feedback. Using a generative AI model, it creates and notifies users of feedback tailored to their level of understanding. This notification also includes encouraging messages to motivate learners. 【0726】 For example, if a learner is preparing for a language exam, the server can generate a message such as, "There are grammar questions to work on in the next session. Please review them aloud," and interactively notify the learner through their device. An example of a prompt to input into the generation AI model is, "Your progress yesterday was good. Please generate today's feedback message." 【0727】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0728】 Step 1: 【0729】 The user enters their learning goals and current progress into the terminal. The terminal sends this information to the server. The entered information includes weekly study time and the name of the target exam. The server receives this information and stores it in a database. 【0730】 Step 2: 【0731】 The server analyzes the received goal setting and progress information and uses a generation mechanism to create an individualized learning plan. In this process, a generative AI model is used to generate optimal learning tasks and schedules, taking into account the input learning goals. This result is then sent to the terminal. The output information includes a list of learning tasks and a schedule for their execution. 【0732】 Step 3: 【0733】 The device displays the learning plan sent from the server to the user. Learning tasks and schedules are displayed as visual output through the user interface. The user reviews this to help them begin their daily learning activities. 【0734】 Step 4: 【0735】 The server analyzes learning history information and calculates review timing using a forgetting curve model. The input is a record of past learning activities. Based on this data, an AI algorithm is used to output the optimal review timing for the user. This information will later be used for notifications. 【0736】 Step 5: 【0737】 The server collects user progress information in real time and evaluates the progress. To generate feedback tailored to the user's understanding, the progress data is input into an AI model, and feedback is output as a generated response. This feedback includes encouraging messages, among other things. 【0738】 Step 6: 【0739】 The device interactively communicates feedback and review notifications from the server to the user. It uses the speaker for audio notifications and also displays messages on the screen. Specific examples of notifications include "You have a review task for today" and "Let's focus on this in your next study session." 【0740】 Step 7: 【0741】 Users receive notifications from their devices and adjust their learning based on that feedback. For example, they might review new vocabulary or review questions they answered incorrectly. This process is important for incorporating progress information into the next plan. 【0742】 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. 【0743】 This invention is a system for supporting a user's learning activities, which combines an emotion engine to provide personalized learning support based on the user's emotional state. The system includes a server, a terminal, and a user, and comprises the following elements: goal setting information, learning progress information, generation means, learning history information, forgetting curve model, review timing, feedback, notifications, learning style, learning tasks, plan, and emotion engine. 【0744】 The server collects information entered by the user through the terminal and records goal setting information and learning progress information in a database. Based on this information, the generation device creates a learning plan tailored to the user. The learning plan includes individualized learning tasks and is optimized considering the user's learning style and available time. The terminal presents the generated learning plan to the user and supports their daily learning. 【0745】 Furthermore, the server maintains learning history information and calculates the optimal review timing using a forgetting curve model. Based on this timing, the device sends a review notification to the user. The user then performs regular reviews according to this notification to solidify the learned content into long-term memory. 【0746】 The emotion engine, a key feature of this invention, recognizes the user's emotional state in real time. This engine accurately captures the user's emotions by combining facial recognition technology, voice analysis technology, and other techniques. Emotional information is used to generate feedback and is analyzed on the server along with the user's progress. If the user exhibits positive emotions during learning, the feedback is enhanced accordingly. Conversely, if negative emotions are detected, encouragement and special learning support are provided to improve motivation. 【0747】 For example, if the server detects that a user is experiencing stress during learning, it uses an emotion engine to analyze the cause and generate advice such as, "Take a short break to relax." In this way, learning plans and feedback are dynamically adjusted according to the user's emotional state, resulting in optimal learning support. 【0748】 The following describes the processing flow. 【0749】 Step 1: 【0750】 Users input their learning goals and current progress via their device. Goals include specific passing targets and learning completion deadlines. 【0751】 Step 2: 【0752】 The device sends collected goal setting information and learning progress information to the server. The server records this information in a database. 【0753】 Step 3: 【0754】 The server uses a generation mechanism to create a user-specific learning plan. The plan includes learning tasks and a schedule based on priority. 【0755】 Step 4: 【0756】 The device receives planning information from the server and presents it visually to the user. This allows the user to check their learning tasks. 【0757】 Step 5: 【0758】 The server applies a forgetting curve model based on learning history information to calculate the timing for review. It then sends a notification to the device at the appropriate time. 【0759】 Step 6: 【0760】 The terminal displays a review notification from the server to the user and updates the review task list. The user then reviews the material accordingly. 【0761】 Step 7: 【0762】 The emotion engine analyzes the user's facial expressions and voice in real time to recognize the user's emotional state. 【0763】 Step 8: 【0764】 Based on information from the emotion engine, the server analyzes it along with the user's progress information and dynamically adjusts the learning plan and feedback. 【0765】 Step 9: 【0766】 The device displays feedback from the server and provides encouragement and advice to the user. For example, if stress is detected, it might advise, "Try taking a short break." 【0767】 Step 10: 【0768】 The user enters the results of completed learning tasks into the device. The device synchronizes this information with the server and updates the database as progress information. 【0769】 These processing steps optimize the user's learning experience individually, enabling dynamic feedback and learning support that takes emotional states into account. 【0770】 (Example 2) 【0771】 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". 【0772】 Modern learning support systems generally fail to adequately address the individual emotional states and learning styles that students face. In particular, the lack of dynamic learning support based on learners' emotional fluctuations and individual time allocations is a challenge that impairs learning efficiency and motivation. 【0773】 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. 【0774】 In this invention, the server includes a calculation means for receiving goal setting information and progress information and generating a user's learning plan; a calculation means for calculating review timing according to a forgetting pattern model based on past learning information; a communication means for generating and notifying feedback based on the user's progress information and level of understanding; and a means for identifying the user's emotional state using emotion analysis technology and dynamically adjusting learning support based on it. This enables optimal learning support tailored to the user's individual learning style and emotions. 【0775】 "Goal setting information" refers to information about the learning goals that users wish to achieve. 【0776】 "Progress information" refers to information about the user's level of achievement and the amount of learning they have done as they progress through their studies. 【0777】 "Computational means" refers to computing devices or algorithms used to perform specific data processing. 【0778】 A "forgetting pattern model" refers to a mathematical model used to predict the retention rate of learned information over time. 【0779】 "Review timing" refers to the appropriate period for reviewing material to prevent forgetting. 【0780】 "Communication means" refers to the configuration of hardware or software used to transmit information. 【0781】 "Emotional analysis technology" refers to technical methods used to identify a user's emotional state. 【0782】 A "learning plan" refers to a plan that systematically arranges the learning tasks and schedules necessary to achieve the goals set by the user. 【0783】 "Feedback" refers to the responses and advice provided to users based on their learning progress. 【0784】 This invention is an advanced system for supporting users' learning activities, and is particularly characterized by the generation of personalized learning plans and the provision of dynamic feedback based on emotional states. 【0785】 The system primarily consists of servers, terminals, and users. The servers function as the central hub for information processing, utilizing high-performance computing devices and cloud services. Specifically, they employ data management software commonly used as databases and execute algorithms using programming languages ​​such as Python. 【0786】 The server receives goal setting information and progress information entered by the user through their terminal, and generates a learning plan based on this information. This learning plan is optimized for the user's learning style and available time. 【0787】 The terminal is primarily a user interface device, displaying generated learning plans and feedback. This includes mobile devices and personal computers, allowing users to intuitively manage their learning. The terminal is equipped with software to display the user interface and can receive notifications from the server in real time. 【0788】 The server also uses a forgetting curve model to calculate the optimal timing for review. This allows it to provide users with efficient review notifications. Furthermore, it incorporates an emotion engine that combines facial recognition and voice analysis technologies to analyze the user's emotional state. This automatically generates appropriate feedback based on positive or negative emotions. 【0789】 For example, if a user is experiencing stress while learning, the server analyzes the situation and provides advice through the device, such as "Take a short break to relax." This kind of adaptive learning support allows learners to continue learning efficiently in a way that suits their own pace and style. 【0790】 An example of a prompt for a generative AI model is, "If the user is feeling stressed, generate and present specific advice to improve learning efficiency." By using this prompt, the system can dynamically generate feedback that is appropriate to the situation. 【0791】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0792】 Step 1: 【0793】 The server receives goal setting information and progress information entered by the user from the terminal. Specifically, the terminal sends data about the user's goals and progress to the server via the network. The input data includes the user's learning goals and current achievement status. The server stores this data in a database, which forms the basis for subsequent calculations. 【0794】 Step 2: 【0795】 The server uses the received goal setting and progress information to generate a learning plan, taking into account available resources. A Python program runs here, applying algorithms based on the collected data. The output is a personalized learning plan, which includes learning tasks and schedules tailored to each user. 【0796】 Step 3: 【0797】 The server retrieves past learning history information and uses a forgetting curve model to calculate the optimal review timing. Past learning data is used as input, and the script calculates the optimal review period based on this data. The server outputs the calculation result and notifies the user in the next step. The specific operation involves computational processing utilizing mathematical models. 【0798】 Step 4: 【0799】 The server sends the generated study plan and review timings to the device. The device receives this information and notifies the user using the appropriate tool. The input data is the plan and timing information from the server, and the device's output displays the study schedule and reminders on the user interface. Specifically, the device's user interface is designed to receive notifications. 【0800】 Step 5: 【0801】 The server uses emotion analysis technology to identify the user's emotional state in real time. Input information includes voice data and facial image data collected from the device. Based on this information, the server applies an emotion analysis algorithm to determine the user's emotional state. The output is evaluation information based on the user's emotions. 【0802】 Step 6: 【0803】 The server generates feedback for the user based on the analyzed emotional state. It uses emotional assessment information and learning progress information as input data. A generative AI model automatically generates appropriate feedback, and prompts such as "If the user is experiencing stress, generate and present specific advice to improve learning efficiency" are applied. The generated feedback is sent to the user's device as a notification. 【0804】 (Application Example 2) 【0805】 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". 【0806】 For learners to sustain their learning, not only is an optimized learning plan necessary, but support tailored to their individual emotional states is also required. However, conventional systems have struggled to adequately capture changes in users' emotions, making it difficult to provide immediate support. Dynamic learning support based on emotions is needed to improve learner motivation and enhance learning efficiency. 【0807】 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. 【0808】 In this invention, the server includes a generation means for receiving goal setting information and learning progress information and generating a user learning plan; a means for calculating review timing according to a forgetting curve model based on learning history information; a means for generating and notifying feedback based on the user's progress information and level of understanding; and a means for analyzing the emotional state using emotion recognition means and dynamically adjusting the learning plan and feedback accordingly. This enables efficient learning support that takes into account the user's emotional state. 【0809】 "Goal-setting information" refers to information related to the learning objectives and goals that the user wants to achieve. 【0810】 "Learning progress information" refers to information that shows the progress and results of a user's ongoing learning activities. 【0811】 A "generation means" is a device or system that has the function of creating a learning plan tailored to individual users based on goal setting information and learning progress information. 【0812】 "Learning history information" refers to information that records the learning history and past activities that a user has undertaken so far. 【0813】 A "forgetting curve model" is a model that mathematically represents the time it takes for a user to forget information they have learned, as well as the patterns involved. 【0814】 A "means for calculating review timing" refers to a device or system that uses learning history information and a forgetting curve model to calculate the optimal timing for review for the user. 【0815】 "Means for generating and notifying feedback" refers to devices or systems that provide learning evaluations and next steps based on the user's progress and understanding, and notify the user accordingly. 【0816】 "Emotion recognition means" refers to technologies and devices for identifying and analyzing emotions from a user's facial expressions and voice. 【0817】 "Means for analyzing emotional states and dynamically adjusting learning plans and feedback based on them" refers to devices and systems that evaluate a user's emotions in real time and quickly adapt and modify individual learning plans and feedback based on the results. 【0818】 To implement this invention, the learning support system is centered around a server, a terminal, and a user. The server manages goal-setting information and learning progress information obtained from the user and uses a generation means to create a personalized learning plan for the user. This plan takes into account the user's learning style and available time. The server also maintains learning history information, calculates the optimal review timing using a forgetting curve model, and notifies the user of this timing via the terminal. 【0819】 The emotion recognition feature is implemented in a device equipped with a camera to analyze the user's facial expressions and a microphone to analyze their voice. For facial recognition, for example, the Face++ API can be used, and for voice analysis, the Google Cloud Speech-to-Text API can be used. This emotion data is sent to a server in real time and helps generate feedback based on learning progress and comprehension. In particular, if the emotional state is negative, special learning tasks are suggested to increase motivation, and if it is positive, feedback is provided to further reinforce that state. 【0820】 For example, when elementary school students are studying at home using smart devices, an emotion recognition system evaluates the user's level of concentration and, if it determines that the user is temporarily feeling tired, displays advice such as, "Let's take a short break." This allows the user to refresh and resume studying efficiently. 【0821】 Examples of prompts used when utilizing generative AI models include: "If the user has a distracted expression, what words of encouragement should the robot offer?" or "If the user's voice contains signs of stress, what is an appropriate learning task to help restore the user's motivation?" 【0822】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0823】 Step 1: 【0824】 The server receives goal setting information and learning progress information from the user as input. Based on this information, the generation mechanism creates a learning plan optimized for each individual user and outputs the results to the database. 【0825】 Step 2: 【0826】 The device uses its camera and microphone to capture the user's facial expressions and voice as input. This data is then analyzed using facial recognition technology (e.g., Face++ API) and voice analysis technology (e.g., Google Cloud Speech-to-Text API) and output as emotion data. 【0827】 Step 3: 【0828】 The server analyzes emotional data and compares it with learning history information. It applies a forgetting curve model to calculate the optimal review timing and sends a review notification to the user. 【0829】 Step 4: 【0830】 The terminal displays the learning plan sent from the server to the user, providing daily learning support. As the user engages in learning through the terminal, progress information is updated and output to the server. 【0831】 Step 5: 【0832】 The server comprehensively analyzes the user's progress, understanding, and emotional state to generate appropriate feedback. This feedback is then sent to the device and notified to the user. 【0833】 Step 6: 【0834】 If a user is experiencing stress, the server analyzes the contributing factors and uses a generative AI model to generate recommendations to improve their motivation. These recommendations are then output to the terminal as prompts. A concrete example of such advice might be, "Take a short break." 【0835】 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. 【0836】 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. 【0837】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0838】 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. 【0839】 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. 【0840】 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. 【0841】 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. 【0842】 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. 【0843】 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." 【0844】 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. 【0845】 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. 【0846】 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. 【0847】 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. 【0848】 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. 【0849】 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. 【0850】 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. 【0851】 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. 【0852】 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. 【0853】 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. 【0854】 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. 【0855】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference. 【0856】 The following is further disclosed regarding the embodiments described above. 【0857】 (Claim 1) 【0858】 A generation means that receives goal setting information and learning progress information and generates a user's learning plan, 【0859】 A means for calculating review timing according to a forgetting curve model based on learning history information, 【0860】 A means of generating and notifying feedback based on the user's progress and understanding, 【0861】 A system that includes this. 【0862】 (Claim 2) 【0863】 The system according to claim 1, which provides a means for presenting personalized learning tasks, taking into account the user's learning style information. 【0864】 (Claim 3) 【0865】 The system according to claim 1, providing means for notifying the user of the generated learning plan and review schedule. 【0866】 "Example 1" 【0867】 (Claim 1) 【0868】 An information processing device that receives goal setting information and learning progress information and generates a learning plan based on user characteristics, 【0869】 A processing device that calculates the optimal review period according to a forgetting model based on learning history information, 【0870】 Information processing device means that generates an evaluation based on the user's progress and level of understanding, and notifies the evaluation through an output device, 【0871】 A process that analyzes progress data in real time and uses a generative AI model to provide messages that stimulate learning motivation, 【0872】 A system that includes this. 【0873】 (Claim 2) 【0874】 The system according to claim 1, a means for configuring personalized learning tasks, taking into account the user's learning methods and information. 【0875】 (Claim 3) 【0876】 The system according to claim 1, comprising means for transmitting generated learning plans and review schedules to the user via an output device. 【0877】 "Application Example 1" 【0878】 (Claim 1) 【0879】 A generation means that receives goal setting information and learning progress information and generates a user's learning plan, 【0880】 A means for calculating review timing according to a forgetting curve model based on learning history information, 【0881】 A means of generating and notifying feedback based on the user's progress and understanding, 【0882】 A means of providing interactive notifications using the learner's voice and vision, 【0883】 A system that includes this. 【0884】 (Claim 2) 【0885】 The system according to claim 1, which provides a means for presenting personalized learning tasks, taking into account the user's learning style information. 【0886】 (Claim 3) 【0887】 The system according to claim 1, providing means for notifying the user of the generated learning plan and review schedule. 【0888】 "Example 2 of combining an emotion engine" 【0889】 (Claim 1) 【0890】 A computation means that receives goal setting information and progress information and generates a learning plan for the user, 【0891】 A calculation means that calculates the timing of review according to a forgetting pattern model based on past learning information, 【0892】 A communication method that generates and notifies feedback based on the user's progress and level of understanding, 【0893】 A means for identifying the user's emotional state using emotion analysis technology and dynamically adjusting learning support based on that, 【0894】 A system that includes this. 【0895】 (Claim 2) 【0896】 The system according to claim 1, which provides a means for presenting individualized learning tasks, taking into account the user's learning style information and time allocation. 【0897】 (Claim 3) 【0898】 The system according to claim 1, comprising means for notifying the user of the generated learning plan and review schedule via communication means. 【0899】 "Application example 2 when combining with an emotional engine" 【0900】 (Claim 1) 【0901】 A generation means that receives goal setting information and learning progress information and generates a user's learning plan, 【0902】 A means for calculating review timing according to a forgetting curve model based on learning history information, 【0903】 A means of generating and notifying feedback based on the user's progress and understanding, 【0904】 A means for analyzing emotional states using emotion recognition means and dynamically adjusting learning plans and feedback based on that analysis, 【0905】 A system that includes this. 【0906】 (Claim 2) 【0907】 The system according to claim 1, which provides a means for presenting personalized learning tasks, taking into account the user's learning style information. 【0908】 (Claim 3) 【0909】 The system according to claim 1, providing means for notifying the user of the generated learning plan and review schedule. [Explanation of symbols] 【0910】 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

[Claim 1] A generation means that receives goal setting information and learning progress information and generates a user's learning plan, A means for calculating review timing according to a forgetting curve model based on learning history information, A means of generating and notifying feedback based on the user's progress and understanding, A system that includes this. [Claim 2] The system according to claim 1, which provides a means for presenting personalized learning tasks, taking into account the user's learning style information. [Claim 3] The system according to claim 1, which provides means for notifying the user of the generated learning plan and review schedule.

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