System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting a learner's learning history, interests and strengths; means for analyzing the collected learning history, interests and strengths to identify a learning pattern; means for generating a personalized learning plan based on the identified learning pattern; means for providing appropriate materials based on the generated learning plan; means for tracking the learner's progress in real time; and means for adjusting the learning plan and materials based on the tracked progress.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In traditional education systems, the same educational content is provided to all learners, making it difficult to adapt to the characteristics and needs of individual learners. This leads to reduced learning effectiveness and satisfaction, and some learners lose interest. Furthermore, it is a burden for educational institutions and private tutors to create individual learning plans and track and adjust progress. This project aims to solve these problems. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means: means for collecting a learner's learning history, interests, and strengths, and means for analyzing the collected learning history, interests, and strengths to identify a learning pattern; means for generating a personalized learning plan based on the identified learning pattern and providing appropriate learning materials based on the plan; and means for tracking the learner's progress in real time and adjusting the learning plan and learning materials based on the progress. This makes it possible to provide an optimized learning experience for each individual learner and improve educational effectiveness.

[0006] "Learning history" is a record of a learner's past learning activities, and is data including test scores, study time, learning materials used, and so on.

[0007] "Interest" refers to the interest or motivation a learner shows in a particular subject or topic, and serves as a criterion for determining the learning materials or themes that the learner prefers.

[0008] "Strengths" refer to the abilities or characteristics that make a learner superior in a particular subject or skill.

[0009] "Collect" means the process of gathering the necessary data from various sources, including manual entry and automated data capture.

[0010] "Analysis" refers to the process of evaluating and analyzing collected data numerically or qualitatively to derive useful information and patterns from it.

[0011] A "learning pattern" refers to a regular movement or tendency extracted from a series of characteristics such as a learner's learning characteristics, tendencies, behavior, and progress.

[0012] "Personalization" means providing learning plans and materials that are optimized to suit each individual learner's attributes, needs, and interests.

[0013] A "learning plan" is a systematic plan that includes specific learning content and a schedule for achieving a learner's goals.

[0014] "Instructional materials" is a general term for educational resources and tools used to deepen learners' understanding and increase their knowledge.

[0015] "Real-time tracking" refers to the process of instantly recording and analyzing learner activity and progress.

[0016] "Adjust" means changing and updating the lesson plan and materials in response to learners' progress and changing needs. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a 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 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0035] The storage 32 stores a data generation model 58 and an 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 process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The present invention is a system for providing a personalized learning experience according to the characteristics of each learner. The system includes a server, a terminal, and a learner (user).

[0039] Server Operation

[0040] The server performs the following sequence of actions:

[0041] 1. Data Collection: The server collects data about learners' learning history, interests, and strengths from multiple databases and online learning platforms, including the learning materials they have viewed, test results, and study time.

[0042] 2. Data analysis: Based on the collected data, the server uses machine learning algorithms to preprocess the data and analyze the learner's learning patterns, for example, identifying which subjects the learner has strengths, interests, or weaknesses in.

[0043] 3. Learning plan generation: Based on the analysis results, the server automatically generates an optimized learning plan for each learner, including a curriculum and learning schedule to further develop the learner's strengths and strengthen their weaknesses.

[0044] 4. Providing learning materials: Based on the generated learning plan, the server selects appropriate learning materials and sends links to the learner's device, such as online lectures, exercises, and simulation materials for a specific unit.

[0045] 5. Progress Tracking: As learners progress through their studies, their devices transmit real-time learning progress data to the server, including study time, correct answer rate, and learning material browsing history.

[0046] 6. Content Adjustment: The server analyzes the received progress data, evaluates the learner's progress and learning effectiveness, and automatically adjusts the learning plan and materials as needed. For example, if a learner is struggling with English, the server may provide additional, more basic learning materials.

[0047] Device behavior

[0048] The terminal works as follows:

[0049] 1. Displaying learning materials: Receives the link to the learning materials sent from the server and displays it so that learners can access it. For example, the learning materials are provided through a web browser or a dedicated learning application.

[0050] 2. Learning data collection: Learners' learning activities are tracked in real time and the data is sent to the server. This data includes information such as which learning materials the learner studied, how long they spent studying them, and the percentage of questions they answered correctly.

[0051] Specific examples

[0052] As a concrete example, let's take the case of Learner A. Learner A is good at math and has an interest in science, but feels that he or she is not good at English. The server collects data on Learner A and identifies these characteristics. As a result, the learning plan provides an emphasis on providing math and science materials, and also includes additional materials to strengthen the learner's English basics. Furthermore, as the learner progresses, the English materials are adjusted to gradually increase in difficulty, starting with more basic materials. In this way, the system can provide a personalized learning experience that is tailored to the learner's characteristics, maximizing learning effectiveness.

[0053] The processing flow will be explained below.

[0054] Step 1:

[0055] The server collects the learner's learning history, interests, and strengths. Specifically, it accesses the online learning platform and the school's academic performance database and retrieves data such as the learner's past learning activities, test results, and learning materials used based on the learner's login information.

[0056] Step 2:

[0057] The server preprocesses the collected data, cleansing it, filling in missing data, and integrating learner profiles, such as standardizing the format of test results and organizing learner interests into categories.

[0058] Step 3:

[0059] The server analyzes the data using machine learning algorithms, specifically cluster analysis and pattern recognition, to identify learners' learning patterns, strengths (e.g., math), interests (e.g., science), and weaknesses (e.g., English), thereby identifying the characteristics and trends of individual learners.

[0060] Step 4:

[0061] The server then generates a personalized learning plan based on the analysis, designing a specific curriculum and study schedule to strengthen the learner's strengths and address their weaknesses, for example, providing additional applied math problems each week and setting up special sessions to strengthen the learner's English fundamentals.

[0062] Step 5:

[0063] The server selects appropriate learning materials based on the generated learning plan and sends them to the learner's device. Specifically, it selects online learning materials, practice questions, simulation tools, etc. and generates links to them.

[0064] Step 6:

[0065] The terminal receives the learning material link sent from the server and displays it to the learner, who then uses it to begin the instructed learning activity, for example, by viewing the learning material through a dedicated learning application or a web browser.

[0066] Step 7:

[0067] Users (learners) use their devices to carry out learning activities according to the provided learning materials, such as watching online lectures, answering exercises, and recording their results.

[0068] Step 8:

[0069] The device tracks the learner's progress in real time and sends it to the server, specifically recording the study time, answer results, and learning material browsing history, and uploading it immediately.

[0070] Step 9:

[0071] The server analyzes the received progress data and evaluates the learner's progress and learning effectiveness, including learning speed, correct answer rate, and duration of learning.

[0072] Step 10:

[0073] The server adjusts the learning plan and materials based on the progress data. For example, if a learner is struggling with English material, it can add easier questions or provide supplementary materials, helping the learner reach their learning goals more effectively.

[0074] Example 1

[0075] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0076] There is a demand for providing a personalized learning experience tailored to each learner's characteristics in order to maximize learning effectiveness. However, conventional systems only collect data on learning history, interests, and strengths, but are unable to effectively utilize this information in learning plans and providing learning materials. Furthermore, there is a lack of means to track learners' progress in real time and adjust learning plans and materials as needed. As a result, it is difficult to provide optimal learning plans tailored to each learner's individual needs.

[0077] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0078] In this invention, the server includes means for collecting the learning history, interests, and strengths of a learner, means for analyzing the collected learning history, interests, and strengths to identify a learning pattern, means for generating a personalized learning plan based on the identified learning pattern, means for providing appropriate learning materials based on the generated learning plan, means for tracking the learner's progress in real time, means for adjusting the learning plan and learning materials based on the tracked progress, and means for evaluating each learner's progress and automatically redesigning and providing the learning materials and learning plan as necessary. This makes it possible to provide an optimal learning plan according to the individual needs of the learner and maximize the learning effect.

[0079] A "learning history" is a record of all learning activities that a learner has performed in the past, including information such as the learning materials viewed, study time, and test results.

[0080] "Interest" refers to the interest or curiosity a learner has in a particular field of study or subject.

[0081] "Strengths" refer to the outstanding abilities and achievements a learner has in a particular field or subject.

[0082] "Data collection means" refers to the functions and methods for collecting data from learners, such as their learning history, interests, and strengths.

[0083] "Data analysis means" refers to functions and methods for analyzing learners' learning patterns based on collected data.

[0084] "Learning patterns" refer to the characteristics and tendencies that indicate the type of learning behavior a learner will exhibit.

[0085] A "learning plan" refers to an optimal curriculum and schedule designed based on the learner's characteristics.

[0086] "Means for providing learning materials" refers to the functions and methods for delivering appropriate learning materials to learners based on their learning plans.

[0087] "Progress tracking means" refers to functions and methods for tracking the progress of learners' learning activities in real time.

[0088] "Progress" refers to information that indicates whether a learner is progressing according to their learning plan.

[0089] "Content adjustment means" refers to functions and methods for redesigning and adjusting learning plans and materials based on progress.

[0090] A "machine learning algorithm" refers to a computational method for automatically extracting and analyzing learning patterns from data.

[0091] "Evaluation tools" refer to functions and methods for analyzing learners' progress and judging the effectiveness of the learning content based on the results.

[0092] This invention is a system that provides a personalized learning experience tailored to the individual characteristics of each learner. This system includes a server, a terminal, and a learner (user). Specific embodiments of each element are described below.

[0093] Server Operation

[0094] The server performs the following series of operations to collect and analyze the learner's data and generate an individualized learning plan.

[0095] 1. Data Collection

[0096] The server collects data on the learner's learning history, interests, and strengths from multiple databases and online learning platforms, specifically by retrieving data from online learning platforms via APIs (e.g., online learning platform APIs) and querying internal databases.

[0097] 2. Data Analysis

[0098] Based on the collected data, the server uses machine learning algorithms to preprocess and analyze the data and identify the learner's learning patterns. Techniques such as data cleaning, feature engineering, and clustering are used. Examples of machine learning algorithms used include clustering and classification algorithms.

[0099] 3. Learning plan generation

[0100] Based on the analysis results, the server automatically generates an optimal learning plan for each learner. The learning plan includes learning progress, curriculum, and schedule. A rule-based engine and scheduler are used for automatic generation.

[0101] 4. Providing teaching materials

[0102] The server selects appropriate learning material links based on the generated learning plan and sends them to the terminal.A content management system is used to provide links to online lectures, exercises, simulation materials, etc.

[0103] 5. Progress Tracking

[0104] As the learner progresses with their studies, progress data is received in real time from the device, including study time, correct answer rate, and learning material viewing history. The progress data is automatically saved in a database and used for analysis.

[0105] 6. Content Adjustment

[0106] The server analyzes the received progress data and automatically redesigns and adjusts the learning plan and materials as needed, using machine learning models to adapt content to reinforce the learner's weaknesses.

[0107] Device behavior

[0108] The terminal is a device that receives the learning material link sent from the server and allows the user to carry out learning activities.

[0109] 1. Teaching material display

[0110] The device receives the link to the learning material sent from the server and displays it to the user in a browser or dedicated application. For example, software such as a web browser or a mobile application is used.

[0111] 2. Learning data collection

[0112] The device tracks learners' learning activity data in real time and sends it to a server. This data includes study time, correct answer rate, and learning material viewing history. A learning management system (LMS) or tracking software is used.

[0113] Specific examples

[0114] As a concrete example, consider the case of Learner A. Learner A is good at math and has an interest in science, but feels that he is not good at English. The server collects data on Learner A and identifies these characteristics. As a result, the learning plan provides him with a focus on math and science materials, and also includes additional materials to strengthen his English foundations. Furthermore, as he progresses in his studies, the English materials are adjusted to gradually increase in difficulty, starting with more basic ones.

[0115] This allows the system to provide a personalized learning experience tailored to each learner's characteristics, maximizing learning effectiveness.

[0116] Example prompts for generative AI models

[0117] Please provide a detailed description of how your program automatically generates a personalized learning plan based on data from a specific learner. Also, please specify how the program adjusts content based on the learner's progress, and what hardware and software are required. Please provide an example of how your program works for a specific learner.

[0118] This prompt sentence serves as a guideline for the generative AI model to understand the specific processing content of the system and generate it accurately.

[0119] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0120] System program processing flow

[0121] Step 1: Data collection

[0122] The server collects data on learners' learning history, interests, and strengths from multiple databases and online learning platforms. Input data is obtained from the learning platform API and internal databases. This data includes the learning materials viewed by the learner, test results, study time, etc. The server obtains this data and stores it in an initial database. The output is the collected learning data. Specifically, for example, learner A's test scores are obtained from the online learning platform.

[0123] Step 2: Data Preprocessing

[0124] The server performs data preprocessing based on the collected data. The input is the raw data collected in step 1, and the output is the clean data after preprocessing. Data preprocessing includes tasks such as imputing missing values, removing outliers, and standardizing. For example, the missing learning time data for learner A is imputed with the average value.

[0125] Step 3: Data analysis

[0126] The server analyzes the preprocessed data using a machine learning algorithm. The input is the preprocessed data, and the output is the analysis results that show the learner's learning patterns. Clustering and classification algorithms are used for the analysis. Specifically, from the data of Learner A, it is determined that he is strong in mathematics but weak in English.

[0127] Step 4: Generate a lesson plan

[0128] Based on the analysis results, the server generates an optimized learning plan for each learner. The input is the analysis results, and the output is the generated learning plan. The learning plan includes learning progress, curriculum, and schedule. For example, for Learner A, a plan is generated that strengthens math and science materials while also strengthening the basics of English.

[0129] Step 5: Provide teaching materials

[0130] The server selects appropriate learning material links based on the generated learning plan and sends them to the terminal. The input is the learning plan, and the output is the learning material links. Specifically, it provides links to online lectures and practice questions to Learner A.

[0131] Step 6: Implementing the learning activity

[0132] Users access learning materials provided by the server through their terminals and perform learning activities. The input is the link to the learning materials provided by the server, and the output is learning progress data. Learning activities include viewing learning materials, taking tests, and answering practice problems. For example, Learner A answers the provided math practice problems.

[0133] Step 7: Track your progress

[0134] The device collects learner activity data in real time and sends it to the server. The input is the learner's activity data, and the output is progress data. Specifically, data such as the study time and correct answer rate of Learner A is collected.

[0135] Step 8: Analyze progress and adjust content

[0136] The server analyzes the received progress data and redesigns and adjusts the learning plan and materials as necessary. The input is progress data, and the output is adjusted learning plans and materials. Analysis includes evaluating learning effectiveness and adding content to strengthen weak points. For example, if Learner A is struggling with English, additional materials at the basic level will be provided.

[0137] Through this series of processes, the system can provide a personalized learning experience tailored to the individual needs of each learner, maximizing learning effectiveness.

[0138] (Application example 1)

[0139] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0140] Conventional learning support systems have proven effective in providing personalized learning experiences based on the individual characteristics of each learner. However, when it comes to shopping experiences in brick-and-mortar stores, there is a need for personalization based on individual customers' purchasing histories and interests, and the technology to achieve this in real time has not yet been fully established. Therefore, there is a need for a system that can instantly grasp customers' movements and interests within a store and provide them with product information tailored to their needs.

[0141] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0142] In this invention, the server includes means for collecting a learner's learning history, interests, and strengths, means for analyzing the collected learning history, interests, and strengths to identify a learning pattern, means for generating a personalized learning plan based on the identified learning pattern, means for providing appropriate learning materials based on the generated learning plan, means for tracking the learner's progress in real time, means for adjusting the learning plan and learning materials based on the tracked progress, means for collecting a purchase history, interests, and preferences, means for analyzing the collected purchase history, interests, and preferences to identify a purchasing pattern, means for generating a personalized product recommendation list based on the identified purchasing pattern, means for providing appropriate product information based on the generated product recommendation list, means for tracking a customer's movement within a store in real time, and means for adjusting the product information based on the tracked movement status. This makes it possible to provide optimal learning materials and product information according to individual characteristics and interests in both learning and shopping experiences.

[0143] - "Learning history" refers to the overall record of a learner's past learning activities, including the materials viewed, test results, study time, etc.

[0144] "Interest" refers to the degree of interest or attention a learner has in a particular field or topic.

[0145] "Strengths" refer to areas or subjects in which a learner excels, including specific skills and areas of knowledge.

[0146] "Analysis" is the process of identifying specific patterns or characteristics from collected data.

[0147] "Personalization" refers to providing experiences and content that are optimized to the characteristics and preferences of individual learners or customers.

[0148] A "learning plan" refers to a plan for learning progress based on the characteristics and needs of the learner, and includes a curriculum and learning schedule.

[0149] "Instructional materials" refers to resources such as textbooks, online lectures, and exercises that learners use to advance their learning.

[0150] "Progress" refers to the current state of a learner's learning, including the amount of time spent studying and the degree of completion of tasks.

[0151] "Purchase history" refers to a record of a customer's past purchasing activities, including the products purchased, the time of purchase, and the frequency of purchases.

[0152] "Preferences" refer to the preferences that customers have for specific products or categories.

[0153] A "product recommendation list" is a list of products suggested to a customer based on analyzed purchasing patterns.

[0154] "Product Information" refers to detailed information about a product, including price, features, and reviews.

[0155] "In-store movement" refers to the movement of customers as they move around the store, including browsing and purchasing products.

[0156] "Adjustment" refers to updating plans and content in accordance with progress and circumstances, and keeping them in the optimal state.

[0157] This invention is a system that provides a personalized experience according to the individual characteristics and interests of learners and customers. It utilizes servers and terminals to optimize learning support for learners and the shopping experience in physical stores.

[0158] Server Operation and Configuration

[0159] The server includes the following means:

[0160] 1. Data collection method: The server collects learners' or customers' past behavioral data (learning history, purchasing history, interests, and strengths) from multiple databases and online platforms. Learning history includes test results and study time, and purchasing history includes purchase dates and times and purchased products.

[0161] 2. Data analysis method: The collected data is preprocessed and analyzed using machine learning algorithms. During the analysis process, programming languages ​​such as Python and libraries such as scikit-learn and Pandas are used to identify and identify users' learning and purchasing patterns.

[0162] 3. Personalized plan generation: Based on the analysis results, a personalized learning plan or product recommendation list is generated for each user. The learning plan includes a curriculum and a learning schedule.

[0163] 4. Providing appropriate educational materials and product information: Based on the generated plan, the most suitable educational materials and product information are provided to the user, including educational material lists and product offers.

[0164] 5. Progress and movement tracking: Learners' learning progress and customers' movements within the store are tracked in real time. IoT devices and GPS tracking are used for real-time tracking.

[0165] 6. Plan and information adjustment: Automatically adjust learning plans and content offerings based on tracking data, including adding more basic learning materials and updating product information to better suit customer preferences.

[0166] Terminal operation and configuration

[0167] The terminal works as follows:

[0168] 1. Data display means: Links to educational materials and product information provided by the server are displayed to the user. Specifically, this is done through a smartphone application or the display of smart glasses.

[0169] 2. Data collection method: Collects user operation and behavior data and sends it to the server in real time.

[0170] Specific examples

[0171] Specific examples of learning

[0172] Learner A is good at math but weak at English. The server collects data on this learner and analyzes their characteristics. Based on the analysis results, the server generates a plan to provide advanced math learning materials and basic English learning materials. As Learner A progresses in his / her studies, the server tracks his / her progress in real time and adjusts the learning materials accordingly.

[0173] Shopping examples

[0174] Customer B has spent a lot of money on electronic devices in the past. The server analyzes this customer's purchasing history and generates a list of products suitable for Customer B. As Customer B moves through the store, the server tracks his movements in real time and provides specific product information in the relevant product section.

[0175] Prompt Sentence Examples

[0176] Learner prompt:

[0177] "Please provide me with additional basic learning materials to enhance my English studies."

[0178] Shopping customer prompt:

[0179] "Can you recommend the latest electronic gadgets based on your recent purchasing history?"

[0180] The system allows learners and customers to enjoy an experience that is tailored to their individual characteristics and interests.

[0181] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0182] Step 1:

[0183] Data collection

[0184] The server collects learner or customer data from existing databases and online platforms.

[0185] Input: Learning history, interests, strengths, purchasing history, preference data.

[0186] Output: The collected dataset.

[0187] Specific operation: The server retrieves user data from the database using an SQL query and saves it as a data frame.

[0188] Step 2:

[0189] Data Preprocessing

[0190] The server preprocesses the collected data and formats it into a form suitable for analysis.

[0191] Input: The collected dataset.

[0192] Output: The preprocessed dataset.

[0193] Specific operation: The server performs preprocessing such as imputing missing values, scaling, and encoding categorical data.

[0194] Step 3:

[0195] Data analysis

[0196] The server uses the pre-processed data and analyzes it with machine learning algorithms to identify user patterns.

[0197] Input: The preprocessed dataset.

[0198] Output: Learning patterns and purchasing patterns.

[0199] Specific operation: The server applies PCA (principal component analysis) and clustering algorithms (such as KMeans) to extract specific patterns.

[0200] Step 4:

[0201] Personalized Plan Generation

[0202] Based on the analysis results, the server generates a personalized study plan or product recommendation list for each user.

[0203] Input: Learning patterns and purchasing patterns.

[0204] Output: A personalized study plan or product list.

[0205] How it works: The server uses a specific algorithm to generate a plan or list optimized for each user, and then registers it in the user profile.

[0206] Step 5:

[0207] Providing appropriate educational materials and product information

[0208] The server provides the user with appropriate educational materials and product information based on the generated plan.

[0209] Input: A personalized study plan or product list.

[0210] Output: Teaching material links, product information.

[0211] Specific operation: The server generates links and information and sends them to the user's device.

[0212] Step 6:

[0213] Progress and movement tracking

[0214] The terminal tracks the user's learning and shopping progress in real time and transmits the data to a server.

[0215] Input: Learning progress data, in-store movement data.

[0216] Output: Real-time tracking data.

[0217] Specific operation: The device collects data using sensors (such as eye tracking in smart glasses or GPS in a smartphone) and sends it to a server.

[0218] Step 7:

[0219] Planning and Information Coordination

[0220] The server adjusts the lesson plan and product information based on the tracking data.

[0221] Input: Real-time tracking data.

[0222] Output: Tailored lesson plan, product list.

[0223] Specific operation: The server analyzes the collected data, regenerates appropriate content and schedules, and provides them to the user.

[0224] As described above, this system provides an optimized experience based on the characteristics of learners and customers. However, it is essential that the server performs appropriate processing at each step and updates and provides information in real time in cooperation with the device.

[0225] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0226] This invention is a system for providing a personalized learning experience according to the characteristics of each learner, and also combines it with an emotion engine that recognizes the user's emotions. This system is composed of a server, a terminal, a learner (user), and an emotion engine.

[0227] Server Operation

[0228] The server performs the following sequence of actions:

[0229] 1. Data Collection: The server collects data about learners' learning history, interests, and strengths from multiple databases and online learning platforms, including the learning materials they have viewed, test results, and study time.

[0230] 2. Emotion data collection: The server uses an emotion engine to collect the learner's emotional data, which is obtained from facial expressions, voice tones, pulse rates, etc.

[0231] 3. Data Analysis: Based on the collected learning data and emotional data, the server uses machine learning algorithms to preprocess the data and analyze the learner's learning patterns. For example, it identifies which subjects the learner is strong in, which subjects they are interested in, and which subjects they struggle with. It also evaluates the learner's motivation and stress level during learning based on emotional data.

[0232] 4. Learning plan generation: Based on the analysis results, the server automatically generates an optimized learning plan for each learner. This plan includes a specific curriculum and learning schedule designed to strengthen the learner's strengths and address their weaknesses. Based on emotional data, the server also suggests learning plans for times when students are least stressed.

[0233] 5. Providing learning materials: Based on the generated learning plan, the server selects appropriate learning materials and sends links to the learner's device, such as online lectures, exercises, and simulation materials for a specific unit.

[0234] 6. Progress Tracking: As learners progress through their studies, their devices transmit real-time learning progress data to the server, including study time, correct answer rate, and learning material browsing history.

[0235] 7. Emotional Data Collection: Collect emotional data in real time using an emotion engine, along with the learner's progress, for example, by analyzing facial expressions and vocal tones during learning.

[0236] 8. Content Adjustment: The server analyzes the received progress and emotion data to evaluate the learner's progress and learning effectiveness. If necessary, it automatically adjusts the learning plan and materials. For example, if the learner is tired or stressed, it provides more relaxing materials and methods.

[0237] Device behavior

[0238] The terminal works as follows:

[0239] 1. Displaying learning materials: Receives the link to the learning materials sent from the server and displays it so that learners can access it. For example, the learning materials are provided through a web browser or a dedicated learning application.

[0240] 2. Learning data collection: Learners' learning activities are tracked in real time and the data is sent to the server. This data includes information such as which learning materials the learner studied, how long they spent studying them, and the percentage of questions they answered correctly.

[0241] 3. Emotion data transmission: The emotion engine transmits the collected emotion data to the server in real time.

[0242] Specific examples

[0243] As a concrete example, consider the case of Learner A. Learner A is good at math and has an interest in science, but feels that he or she is not good at English. The server collects data on Learner A and identifies these characteristics. As a result, the learning plan provides Learner A with a focus on math and science materials, and also includes additional materials to strengthen his or her English foundation. Furthermore, if the emotion engine detects stress while Learner A is studying, it immediately adjusts the learning plan and inserts short breaks and relaxation materials to help him or her relax. In this way, the system can provide a personalized learning experience that is tailored to the learner's characteristics and emotions, maximizing learning effectiveness.

[0244] The processing flow will be explained below.

[0245] Step 1:

[0246] The server collects the learner's learning history, interests, and strengths. Specifically, it accesses the online learning platform and the school's academic performance database and retrieves data such as the learner's past learning activities, test results, and learning materials used based on the learner's login information.

[0247] Step 2:

[0248] The server collects emotional data using an emotion engine. Specifically, it monitors the learner's facial expressions, voice tone, pulse rate, etc., and records them as emotional data. For example, a learner may use a webcam to analyze changes in their facial expressions in real time.

[0249] Step 3:

[0250] The server preprocesses the collected learning and sentiment data, cleansing the data, filling in missing data, and integrating learner profiles, such as standardizing the format of test results and organizing learner interests into categories.

[0251] Step 4:

[0252] The server analyzes the data using machine learning algorithms, such as cluster analysis and pattern recognition, to identify learners' learning patterns, strengths (e.g., math), interests (e.g., science), and weaknesses (e.g., English), and also assesses their stress levels and motivation during learning based on emotional data.

[0253] Step 5:

[0254] The server then generates a personalized learning plan based on the analysis results, designing a specific curriculum and study schedule, such as providing high-intensity learning at times when stress is low and lighter learning materials when relaxation is needed.

[0255] Step 6:

[0256] The server selects appropriate learning materials based on the generated learning plan and sends them to the learner's device. For example, it selects online lectures, exercises, simulation materials, etc. for a specific unit and generates links to them.

[0257] Step 7:

[0258] The terminal receives the learning material link sent from the server and displays it to the learner, who then uses it to begin the instructed learning activity, for example, by viewing the learning material through a dedicated learning application or a web browser.

[0259] Step 8:

[0260] Users (learners) use their devices to carry out learning activities according to the provided learning materials, such as watching online lectures, answering exercises, and recording their results.

[0261] Step 9:

[0262] The device tracks the learner's progress in real time and sends it to the server, specifically recording the study time, answer results, and learning material browsing history, and uploading it immediately.

[0263] Step 10:

[0264] At the same time, the device uses an emotion engine to collect emotional data and transmit it to the server in real time, for example, analyzing facial expressions and voice tones during learning.

[0265] Step 11:

[0266] The server analyzes the received progress and emotion data to assess the learner's progress and learning effectiveness, and automatically adjusts the learning plan and materials as needed. For example, if the learner is feeling stressed, it will insert short breaks or relaxation materials to help them relax.

[0267] Step 12:

[0268] The server then sends the updated learning plan and materials back to the device to coordinate the learner's next learning experience, and the learner again accesses the new plan and materials through the device to continue their learning.

[0269] Example 2

[0270] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0271] Traditional learning systems provide personalized learning plans based only on a learner's learning history, interests, and strengths, but do not take into account the learner's emotional state or stress level, making it difficult to achieve optimal learning results. It is also difficult to easily track progress and adjust learning plans in real time.

[0272] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0273] In this invention, the server includes means for collecting a learner's learning history, interests, and strengths, means for collecting emotional data during learning, means for analyzing the collected learning history, interests, strengths, and emotional data to identify a learning pattern and an emotional state, means for generating a personalized learning plan based on the identified learning pattern and emotional state, means for providing appropriate learning materials based on the generated learning plan, means for tracking the learner's progress and emotional data during learning in real time, and means for adjusting the learning plan and learning materials based on the tracked progress and emotional data. This makes it possible to provide an optimal learning experience that takes into account the learner's individual characteristics and emotional state.

[0274] "Learning history" refers to a record of all of a learner's past learning activities, including the materials studied, test results, and study time.

[0275] "Interests" refer to areas or subjects that a learner is interested in and wants to learn about, such as a particular field of science or history.

[0276] "Strengths" refer to areas or subjects in which a learner is particularly good, and are assessed based on test results and learning outcomes.

[0277] "Emotional data" refers to information about the emotions of learners obtained from facial expressions, vocal tones, pulse rates, etc., displayed during learning. For example, happiness, sadness, stress, etc.

[0278] "Analysis" refers to the process of analyzing collected data using statistical methods and machine learning algorithms to extract meaningful information.

[0279] A "learning pattern" refers to a set of tendencies and behaviors identified based on a learner's learning history, interests, and strengths, including which subjects they frequently study and how they progress through their studies.

[0280] "Emotional state" refers to the emotions a learner is feeling at a particular time, including their motivation and stress levels while learning.

[0281] A "personalized learning plan" refers to a learning schedule and curriculum that is optimized based on the learner's individual characteristics and emotional state.

[0282] "Learning materials" refers to the materials and content that learners use to carry out learning activities, such as textbooks, video lectures, and exercises.

[0283] "Progress" refers to information that shows where a learner is at and how they are progressing in their studies, including the amount of time spent studying and the percentage of questions answered correctly.

[0284] "Real-time tracking" refers to the process of instantly monitoring learner activity and instantly collecting and analyzing that data.

[0285] "Adjustment" refers to the process of modifying or optimizing existing learning plans and materials based on the learner's progress and emotional state.

[0286] This invention is a system that provides a personalized learning experience according to the characteristics of each learner, and further combines it with an emotion engine that recognizes the user's emotions. This system is composed of a server, a terminal, a learner (user), and an emotion engine.

[0287] Server Operation

[0288] The server collects learners' learning history, interests, and strengths from multiple databases and online learning platforms. Specific platforms used include online learning platforms and databases. Collected data includes the learning materials viewed by the learner, test results, and study time. It also uses an emotion engine to collect learners' emotional data. Emotional data is obtained from facial expressions, voice tone, pulse rate, and other factors while studying. For this purpose, emotion recognition software and sensors are used.

[0289] The server preprocesses and analyzes the collected learning data and emotional data using machine learning algorithms (e.g., TENSORFLOW (registered trademark)). This analysis identifies the learner's learning patterns and emotional state. For example, it identifies which subjects the learner is strong in, which subjects they are interested in, and which subjects they are weak in. It also evaluates the learner's motivation and stress level during learning from the emotional data.

[0290] Based on the analysis results, the server automatically generates an optimized learning plan for each learner. This plan includes a curriculum and learning schedule designed to strengthen the learner's strengths and shore up their weaknesses. It also suggests learning plans based on emotional data and timelines when students are least stressed. Based on the generated learning plan, the server selects appropriate learning materials and sends links to the learner's device. For example, online lectures, practice questions, and simulation materials for specific units are provided.

[0291] Device behavior

[0292] The device receives the learning material link sent from the server and displays it so that the learner can access it. Specifically, the learning material is provided using a web browser or a dedicated learning application. The device also tracks the learner's learning activities in real time and sends the data to the server. This data includes information such as how much of each learning material the learner has studied and the percentage of questions they have answered correctly. The device's emotion engine collects emotional data such as facial expressions and tone of voice during learning and sends this data to the server in real time.

[0293] Content Adjustment

[0294] The server analyzes the user's progress and emotional data and automatically adjusts the learning plan and materials as needed, for example, suggesting more relaxing materials or breaks if the learner is fatigued or stressed.

[0295] Specific examples

[0296] For example, suppose Learner A is good at math and interested in science, but feels that he or she is not good at English. The server collects data on Learner A and identifies these characteristics. As a result, the learning plan provides a focus on math and science materials, and includes additional materials to strengthen the foundations of English. Also, if the emotion engine detects stress while Learner A is studying, the learning plan will be immediately adjusted to include short breaks or videos for relaxation.

[0297] Prompt Sentence Examples

[0298] Examples of prompts to input into a generative AI model include:

[0299] "If Learner A is feeling stressed, how can I provide learning materials that will help him relax?"

[0300] In this way, this system can provide a personalized learning experience that is tailored to the learner's characteristics and emotions, maximizing learning effectiveness.

[0301] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0302] Step 1: Data collection

[0303] The server collects data on learners' learning history, interests, and strengths from multiple databases and online learning platforms. Specifically, it uses APIs to obtain data such as learning material browsing history, test results, and study time from the learning platforms. The input is the raw data obtained from the API, and the output is the collected learning dataset.

[0304] Step 2: Collecting Emotional Data

[0305] Once the user starts learning, the device's emotion engine collects emotion data using a facial recognition camera, microphone, pulse sensor, etc. Specifically, emotion recognition software is used to analyze the user's facial expressions in real time. The input is raw sensor data, and the output is analyzed emotion data. This data is sent to the server in real time.

[0306] Step 3: Data Preprocessing

[0307] The server uses statistical analysis tools and machine learning algorithms to perform preprocessing based on the collected training data and emotion data. Specifically, it complements missing data and corrects outliers. The input is the collected raw data, and the output is the preprocessed data.

[0308] Step 4: Data analysis

[0309] The server analyzes the preprocessed data and identifies the learner's learning patterns and emotional state. It uses machine learning algorithms (e.g., TensorFlow) to identify which subjects the learner has strengths and interests in, as well as other learning characteristics. The input is the preprocessed dataset, and the output is the analysis results.

[0310] Step 5: Generate a lesson plan

[0311] Based on the analysis results, the server generates an optimized learning plan for each learner. This plan includes a specific curriculum and study schedule to utilize the learner's strengths and overcome their weaknesses. It also reflects emotional data to create a study schedule for times when students are least stressed. The input is the analyzed learning patterns and emotional data, and the output is a personalized learning plan.

[0312] Step 6: Provide teaching materials

[0313] The server selects appropriate learning materials based on the generated learning plan and sends links to the learner's device. For example, it provides online lectures and exercises for a specific unit. The input is the learning plan, and the output is the learning material links.

[0314] Step 7: Track progress

[0315] The device sends real-time progress data to the server as the learner progresses through the study, including study time, percentage of correct answers to questions, and materials viewed. The input is the learner's behavioral data, and the output is updated progress data.

[0316] Step 8: Collect emotional data

[0317] The device's emotion engine analyzes the user's facial expressions and tone of voice during training and sends the emotion data to the server. The input is emotion data collected in real time, and the output is the analyzed emotion data.

[0318] Step 9: Content Adjustment

[0319] The server evaluates the learner's progress and learning effectiveness based on the received progress and emotion data, and adjusts the learning plan and learning materials as needed. For example, if the learner is fatigued, it provides relaxation materials. The input is the updated progress and emotion data, and the output is the adjusted learning plan and learning materials.

[0320] (Application example 2)

[0321] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0322] Conventional learning and sales systems have struggled to provide personalized services based on the characteristics and emotions of individual learners and customers. This has led to problems such as lower customer satisfaction during learning and purchasing, and a lack of appropriate product suggestions. As a result, the effectiveness of learning and purchasing experiences are not maximized, and users' expectations cannot be met.

[0323] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0324] In this invention, the server includes means for collecting a learner's learning history, interests, and strengths, means for analyzing the collected learning history, interests, and strengths to identify a learning pattern, means for generating a personalized learning plan based on the identified learning pattern, means for providing appropriate learning materials based on the generated learning plan, means for tracking the learner's progress in real time, means for adjusting the learning plan and learning materials based on the tracked progress, means for selecting products based on the customer's purchasing history and interests, means for collecting and analyzing customer emotional data at the time of purchase, and means for providing personalized product suggestions and services based on the analyzed emotional data. This makes it possible to provide a personalized learning experience and purchasing experience according to the characteristics and emotions of each learner or customer, maximizing learning effectiveness and improving customer satisfaction.

[0325] "Learning history" is a record of a learner's past learning activities and course status.

[0326] "Interest" is the state in which a learner or customer has interest in a particular field or product.

[0327] "Strengths" are subjects or skills that a learner excels at, or characteristics of a product that make it easy for customers to buy.

[0328] A "learning pattern" is a tendency or characteristic that is derived from an analysis of a learner's learning behavior and learning history.

[0329] A "personalized learning plan" is a learning schedule and curriculum that is optimized according to the characteristics of each individual learner.

[0330] "Learning Materials" refers to learning content and resources provided to learners, including online lectures, exercises, and simulation materials.

[0331] "Progress" refers to the status or assessment that indicates how far a learner has progressed in their studies.

[0332] "Purchase history" is a record of products and services that a customer has purchased in the past.

[0333] "Emotional data" is data that represents the emotional state of a customer or learner, and is obtained from facial expressions, tone of voice, pulse rate, etc.

[0334] A "product suggestion" is a recommendation of a specific product or service to a customer to encourage them to purchase it.

[0335] "Services" are value-added activities and support provided to Customers.

[0336] A "machine learning algorithm" is a computational method used to analyze large amounts of data and extract patterns and features.

[0337] "Analysis" is the process of extracting meaningful information from collected data.

[0338] The present invention provides a system for providing a personalized experience according to the characteristics and emotional state of individual learners and customers. The system includes a server, a terminal, a user, and an emotion engine.

[0339] Server Operation

[0340] 1. User Identification and Login

[0341] The server authenticates the user using the user ID, sends a request to the API server, and retrieves the user information after authentication.

[0342] 2. Data Collection

[0343] The server collects data about learners' learning history, interests, and strengths, as well as customer purchasing history and interests. This information is collected automatically from multiple databases and online platforms.

[0344] 3. Emotional Data Analysis

[0345] The server analyzes audio and video data in real time using an emotion engine (e.g., EmotionEngine library) to acquire and analyze emotional data of customers and learners.

[0346] 4. Generating personalized suggestions

[0347] The server uses machine learning algorithms to generate personalized learning plans and product recommendations based on the collected data and analyzed emotional data.

[0348] 5. Real-time feedback

[0349] The server sends the generated learning plans and product suggestions to the learner's or customer's device in real time, enabling dynamic adjustments based on the user's progress and emotions.

[0350] Device behavior

[0351] 1. Display of educational materials or product suggestions

[0352] The terminal receives links to educational materials and product suggestions sent from the server and displays them so that the user can access them. For this purpose, a web browser or a dedicated application is used.

[0353] 2. Learning and purchasing data collection

[0354] The device tracks data in real time as users study materials or view products and transmits it to a server.

[0355] 3. Emotional Data Transmission

[0356] The device is equipped with an emotion engine that collects emotional data from the user's facial expressions and voice tone during training and transmits it to the server in real time.

[0357] Specific examples

[0358] For example, suppose Customer A visits a physical store and logs in to a dedicated app. Their past purchase history reveals that they are interested in health foods, and the app also analyzes their facial expressions and tone of voice to sense fatigue. In this case, the server suggests products with a relaxing effect and sends this information to Customer A's device in real time.

[0359] In this way, the system, which is composed of a server, a terminal, a user, and an emotion engine, provides a personalized experience according to individual characteristics and emotions, solving the conventional problem.

[0360] Prompt Sentence Examples

[0361] Develop a strategy for recommending specific products to customers based on their past purchase history and current emotional data. For example, if a customer is feeling stressed, suggest products that will help them relax.

[0362] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0363] Step 1:

[0364] User Awareness and Login

[0365] The server receives the user ID and sends a request to the authentication API server. The input is the user ID, and the output is the authenticated user information. This information includes past learning history, purchase history, interests, etc. The server stores this information and prepares it for data collection in the next step.

[0366] Step 2:

[0367] Data collection

[0368] The server retrieves data about the target user's past learning history, interests, and strengths, as well as customer purchasing history and interests, from databases and online platforms. In this step, the server accesses various databases to obtain and aggregate information about each user. The input is the user ID and authentication information, and the output is all associated historical information.

[0369] Step 3:

[0370] Emotional Data Analysis

[0371] The device used by the user captures the user's voice and video data in real time and analyzes it using an emotion engine. The server receives the analysis results and identifies the user's emotional state. The input is voice and video data, and the output is emotional data (e.g., stress level, enjoyment, tiredness, etc.). This emotional data is processed by the emotion engine.

[0372] Step 4:

[0373] Generate personalized suggestions

[0374] The server uses a machine learning algorithm to generate personalized learning plans and product suggestions based on the collected data (learning history, purchasing history, interests, strengths) and emotional data. The input here is all of the above data (learning history, interests, strengths, emotional data), and the output is individually optimized learning plans and product suggestions. The specific suggestions are generated using a generative AI model.

[0375] Step 5:

[0376] Real-time feedback

[0377] The server sends the generated learning plan and product suggestions to the user's device in real time. The device receives them and visually presents them to the user. The input is the generated optimization plan, and the output is the suggestions or plan displayed on the user's device. At this point, the user's learning and purchasing progress is tracked and analyzed again and adjusted as necessary.

[0378] Step 6:

[0379] Collecting progress and sentiment data

[0380] The device tracks the user's learning or purchasing progress in real time and sends new data to the server, including study time, accuracy rate of questions answered, purchase history, information about products viewed, and emotional changes during learning or purchasing. The input is the user's real-time activity data, and the output is a new dataset for analysis.

[0381] Step 7:

[0382] Coordination of plans and proposals

[0383] The server reevaluates the learning plan and product recommendations based on progress and new emotional data, making adjustments as needed. The input is real-time collected progress and emotional data, and the output is a re-adjusted personalized plan, ensuring that users always receive an optimized experience.

[0384] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0385] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0386] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0387] [Second embodiment]

[0388] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0389] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0390] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0391] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0392] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0393] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0394] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0395] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0396] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0397] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0398] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0399] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0400] The present invention is a system for providing a personalized learning experience according to the characteristics of each learner. The system includes a server, a terminal, and a learner (user).

[0401] Server Operation

[0402] The server performs the following sequence of actions:

[0403] 1. Data Collection: The server collects data about learners' learning history, interests, and strengths from multiple databases and online learning platforms, including the learning materials they have viewed, test results, and study time.

[0404] 2. Data analysis: Based on the collected data, the server uses machine learning algorithms to preprocess the data and analyze the learner's learning patterns, for example, identifying which subjects the learner has strengths, interests, or weaknesses in.

[0405] 3. Learning plan generation: Based on the analysis results, the server automatically generates an optimized learning plan for each learner, including a curriculum and learning schedule to further develop the learner's strengths and strengthen their weaknesses.

[0406] 4. Providing learning materials: Based on the generated learning plan, the server selects appropriate learning materials and sends links to the learner's device, such as online lectures, exercises, and simulation materials for a specific unit.

[0407] 5. Progress Tracking: As learners progress through their studies, their devices transmit real-time learning progress data to the server, including study time, correct answer rate, and learning material browsing history.

[0408] 6. Content Adjustment: The server analyzes the received progress data, evaluates the learner's progress and learning effectiveness, and automatically adjusts the learning plan and materials as needed. For example, if a learner is struggling with English, the server may provide additional, more basic learning materials.

[0409] Device behavior

[0410] The terminal works as follows:

[0411] 1. Displaying learning materials: Receives the link to the learning materials sent from the server and displays it so that learners can access it. For example, the learning materials are provided through a web browser or a dedicated learning application.

[0412] 2. Learning data collection: Learners' learning activities are tracked in real time and the data is sent to the server. This data includes information such as which learning materials the learner studied, how long they spent studying them, and the percentage of questions they answered correctly.

[0413] Specific examples

[0414] As a concrete example, let's take the case of Learner A. Learner A is good at math and has an interest in science, but feels that he or she is not good at English. The server collects data on Learner A and identifies these characteristics. As a result, the learning plan provides an emphasis on providing math and science materials, and also includes additional materials to strengthen the learner's English basics. Furthermore, as the learner progresses, the English materials are adjusted to gradually increase in difficulty, starting with more basic materials. In this way, the system can provide a personalized learning experience that is tailored to the learner's characteristics, maximizing learning effectiveness.

[0415] The processing flow will be explained below.

[0416] Step 1:

[0417] The server collects the learner's learning history, interests, and strengths. Specifically, it accesses the online learning platform and the school's academic performance database and retrieves data such as the learner's past learning activities, test results, and learning materials used based on the learner's login information.

[0418] Step 2:

[0419] The server preprocesses the collected data, cleansing it, filling in missing data, and integrating learner profiles, such as standardizing the format of test results and organizing learner interests into categories.

[0420] Step 3:

[0421] The server analyzes the data using machine learning algorithms, specifically cluster analysis and pattern recognition, to identify learners' learning patterns, strengths (e.g., math), interests (e.g., science), and weaknesses (e.g., English), thereby identifying the characteristics and trends of individual learners.

[0422] Step 4:

[0423] The server then generates a personalized learning plan based on the analysis, designing a specific curriculum and study schedule to strengthen the learner's strengths and address their weaknesses, for example, providing additional applied math problems each week and setting up special sessions to strengthen the learner's English fundamentals.

[0424] Step 5:

[0425] The server selects appropriate learning materials based on the generated learning plan and sends them to the learner's device. Specifically, it selects online learning materials, practice questions, simulation tools, etc. and generates links to them.

[0426] Step 6:

[0427] The terminal receives the learning material link sent from the server and displays it to the learner, who then uses it to begin the instructed learning activity, for example, by viewing the learning material through a dedicated learning application or a web browser.

[0428] Step 7:

[0429] Users (learners) use their devices to carry out learning activities according to the provided learning materials, such as watching online lectures, answering exercises, and recording their results.

[0430] Step 8:

[0431] The device tracks the learner's progress in real time and sends it to the server, specifically recording the study time, answer results, and learning material browsing history, and uploading it immediately.

[0432] Step 9:

[0433] The server analyzes the received progress data and evaluates the learner's progress and learning effectiveness, including learning speed, correct answer rate, and duration of learning.

[0434] Step 10:

[0435] The server adjusts the learning plan and materials based on the progress data. For example, if a learner is struggling with English material, it can add easier questions or provide supplementary materials, helping the learner reach their learning goals more effectively.

[0436] Example 1

[0437] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0438] There is a demand for providing a personalized learning experience tailored to each learner's characteristics in order to maximize learning effectiveness. However, conventional systems only collect data on learning history, interests, and strengths, but are unable to effectively utilize this information in learning plans and providing learning materials. Furthermore, there is a lack of means to track learners' progress in real time and adjust learning plans and materials as needed. As a result, it is difficult to provide optimal learning plans tailored to each learner's individual needs.

[0439] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0440] In this invention, the server includes means for collecting the learning history, interests, and strengths of a learner, means for analyzing the collected learning history, interests, and strengths to identify a learning pattern, means for generating a personalized learning plan based on the identified learning pattern, means for providing appropriate learning materials based on the generated learning plan, means for tracking the learner's progress in real time, means for adjusting the learning plan and learning materials based on the tracked progress, and means for evaluating each learner's progress and automatically redesigning and providing the learning materials and learning plan as necessary. This makes it possible to provide an optimal learning plan according to the individual needs of the learner and maximize the learning effect.

[0441] A "learning history" is a record of all learning activities that a learner has performed in the past, including information such as the learning materials viewed, study time, and test results.

[0442] "Interest" refers to the interest or curiosity a learner has in a particular field of study or subject.

[0443] "Strengths" refer to the outstanding abilities and achievements a learner has in a particular field or subject.

[0444] "Data collection means" refers to the functions and methods for collecting data from learners, such as their learning history, interests, and strengths.

[0445] "Data analysis means" refers to functions and methods for analyzing learners' learning patterns based on collected data.

[0446] "Learning patterns" refer to the characteristics and tendencies that indicate the type of learning behavior a learner will exhibit.

[0447] A "learning plan" refers to an optimal curriculum and schedule designed based on the learner's characteristics.

[0448] "Means for providing learning materials" refers to the functions and methods for delivering appropriate learning materials to learners based on their learning plans.

[0449] "Progress tracking means" refers to functions and methods for tracking the progress of learners' learning activities in real time.

[0450] "Progress" refers to information that indicates whether a learner is progressing according to their learning plan.

[0451] "Content adjustment means" refers to functions and methods for redesigning and adjusting learning plans and materials based on progress.

[0452] A "machine learning algorithm" refers to a computational method for automatically extracting and analyzing learning patterns from data.

[0453] "Evaluation tools" refer to functions and methods for analyzing learners' progress and judging the effectiveness of the learning content based on the results.

[0454] This invention is a system that provides a personalized learning experience tailored to the individual characteristics of each learner. This system includes a server, a terminal, and a learner (user). Specific embodiments of each element are described below.

[0455] Server Operation

[0456] The server performs the following series of operations to collect and analyze the learner's data and generate an individualized learning plan.

[0457] 1. Data Collection

[0458] The server collects data on the learner's learning history, interests, and strengths from multiple databases and online learning platforms, specifically by retrieving data from online learning platforms via APIs (e.g., online learning platform APIs) and querying internal databases.

[0459] 2. Data Analysis

[0460] Based on the collected data, the server uses machine learning algorithms to preprocess and analyze the data and identify the learner's learning patterns. Techniques such as data cleaning, feature engineering, and clustering are used. Examples of machine learning algorithms used include clustering and classification algorithms.

[0461] 3. Learning plan generation

[0462] Based on the analysis results, the server automatically generates an optimal learning plan for each learner. The learning plan includes learning progress, curriculum, and schedule. A rule-based engine and scheduler are used for automatic generation.

[0463] 4. Providing teaching materials

[0464] The server selects appropriate learning material links based on the generated learning plan and sends them to the terminal.A content management system is used to provide links to online lectures, exercises, simulation materials, etc.

[0465] 5. Progress Tracking

[0466] As the learner progresses with their studies, progress data is received in real time from the device, including study time, correct answer rate, and learning material viewing history. The progress data is automatically saved in a database and used for analysis.

[0467] 6. Content Adjustment

[0468] The server analyzes the received progress data and automatically redesigns and adjusts the learning plan and materials as needed, using machine learning models to adapt content to reinforce the learner's weaknesses.

[0469] Device behavior

[0470] The terminal is a device that receives the learning material link sent from the server and allows the user to carry out learning activities.

[0471] 1. Teaching material display

[0472] The device receives the link to the learning material sent from the server and displays it to the user in a browser or dedicated application. For example, software such as a web browser or a mobile application is used.

[0473] 2. Learning data collection

[0474] The device tracks learners' learning activity data in real time and sends it to a server. This data includes study time, correct answer rate, and learning material viewing history. A learning management system (LMS) or tracking software is used.

[0475] Specific examples

[0476] As a concrete example, consider the case of Learner A. Learner A is good at math and has an interest in science, but feels that he is not good at English. The server collects data on Learner A and identifies these characteristics. As a result, the learning plan provides him with a focus on math and science materials, and also includes additional materials to strengthen his English foundations. Furthermore, as he progresses in his studies, the English materials are adjusted to gradually increase in difficulty, starting with more basic ones.

[0477] This allows the system to provide a personalized learning experience tailored to each learner's characteristics, maximizing learning effectiveness.

[0478] Example prompts for generative AI models

[0479] Please provide a detailed description of how your program automatically generates a personalized learning plan based on data from a specific learner. Also, please specify how the program adjusts content based on the learner's progress, and what hardware and software are required. Please provide an example of how your program works for a specific learner.

[0480] This prompt sentence serves as a guideline for the generative AI model to understand the specific processing content of the system and generate it accurately.

[0481] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0482] System program processing flow

[0483] Step 1: Data collection

[0484] The server collects data on learners' learning history, interests, and strengths from multiple databases and online learning platforms. Input data is obtained from the learning platform API and internal databases. This data includes the learning materials viewed by the learner, test results, study time, etc. The server obtains this data and stores it in an initial database. The output is the collected learning data. Specifically, for example, learner A's test scores are obtained from the online learning platform.

[0485] Step 2: Data Preprocessing

[0486] The server performs data preprocessing based on the collected data. The input is the raw data collected in step 1, and the output is the clean data after preprocessing. Data preprocessing includes tasks such as imputing missing values, removing outliers, and standardizing. For example, the missing learning time data for learner A is imputed with the average value.

[0487] Step 3: Data analysis

[0488] The server analyzes the preprocessed data using a machine learning algorithm. The input is the preprocessed data, and the output is the analysis results that show the learner's learning patterns. Clustering and classification algorithms are used for the analysis. Specifically, from the data of Learner A, it is determined that he is strong in mathematics but weak in English.

[0489] Step 4: Generate a lesson plan

[0490] Based on the analysis results, the server generates an optimized learning plan for each learner. The input is the analysis results, and the output is the generated learning plan. The learning plan includes learning progress, curriculum, and schedule. For example, for Learner A, a plan is generated that strengthens math and science materials while also strengthening the basics of English.

[0491] Step 5: Provide teaching materials

[0492] The server selects appropriate learning material links based on the generated learning plan and sends them to the terminal. The input is the learning plan, and the output is the learning material links. Specifically, it provides links to online lectures and practice questions to Learner A.

[0493] Step 6: Implementing the learning activity

[0494] Users access learning materials provided by the server through their terminals and perform learning activities. The input is the link to the learning materials provided by the server, and the output is learning progress data. Learning activities include viewing learning materials, taking tests, and answering practice problems. For example, Learner A answers the provided math practice problems.

[0495] Step 7: Track your progress

[0496] The device collects learner activity data in real time and sends it to the server. The input is the learner's activity data, and the output is progress data. Specifically, data such as the study time and correct answer rate of Learner A is collected.

[0497] Step 8: Analyze progress and adjust content

[0498] The server analyzes the received progress data and redesigns and adjusts the learning plan and materials as necessary. The input is progress data, and the output is adjusted learning plans and materials. Analysis includes evaluating learning effectiveness and adding content to strengthen weak points. For example, if Learner A is struggling with English, additional materials at the basic level will be provided.

[0499] Through this series of processes, the system can provide a personalized learning experience tailored to the individual needs of each learner, maximizing learning effectiveness.

[0500] (Application example 1)

[0501] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0502] Conventional learning support systems have proven effective in providing personalized learning experiences based on the individual characteristics of each learner. However, when it comes to shopping experiences in brick-and-mortar stores, there is a need for personalization based on individual customers' purchasing histories and interests, and the technology to achieve this in real time has not yet been fully established. Therefore, there is a need for a system that can instantly grasp customers' movements and interests within a store and provide them with product information tailored to their needs.

[0503] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0504] In this invention, the server includes means for collecting a learner's learning history, interests, and strengths, means for analyzing the collected learning history, interests, and strengths to identify a learning pattern, means for generating a personalized learning plan based on the identified learning pattern, means for providing appropriate learning materials based on the generated learning plan, means for tracking the learner's progress in real time, means for adjusting the learning plan and learning materials based on the tracked progress, means for collecting a purchase history, interests, and preferences, means for analyzing the collected purchase history, interests, and preferences to identify a purchasing pattern, means for generating a personalized product recommendation list based on the identified purchasing pattern, means for providing appropriate product information based on the generated product recommendation list, means for tracking a customer's movement within a store in real time, and means for adjusting the product information based on the tracked movement status. This makes it possible to provide optimal learning materials and product information according to individual characteristics and interests in both learning and shopping experiences.

[0505] - "Learning history" refers to the overall record of a learner's past learning activities, including the materials viewed, test results, study time, etc.

[0506] "Interest" refers to the degree of interest or attention a learner has in a particular field or topic.

[0507] "Strengths" refer to areas or subjects in which a learner excels, including specific skills and areas of knowledge.

[0508] "Analysis" is the process of identifying specific patterns or characteristics from collected data.

[0509] "Personalization" refers to providing experiences and content that are optimized to the characteristics and preferences of individual learners or customers.

[0510] A "learning plan" refers to a plan for learning progress based on the characteristics and needs of the learner, and includes a curriculum and learning schedule.

[0511] "Instructional materials" refers to resources such as textbooks, online lectures, and exercises that learners use to advance their learning.

[0512] "Progress" refers to the current state of a learner's learning, including the amount of time spent studying and the degree of completion of tasks.

[0513] "Purchase history" refers to a record of a customer's past purchasing activities, including the products purchased, the time of purchase, and the frequency of purchases.

[0514] "Preferences" refer to the preferences that customers have for specific products or categories.

[0515] A "product recommendation list" is a list of products suggested to a customer based on analyzed purchasing patterns.

[0516] "Product Information" refers to detailed information about a product, including price, features, and reviews.

[0517] "In-store movement" refers to the movement of customers as they move around the store, including browsing and purchasing products.

[0518] "Adjustment" refers to updating plans and content in accordance with progress and circumstances, and keeping them in the optimal state.

[0519] This invention is a system that provides a personalized experience according to the individual characteristics and interests of learners and customers. It utilizes servers and terminals to optimize learning support for learners and the shopping experience in physical stores.

[0520] Server Operation and Configuration

[0521] The server includes the following means:

[0522] 1. Data collection method: The server collects learners' or customers' past behavioral data (learning history, purchasing history, interests, and strengths) from multiple databases and online platforms. Learning history includes test results and study time, and purchasing history includes purchase dates and times and purchased products.

[0523] 2. Data analysis method: The collected data is preprocessed and analyzed using machine learning algorithms. During the analysis process, programming languages ​​such as Python and libraries such as scikit-learn and Pandas are used to identify and identify users' learning and purchasing patterns.

[0524] 3. Personalized plan generation: Based on the analysis results, a personalized learning plan or product recommendation list is generated for each user. The learning plan includes a curriculum and a learning schedule.

[0525] 4. Providing appropriate educational materials and product information: Based on the generated plan, the most suitable educational materials and product information are provided to the user, including educational material lists and product offers.

[0526] 5. Progress and movement tracking: Learners' learning progress and customers' movements within the store are tracked in real time. IoT devices and GPS tracking are used for real-time tracking.

[0527] 6. Plan and information adjustment: Automatically adjust learning plans and content offerings based on tracking data, including adding more basic learning materials and updating product information to better suit customer preferences.

[0528] Terminal operation and configuration

[0529] The terminal works as follows:

[0530] 1. Data display means: Links to educational materials and product information provided by the server are displayed to the user. Specifically, this is done through a smartphone application or the display of smart glasses.

[0531] 2. Data collection method: Collects user operation and behavior data and sends it to the server in real time.

[0532] Specific examples

[0533] Specific examples of learning

[0534] Learner A is good at math but weak at English. The server collects data on this learner and analyzes their characteristics. Based on the analysis results, the server generates a plan to provide advanced math learning materials and basic English learning materials. As Learner A progresses in his / her studies, the server tracks his / her progress in real time and adjusts the learning materials accordingly.

[0535] Shopping examples

[0536] Customer B has spent a lot of money on electronic devices in the past. The server analyzes this customer's purchasing history and generates a list of products suitable for Customer B. As Customer B moves through the store, the server tracks his movements in real time and provides specific product information in the relevant product section.

[0537] Prompt Sentence Examples

[0538] Learner prompt:

[0539] "Please provide me with additional basic learning materials to enhance my English studies."

[0540] Shopping customer prompt:

[0541] "Can you recommend the latest electronic gadgets based on your recent purchasing history?"

[0542] The system allows learners and customers to enjoy an experience that is tailored to their individual characteristics and interests.

[0543] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0544] Step 1:

[0545] Data collection

[0546] The server collects learner or customer data from existing databases and online platforms.

[0547] Input: Learning history, interests, strengths, purchasing history, preference data.

[0548] Output: The collected dataset.

[0549] Specific operation: The server retrieves user data from the database using an SQL query and saves it as a data frame.

[0550] Step 2:

[0551] Data Preprocessing

[0552] The server preprocesses the collected data and formats it into a form suitable for analysis.

[0553] Input: The collected dataset.

[0554] Output: The preprocessed dataset.

[0555] Specific operation: The server performs preprocessing such as imputing missing values, scaling, and encoding categorical data.

[0556] Step 3:

[0557] Data analysis

[0558] The server uses the pre-processed data and analyzes it with machine learning algorithms to identify user patterns.

[0559] Input: The preprocessed dataset.

[0560] Output: Learning patterns and purchasing patterns.

[0561] Specific operation: The server applies PCA (principal component analysis) and clustering algorithms (such as KMeans) to extract specific patterns.

[0562] Step 4:

[0563] Personalized Plan Generation

[0564] Based on the analysis results, the server generates a personalized study plan or product recommendation list for each user.

[0565] Input: Learning patterns and purchasing patterns.

[0566] Output: A personalized study plan or product list.

[0567] How it works: The server uses a specific algorithm to generate a plan or list optimized for each user, and then registers it in the user profile.

[0568] Step 5:

[0569] Providing appropriate educational materials and product information

[0570] The server provides the user with appropriate educational materials and product information based on the generated plan.

[0571] Input: A personalized study plan or product list.

[0572] Output: Teaching material links, product information.

[0573] Specific operation: The server generates links and information and sends them to the user's device.

[0574] Step 6:

[0575] Progress and movement tracking

[0576] The terminal tracks the user's learning and shopping progress in real time and transmits the data to a server.

[0577] Input: Learning progress data, in-store movement data.

[0578] Output: Real-time tracking data.

[0579] Specific operation: The device collects data using sensors (such as eye tracking in smart glasses or GPS in a smartphone) and sends it to a server.

[0580] Step 7:

[0581] Planning and Information Coordination

[0582] The server adjusts the lesson plan and product information based on the tracking data.

[0583] Input: Real-time tracking data.

[0584] Output: Tailored lesson plan, product list.

[0585] Specific operation: The server analyzes the collected data, regenerates appropriate content and schedules, and provides them to the user.

[0586] As described above, this system provides an optimized experience based on the characteristics of learners and customers. However, it is essential that the server performs appropriate processing at each step and updates and provides information in real time in cooperation with the device.

[0587] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0588] This invention is a system for providing a personalized learning experience according to the characteristics of each learner, and also combines it with an emotion engine that recognizes the user's emotions. This system is composed of a server, a terminal, a learner (user), and an emotion engine.

[0589] Server Operation

[0590] The server performs the following sequence of actions:

[0591] 1. Data Collection: The server collects data about learners' learning history, interests, and strengths from multiple databases and online learning platforms, including the learning materials they have viewed, test results, and study time.

[0592] 2. Emotion data collection: The server uses an emotion engine to collect the learner's emotional data, which is obtained from facial expressions, voice tones, pulse rates, etc.

[0593] 3. Data Analysis: Based on the collected learning data and emotional data, the server uses machine learning algorithms to preprocess the data and analyze the learner's learning patterns. For example, it identifies which subjects the learner is strong in, which subjects they are interested in, and which subjects they struggle with. It also evaluates the learner's motivation and stress level during learning based on emotional data.

[0594] 4. Learning plan generation: Based on the analysis results, the server automatically generates an optimized learning plan for each learner. This plan includes a specific curriculum and learning schedule designed to strengthen the learner's strengths and address their weaknesses. Based on emotional data, the server also suggests learning plans for times when students are least stressed.

[0595] 5. Providing learning materials: Based on the generated learning plan, the server selects appropriate learning materials and sends links to the learner's device, such as online lectures, exercises, and simulation materials for a specific unit.

[0596] 6. Progress Tracking: As learners progress through their studies, their devices transmit real-time learning progress data to the server, including study time, correct answer rate, and learning material browsing history.

[0597] 7. Emotional Data Collection: Collect emotional data in real time using an emotion engine, along with the learner's progress, for example, by analyzing facial expressions and vocal tones during learning.

[0598] 8. Content Adjustment: The server analyzes the received progress and emotion data to evaluate the learner's progress and learning effectiveness. If necessary, it automatically adjusts the learning plan and materials. For example, if the learner is tired or stressed, it provides more relaxing materials and methods.

[0599] Device behavior

[0600] The terminal works as follows:

[0601] 1. Displaying learning materials: Receives the link to the learning materials sent from the server and displays it so that learners can access it. For example, the learning materials are provided through a web browser or a dedicated learning application.

[0602] 2. Learning data collection: Learners' learning activities are tracked in real time and the data is sent to the server. This data includes information such as which learning materials the learner studied, how long they spent studying them, and the percentage of questions they answered correctly.

[0603] 3. Emotion data transmission: The emotion engine transmits the collected emotion data to the server in real time.

[0604] Specific examples

[0605] As a concrete example, consider the case of Learner A. Learner A is good at math and has an interest in science, but feels that he or she is not good at English. The server collects data on Learner A and identifies these characteristics. As a result, the learning plan provides Learner A with a focus on math and science materials, and also includes additional materials to strengthen his or her English foundation. Furthermore, if the emotion engine detects stress while Learner A is studying, it immediately adjusts the learning plan and inserts short breaks and relaxation materials to help him or her relax. In this way, the system can provide a personalized learning experience that is tailored to the learner's characteristics and emotions, maximizing learning effectiveness.

[0606] The processing flow will be explained below.

[0607] Step 1:

[0608] The server collects the learner's learning history, interests, and strengths. Specifically, it accesses the online learning platform and the school's academic performance database and retrieves data such as the learner's past learning activities, test results, and learning materials used based on the learner's login information.

[0609] Step 2:

[0610] The server collects emotional data using an emotion engine. Specifically, it monitors the learner's facial expressions, voice tone, pulse rate, etc., and records them as emotional data. For example, a learner may use a webcam to analyze changes in their facial expressions in real time.

[0611] Step 3:

[0612] The server preprocesses the collected learning and sentiment data, cleansing the data, filling in missing data, and integrating learner profiles, such as standardizing the format of test results and organizing learner interests into categories.

[0613] Step 4:

[0614] The server analyzes the data using machine learning algorithms, such as cluster analysis and pattern recognition, to identify learners' learning patterns, strengths (e.g., math), interests (e.g., science), and weaknesses (e.g., English), and also assesses their stress levels and motivation during learning based on emotional data.

[0615] Step 5:

[0616] The server then generates a personalized learning plan based on the analysis results, designing a specific curriculum and study schedule, such as providing high-intensity learning at times when stress is low and lighter learning materials when relaxation is needed.

[0617] Step 6:

[0618] The server selects appropriate learning materials based on the generated learning plan and sends them to the learner's device. For example, it selects online lectures, exercises, simulation materials, etc. for a specific unit and generates links to them.

[0619] Step 7:

[0620] The terminal receives the learning material link sent from the server and displays it to the learner, who then uses it to begin the instructed learning activity, for example, by viewing the learning material through a dedicated learning application or a web browser.

[0621] Step 8:

[0622] Users (learners) use their devices to carry out learning activities according to the provided learning materials, such as watching online lectures, answering exercises, and recording their results.

[0623] Step 9:

[0624] The device tracks the learner's progress in real time and sends it to the server, specifically recording the study time, answer results, and learning material browsing history, and uploading it immediately.

[0625] Step 10:

[0626] At the same time, the device uses an emotion engine to collect emotional data and transmit it to the server in real time, for example, analyzing facial expressions and voice tones during learning.

[0627] Step 11:

[0628] The server analyzes the received progress and emotion data to assess the learner's progress and learning effectiveness, and automatically adjusts the learning plan and materials as needed. For example, if the learner is feeling stressed, it will insert short breaks or relaxation materials to help them relax.

[0629] Step 12:

[0630] The server then sends the updated learning plan and materials back to the device to coordinate the learner's next learning experience, and the learner again accesses the new plan and materials through the device to continue their learning.

[0631] Example 2

[0632] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0633] Traditional learning systems provide personalized learning plans based only on a learner's learning history, interests, and strengths, but do not take into account the learner's emotional state or stress level, making it difficult to achieve optimal learning results. It is also difficult to easily track progress and adjust learning plans in real time.

[0634] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0635] In this invention, the server includes means for collecting a learner's learning history, interests, and strengths, means for collecting emotional data during learning, means for analyzing the collected learning history, interests, strengths, and emotional data to identify a learning pattern and an emotional state, means for generating a personalized learning plan based on the identified learning pattern and emotional state, means for providing appropriate learning materials based on the generated learning plan, means for tracking the learner's progress and emotional data during learning in real time, and means for adjusting the learning plan and learning materials based on the tracked progress and emotional data. This makes it possible to provide an optimal learning experience that takes into account the learner's individual characteristics and emotional state.

[0636] "Learning history" refers to a record of all of a learner's past learning activities, including the materials studied, test results, and study time.

[0637] "Interests" refer to areas or subjects that a learner is interested in and wants to learn about, such as a particular field of science or history.

[0638] "Strengths" refer to areas or subjects in which a learner is particularly good, and are assessed based on test results and learning outcomes.

[0639] "Emotional data" refers to information about the emotions of learners obtained from facial expressions, vocal tones, pulse rates, etc., displayed during learning. For example, happiness, sadness, stress, etc.

[0640] "Analysis" refers to the process of analyzing collected data using statistical methods and machine learning algorithms to extract meaningful information.

[0641] A "learning pattern" refers to a set of tendencies and behaviors identified based on a learner's learning history, interests, and strengths, including which subjects they frequently study and how they progress through their studies.

[0642] "Emotional state" refers to the emotions a learner is feeling at a particular time, including their motivation and stress levels while learning.

[0643] A "personalized learning plan" refers to a learning schedule and curriculum that is optimized based on the learner's individual characteristics and emotional state.

[0644] "Learning materials" refers to the materials and content that learners use to carry out learning activities, such as textbooks, video lectures, and exercises.

[0645] "Progress" refers to information that shows where a learner is at and how they are progressing in their studies, including the amount of time spent studying and the percentage of questions answered correctly.

[0646] "Real-time tracking" refers to the process of instantly monitoring learner activity and instantly collecting and analyzing that data.

[0647] "Adjustment" refers to the process of modifying or optimizing existing learning plans and materials based on the learner's progress and emotional state.

[0648] This invention is a system that provides a personalized learning experience according to the characteristics of each learner, and further combines it with an emotion engine that recognizes the user's emotions. This system is composed of a server, a terminal, a learner (user), and an emotion engine.

[0649] Server Operation

[0650] The server collects learners' learning history, interests, and strengths from multiple databases and online learning platforms. Specific platforms used include online learning platforms and databases. Collected data includes the learning materials viewed by the learner, test results, and study time. It also uses an emotion engine to collect learners' emotional data. Emotional data is obtained from facial expressions, voice tone, pulse rate, and other factors while studying. For this purpose, emotion recognition software and sensors are used.

[0651] The server preprocesses and analyzes the collected learning data and emotional data using machine learning algorithms (e.g., TensorFlow). This analysis identifies the learner's learning patterns and emotional state. For example, it identifies which subjects the learner is strong in, which subjects they are interested in, and which subjects they are weak in. It also evaluates the learner's motivation and stress level during learning based on the emotional data.

[0652] Based on the analysis results, the server automatically generates an optimized learning plan for each learner. This plan includes a curriculum and learning schedule designed to strengthen the learner's strengths and shore up their weaknesses. It also suggests learning plans based on emotional data and timelines when students are least stressed. Based on the generated learning plan, the server selects appropriate learning materials and sends links to the learner's device. For example, online lectures, practice questions, and simulation materials for specific units are provided.

[0653] Device behavior

[0654] The device receives the learning material link sent from the server and displays it so that the learner can access it. Specifically, the learning material is provided using a web browser or a dedicated learning application. The device also tracks the learner's learning activities in real time and sends the data to the server. This data includes information such as how much of each learning material the learner has studied and the percentage of questions they have answered correctly. The device's emotion engine collects emotional data such as facial expressions and tone of voice during learning and sends this data to the server in real time.

[0655] Content Adjustment

[0656] The server analyzes the user's progress and emotional data and automatically adjusts the learning plan and materials as needed, for example, suggesting more relaxing materials or breaks if the learner is fatigued or stressed.

[0657] Specific examples

[0658] For example, suppose Learner A is good at math and interested in science, but feels that he or she is not good at English. The server collects data on Learner A and identifies these characteristics. As a result, the learning plan provides a focus on math and science materials, and includes additional materials to strengthen the foundations of English. Also, if the emotion engine detects stress while Learner A is studying, the learning plan will be immediately adjusted to include short breaks or videos for relaxation.

[0659] Prompt Sentence Examples

[0660] Examples of prompts to input into a generative AI model include:

[0661] "If Learner A is feeling stressed, how can I provide learning materials that will help him relax?"

[0662] In this way, this system can provide a personalized learning experience that is tailored to the learner's characteristics and emotions, maximizing learning effectiveness.

[0663] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0664] Step 1: Data collection

[0665] The server collects data on learners' learning history, interests, and strengths from multiple databases and online learning platforms. Specifically, it uses APIs to obtain data such as learning material browsing history, test results, and study time from the learning platforms. The input is the raw data obtained from the API, and the output is the collected learning dataset.

[0666] Step 2: Collecting Emotional Data

[0667] Once the user starts learning, the device's emotion engine collects emotion data using a facial recognition camera, microphone, pulse sensor, etc. Specifically, emotion recognition software is used to analyze the user's facial expressions in real time. The input is raw sensor data, and the output is analyzed emotion data. This data is sent to the server in real time.

[0668] Step 3: Data Preprocessing

[0669] The server uses statistical analysis tools and machine learning algorithms to perform preprocessing based on the collected training data and emotion data. Specifically, it complements missing data and corrects outliers. The input is the collected raw data, and the output is the preprocessed data.

[0670] Step 4: Data analysis

[0671] The server analyzes the preprocessed data and identifies the learner's learning patterns and emotional state. It uses machine learning algorithms (e.g., TensorFlow) to identify which subjects the learner has strengths and interests in, as well as other learning characteristics. The input is the preprocessed dataset, and the output is the analysis results.

[0672] Step 5: Generate a lesson plan

[0673] Based on the analysis results, the server generates an optimized learning plan for each learner. This plan includes a specific curriculum and study schedule to utilize the learner's strengths and overcome their weaknesses. It also reflects emotional data to create a study schedule for times when students are least stressed. The input is the analyzed learning patterns and emotional data, and the output is a personalized learning plan.

[0674] Step 6: Provide teaching materials

[0675] The server selects appropriate learning materials based on the generated learning plan and sends links to the learner's device. For example, it provides online lectures and exercises for a specific unit. The input is the learning plan, and the output is the learning material links.

[0676] Step 7: Track progress

[0677] The device sends real-time progress data to the server as the learner progresses through the study, including study time, percentage of correct answers to questions, and materials viewed. The input is the learner's behavioral data, and the output is updated progress data.

[0678] Step 8: Collect emotional data

[0679] The device's emotion engine analyzes the user's facial expressions and tone of voice during training and sends the emotion data to the server. The input is emotion data collected in real time, and the output is the analyzed emotion data.

[0680] Step 9: Content Adjustment

[0681] The server evaluates the learner's progress and learning effectiveness based on the received progress and emotion data, and adjusts the learning plan and learning materials as needed. For example, if the learner is fatigued, it provides relaxation materials. The input is the updated progress and emotion data, and the output is the adjusted learning plan and learning materials.

[0682] (Application example 2)

[0683] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0684] Conventional learning and sales systems have struggled to provide personalized services based on the characteristics and emotions of individual learners and customers. This has led to problems such as lower customer satisfaction during learning and purchasing, and a lack of appropriate product suggestions. As a result, the effectiveness of learning and purchasing experiences are not maximized, and users' expectations cannot be met.

[0685] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0686] In this invention, the server includes means for collecting a learner's learning history, interests, and strengths, means for analyzing the collected learning history, interests, and strengths to identify a learning pattern, means for generating a personalized learning plan based on the identified learning pattern, means for providing appropriate learning materials based on the generated learning plan, means for tracking the learner's progress in real time, means for adjusting the learning plan and learning materials based on the tracked progress, means for selecting products based on the customer's purchasing history and interests, means for collecting and analyzing customer emotional data at the time of purchase, and means for providing personalized product suggestions and services based on the analyzed emotional data. This makes it possible to provide a personalized learning experience and purchasing experience according to the characteristics and emotions of each learner or customer, maximizing learning effectiveness and improving customer satisfaction.

[0687] "Learning history" is a record of a learner's past learning activities and course status.

[0688] "Interest" is the state in which a learner or customer has interest in a particular field or product.

[0689] "Strengths" are subjects or skills that a learner excels at, or characteristics of a product that make it easy for customers to buy.

[0690] A "learning pattern" is a tendency or characteristic that is derived from an analysis of a learner's learning behavior and learning history.

[0691] A "personalized learning plan" is a learning schedule and curriculum that is optimized according to the characteristics of each individual learner.

[0692] "Learning Materials" refers to learning content and resources provided to learners, including online lectures, exercises, and simulation materials.

[0693] "Progress" refers to the status or assessment that indicates how far a learner has progressed in their studies.

[0694] "Purchase history" is a record of products and services that a customer has purchased in the past.

[0695] "Emotional data" is data that represents the emotional state of a customer or learner, and is obtained from facial expressions, tone of voice, pulse rate, etc.

[0696] A "product suggestion" is a recommendation of a specific product or service to a customer to encourage them to purchase it.

[0697] "Services" are value-added activities and support provided to Customers.

[0698] A "machine learning algorithm" is a computational method used to analyze large amounts of data and extract patterns and features.

[0699] "Analysis" is the process of extracting meaningful information from collected data.

[0700] The present invention provides a system for providing a personalized experience according to the characteristics and emotional state of individual learners and customers. The system includes a server, a terminal, a user, and an emotion engine.

[0701] Server Operation

[0702] 1. User Identification and Login

[0703] The server authenticates the user using the user ID, sends a request to the API server, and retrieves the user information after authentication.

[0704] 2. Data Collection

[0705] The server collects data about learners' learning history, interests, and strengths, as well as customer purchasing history and interests. This information is collected automatically from multiple databases and online platforms.

[0706] 3. Emotional Data Analysis

[0707] The server analyzes audio and video data in real time using an emotion engine (e.g., EmotionEngine library) to acquire and analyze emotional data of customers and learners.

[0708] 4. Generating personalized suggestions

[0709] The server uses machine learning algorithms to generate personalized learning plans and product recommendations based on the collected data and analyzed emotional data.

[0710] 5. Real-time feedback

[0711] The server sends the generated learning plans and product suggestions to the learner's or customer's device in real time, enabling dynamic adjustments based on the user's progress and emotions.

[0712] Device behavior

[0713] 1. Display of educational materials or product suggestions

[0714] The terminal receives links to educational materials and product suggestions sent from the server and displays them so that the user can access them. For this purpose, a web browser or a dedicated application is used.

[0715] 2. Learning and purchasing data collection

[0716] The device tracks data in real time as users study materials or view products and transmits it to a server.

[0717] 3. Emotional Data Transmission

[0718] The device is equipped with an emotion engine that collects emotional data from the user's facial expressions and voice tone during training and transmits it to the server in real time.

[0719] Specific examples

[0720] For example, suppose Customer A visits a physical store and logs in to a dedicated app. Their past purchase history reveals that they are interested in health foods, and the app also analyzes their facial expressions and tone of voice to sense fatigue. In this case, the server suggests products with a relaxing effect and sends this information to Customer A's device in real time.

[0721] In this way, the system, which is composed of a server, a terminal, a user, and an emotion engine, provides a personalized experience according to individual characteristics and emotions, solving the conventional problem.

[0722] Prompt Sentence Examples

[0723] Develop a strategy for recommending specific products to customers based on their past purchase history and current emotional data. For example, if a customer is feeling stressed, suggest products that will help them relax.

[0724] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0725] Step 1:

[0726] User Awareness and Login

[0727] The server receives the user ID and sends a request to the authentication API server. The input is the user ID, and the output is the authenticated user information. This information includes past learning history, purchase history, interests, etc. The server stores this information and prepares it for data collection in the next step.

[0728] Step 2:

[0729] Data collection

[0730] The server retrieves data about the target user's past learning history, interests, and strengths, as well as customer purchasing history and interests, from databases and online platforms. In this step, the server accesses various databases to obtain and aggregate information about each user. The input is the user ID and authentication information, and the output is all associated historical information.

[0731] Step 3:

[0732] Emotional Data Analysis

[0733] The device used by the user captures the user's voice and video data in real time and analyzes it using an emotion engine. The server receives the analysis results and identifies the user's emotional state. The input is voice and video data, and the output is emotional data (e.g., stress level, enjoyment, tiredness, etc.). This emotional data is processed by the emotion engine.

[0734] Step 4:

[0735] Generate personalized suggestions

[0736] The server uses a machine learning algorithm to generate personalized learning plans and product suggestions based on the collected data (learning history, purchasing history, interests, strengths) and emotional data. The input here is all of the above data (learning history, interests, strengths, emotional data), and the output is individually optimized learning plans and product suggestions. The specific suggestions are generated using a generative AI model.

[0737] Step 5:

[0738] Real-time feedback

[0739] The server sends the generated learning plan and product suggestions to the user's device in real time. The device receives them and visually presents them to the user. The input is the generated optimization plan, and the output is the suggestions or plan displayed on the user's device. At this point, the user's learning and purchasing progress is tracked and analyzed again and adjusted as necessary.

[0740] Step 6:

[0741] Collecting progress and sentiment data

[0742] The device tracks the user's learning or purchasing progress in real time and sends new data to the server, including study time, accuracy rate of questions answered, purchase history, information about products viewed, and emotional changes during learning or purchasing. The input is the user's real-time activity data, and the output is a new dataset for analysis.

[0743] Step 7:

[0744] Coordination of plans and proposals

[0745] The server reevaluates the learning plan and product recommendations based on progress and new emotional data, making adjustments as needed. The input is real-time collected progress and emotional data, and the output is a re-adjusted personalized plan, ensuring that users always receive an optimized experience.

[0746] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0747] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0748] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0749] [Third embodiment]

[0750] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0751] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0752] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0753] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0754] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0755] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0756] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0757] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0758] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0759] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0760] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0761] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0762] The present invention is a system for providing a personalized learning experience according to the characteristics of each learner. The system includes a server, a terminal, and a learner (user).

[0763] Server Operation

[0764] The server performs the following sequence of actions:

[0765] 1. Data Collection: The server collects data about learners' learning history, interests, and strengths from multiple databases and online learning platforms, including the learning materials they have viewed, test results, and study time.

[0766] 2. Data analysis: Based on the collected data, the server uses machine learning algorithms to preprocess the data and analyze the learner's learning patterns, for example, identifying which subjects the learner has strengths, interests, or weaknesses in.

[0767] 3. Learning plan generation: Based on the analysis results, the server automatically generates an optimized learning plan for each learner, including a curriculum and learning schedule to further develop the learner's strengths and strengthen their weaknesses.

[0768] 4. Providing learning materials: Based on the generated learning plan, the server selects appropriate learning materials and sends links to the learner's device, such as online lectures, exercises, and simulation materials for a specific unit.

[0769] 5. Progress Tracking: As learners progress through their studies, their devices transmit real-time learning progress data to the server, including study time, correct answer rate, and learning material browsing history.

[0770] 6. Content Adjustment: The server analyzes the received progress data, evaluates the learner's progress and learning effectiveness, and automatically adjusts the learning plan and materials as needed. For example, if a learner is struggling with English, the server may provide additional, more basic learning materials.

[0771] Device behavior

[0772] The terminal works as follows:

[0773] 1. Displaying learning materials: Receives the link to the learning materials sent from the server and displays it so that learners can access it. For example, the learning materials are provided through a web browser or a dedicated learning application.

[0774] 2. Learning data collection: Learners' learning activities are tracked in real time and the data is sent to the server. This data includes information such as which learning materials the learner studied, how long they spent studying them, and the percentage of questions they answered correctly.

[0775] Specific examples

[0776] As a concrete example, let's take the case of Learner A. Learner A is good at math and has an interest in science, but feels that he or she is not good at English. The server collects data on Learner A and identifies these characteristics. As a result, the learning plan provides an emphasis on providing math and science materials, and also includes additional materials to strengthen the learner's English basics. Furthermore, as the learner progresses, the English materials are adjusted to gradually increase in difficulty, starting with more basic materials. In this way, the system can provide a personalized learning experience that is tailored to the learner's characteristics, maximizing learning effectiveness.

[0777] The processing flow will be explained below.

[0778] Step 1:

[0779] The server collects the learner's learning history, interests, and strengths. Specifically, it accesses the online learning platform and the school's academic performance database and retrieves data such as the learner's past learning activities, test results, and learning materials used based on the learner's login information.

[0780] Step 2:

[0781] The server preprocesses the collected data, cleansing it, filling in missing data, and integrating learner profiles, such as standardizing the format of test results and organizing learner interests into categories.

[0782] Step 3:

[0783] The server analyzes the data using machine learning algorithms, specifically cluster analysis and pattern recognition, to identify learners' learning patterns, strengths (e.g., math), interests (e.g., science), and weaknesses (e.g., English), thereby identifying the characteristics and trends of individual learners.

[0784] Step 4:

[0785] The server then generates a personalized learning plan based on the analysis, designing a specific curriculum and study schedule to strengthen the learner's strengths and address their weaknesses, for example, providing additional applied math problems each week and setting up special sessions to strengthen the learner's English fundamentals.

[0786] Step 5:

[0787] The server selects appropriate learning materials based on the generated learning plan and sends them to the learner's device. Specifically, it selects online learning materials, practice questions, simulation tools, etc. and generates links to them.

[0788] Step 6:

[0789] The terminal receives the learning material link sent from the server and displays it to the learner, who then uses it to begin the instructed learning activity, for example, by viewing the learning material through a dedicated learning application or a web browser.

[0790] Step 7:

[0791] Users (learners) use their devices to carry out learning activities according to the provided learning materials, such as watching online lectures, answering exercises, and recording their results.

[0792] Step 8:

[0793] The device tracks the learner's progress in real time and sends it to the server, specifically recording the study time, answer results, and learning material browsing history, and uploading it immediately.

[0794] Step 9:

[0795] The server analyzes the received progress data and evaluates the learner's progress and learning effectiveness, including learning speed, correct answer rate, and duration of learning.

[0796] Step 10:

[0797] The server adjusts the learning plan and materials based on the progress data. For example, if a learner is struggling with English material, it can add easier questions or provide supplementary materials, helping the learner reach their learning goals more effectively.

[0798] Example 1

[0799] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0800] There is a demand for providing a personalized learning experience tailored to each learner's characteristics in order to maximize learning effectiveness. However, conventional systems only collect data on learning history, interests, and strengths, but are unable to effectively utilize this information in learning plans and providing learning materials. Furthermore, there is a lack of means to track learners' progress in real time and adjust learning plans and materials as needed. As a result, it is difficult to provide optimal learning plans tailored to each learner's individual needs.

[0801] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0802] In this invention, the server includes means for collecting the learning history, interests, and strengths of a learner, means for analyzing the collected learning history, interests, and strengths to identify a learning pattern, means for generating a personalized learning plan based on the identified learning pattern, means for providing appropriate learning materials based on the generated learning plan, means for tracking the learner's progress in real time, means for adjusting the learning plan and learning materials based on the tracked progress, and means for evaluating each learner's progress and automatically redesigning and providing the learning materials and learning plan as necessary. This makes it possible to provide an optimal learning plan according to the individual needs of the learner and maximize the learning effect.

[0803] A "learning history" is a record of all learning activities that a learner has performed in the past, including information such as the learning materials viewed, study time, and test results.

[0804] "Interest" refers to the interest or curiosity a learner has in a particular field of study or subject.

[0805] "Strengths" refer to the outstanding abilities and achievements a learner has in a particular field or subject.

[0806] "Data collection means" refers to the functions and methods for collecting data from learners, such as their learning history, interests, and strengths.

[0807] "Data analysis means" refers to functions and methods for analyzing learners' learning patterns based on collected data.

[0808] "Learning patterns" refer to the characteristics and tendencies that indicate the type of learning behavior a learner will exhibit.

[0809] A "learning plan" refers to an optimal curriculum and schedule designed based on the learner's characteristics.

[0810] "Means for providing learning materials" refers to the functions and methods for delivering appropriate learning materials to learners based on their learning plans.

[0811] "Progress tracking means" refers to functions and methods for tracking the progress of learners' learning activities in real time.

[0812] "Progress" refers to information that indicates whether a learner is progressing according to their learning plan.

[0813] "Content adjustment means" refers to functions and methods for redesigning and adjusting learning plans and materials based on progress.

[0814] A "machine learning algorithm" refers to a computational method for automatically extracting and analyzing learning patterns from data.

[0815] "Evaluation tools" refer to functions and methods for analyzing learners' progress and judging the effectiveness of the learning content based on the results.

[0816] This invention is a system that provides a personalized learning experience tailored to the individual characteristics of each learner. This system includes a server, a terminal, and a learner (user). Specific embodiments of each element are described below.

[0817] Server Operation

[0818] The server performs the following series of operations to collect and analyze the learner's data and generate an individualized learning plan.

[0819] 1. Data Collection

[0820] The server collects data on the learner's learning history, interests, and strengths from multiple databases and online learning platforms, specifically by retrieving data from online learning platforms via APIs (e.g., online learning platform APIs) and querying internal databases.

[0821] 2. Data Analysis

[0822] Based on the collected data, the server uses machine learning algorithms to preprocess and analyze the data and identify the learner's learning patterns. Techniques such as data cleaning, feature engineering, and clustering are used. Examples of machine learning algorithms used include clustering and classification algorithms.

[0823] 3. Learning plan generation

[0824] Based on the analysis results, the server automatically generates an optimal learning plan for each learner. The learning plan includes learning progress, curriculum, and schedule. A rule-based engine and scheduler are used for automatic generation.

[0825] 4. Providing teaching materials

[0826] The server selects appropriate learning material links based on the generated learning plan and sends them to the terminal.A content management system is used to provide links to online lectures, exercises, simulation materials, etc.

[0827] 5. Progress Tracking

[0828] As the learner progresses with their studies, progress data is received in real time from the device, including study time, correct answer rate, and learning material viewing history. The progress data is automatically saved in a database and used for analysis.

[0829] 6. Content Adjustment

[0830] The server analyzes the received progress data and automatically redesigns and adjusts the learning plan and materials as needed, using machine learning models to adapt content to reinforce the learner's weaknesses.

[0831] Device behavior

[0832] The terminal is a device that receives the learning material link sent from the server and allows the user to carry out learning activities.

[0833] 1. Teaching material display

[0834] The device receives the link to the learning material sent from the server and displays it to the user in a browser or dedicated application. For example, software such as a web browser or a mobile application is used.

[0835] 2. Learning data collection

[0836] The device tracks learners' learning activity data in real time and sends it to a server. This data includes study time, correct answer rate, and learning material viewing history. A learning management system (LMS) or tracking software is used.

[0837] Specific examples

[0838] As a concrete example, consider the case of Learner A. Learner A is good at math and has an interest in science, but feels that he is not good at English. The server collects data on Learner A and identifies these characteristics. As a result, the learning plan provides him with a focus on math and science materials, and also includes additional materials to strengthen his English foundations. Furthermore, as he progresses in his studies, the English materials are adjusted to gradually increase in difficulty, starting with more basic ones.

[0839] This allows the system to provide a personalized learning experience tailored to each learner's characteristics, maximizing learning effectiveness.

[0840] Example prompts for generative AI models

[0841] Please provide a detailed description of how your program automatically generates a personalized learning plan based on data from a specific learner. Also, please specify how the program adjusts content based on the learner's progress, and what hardware and software are required. Please provide an example of how your program works for a specific learner.

[0842] This prompt sentence serves as a guideline for the generative AI model to understand the specific processing content of the system and generate it accurately.

[0843] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0844] System program processing flow

[0845] Step 1: Data collection

[0846] The server collects data on learners' learning history, interests, and strengths from multiple databases and online learning platforms. Input data is obtained from the learning platform API and internal databases. This data includes the learning materials viewed by the learner, test results, study time, etc. The server obtains this data and stores it in an initial database. The output is the collected learning data. Specifically, for example, learner A's test scores are obtained from the online learning platform.

[0847] Step 2: Data Preprocessing

[0848] The server performs data preprocessing based on the collected data. The input is the raw data collected in step 1, and the output is the clean data after preprocessing. Data preprocessing includes tasks such as imputing missing values, removing outliers, and standardizing. For example, the missing learning time data for learner A is imputed with the average value.

[0849] Step 3: Data analysis

[0850] The server analyzes the preprocessed data using a machine learning algorithm. The input is the preprocessed data, and the output is the analysis results that show the learner's learning patterns. Clustering and classification algorithms are used for the analysis. Specifically, from the data of Learner A, it is determined that he is strong in mathematics but weak in English.

[0851] Step 4: Generate a lesson plan

[0852] Based on the analysis results, the server generates an optimized learning plan for each learner. The input is the analysis results, and the output is the generated learning plan. The learning plan includes learning progress, curriculum, and schedule. For example, for Learner A, a plan is generated that strengthens math and science materials while also strengthening the basics of English.

[0853] Step 5: Provide teaching materials

[0854] The server selects appropriate learning material links based on the generated learning plan and sends them to the terminal. The input is the learning plan, and the output is the learning material links. Specifically, it provides links to online lectures and practice questions to Learner A.

[0855] Step 6: Implementing the learning activity

[0856] Users access learning materials provided by the server through their terminals and perform learning activities. The input is the link to the learning materials provided by the server, and the output is learning progress data. Learning activities include viewing learning materials, taking tests, and answering practice problems. For example, Learner A answers the provided math practice problems.

[0857] Step 7: Track your progress

[0858] The device collects learner activity data in real time and sends it to the server. The input is the learner's activity data, and the output is progress data. Specifically, data such as the study time and correct answer rate of Learner A is collected.

[0859] Step 8: Analyze progress and adjust content

[0860] The server analyzes the received progress data and redesigns and adjusts the learning plan and materials as necessary. The input is progress data, and the output is adjusted learning plans and materials. Analysis includes evaluating learning effectiveness and adding content to strengthen weak points. For example, if Learner A is struggling with English, additional materials at the basic level will be provided.

[0861] Through this series of processes, the system can provide a personalized learning experience tailored to the individual needs of each learner, maximizing learning effectiveness.

[0862] (Application example 1)

[0863] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0864] Conventional learning support systems have proven effective in providing personalized learning experiences based on the individual characteristics of each learner. However, when it comes to shopping experiences in brick-and-mortar stores, there is a need for personalization based on individual customers' purchasing histories and interests, and the technology to achieve this in real time has not yet been fully established. Therefore, there is a need for a system that can instantly grasp customers' movements and interests within a store and provide them with product information tailored to their needs.

[0865] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0866] In this invention, the server includes means for collecting a learner's learning history, interests, and strengths, means for analyzing the collected learning history, interests, and strengths to identify a learning pattern, means for generating a personalized learning plan based on the identified learning pattern, means for providing appropriate learning materials based on the generated learning plan, means for tracking the learner's progress in real time, means for adjusting the learning plan and learning materials based on the tracked progress, means for collecting a purchase history, interests, and preferences, means for analyzing the collected purchase history, interests, and preferences to identify a purchasing pattern, means for generating a personalized product recommendation list based on the identified purchasing pattern, means for providing appropriate product information based on the generated product recommendation list, means for tracking a customer's movement within a store in real time, and means for adjusting the product information based on the tracked movement status. This makes it possible to provide optimal learning materials and product information according to individual characteristics and interests in both learning and shopping experiences.

[0867] - "Learning history" refers to the overall record of a learner's past learning activities, including the materials viewed, test results, study time, etc.

[0868] "Interest" refers to the degree of interest or attention a learner has in a particular field or topic.

[0869] "Strengths" refer to areas or subjects in which a learner excels, including specific skills and areas of knowledge.

[0870] "Analysis" is the process of identifying specific patterns or characteristics from collected data.

[0871] "Personalization" refers to providing experiences and content that are optimized to the characteristics and preferences of individual learners or customers.

[0872] A "learning plan" refers to a plan for learning progress based on the characteristics and needs of the learner, and includes a curriculum and learning schedule.

[0873] "Instructional materials" refers to resources such as textbooks, online lectures, and exercises that learners use to advance their learning.

[0874] "Progress" refers to the current state of a learner's learning, including the amount of time spent studying and the degree of completion of tasks.

[0875] "Purchase history" refers to a record of a customer's past purchasing activities, including the products purchased, the time of purchase, and the frequency of purchases.

[0876] "Preferences" refer to the preferences that customers have for specific products or categories.

[0877] A "product recommendation list" is a list of products suggested to a customer based on analyzed purchasing patterns.

[0878] "Product Information" refers to detailed information about a product, including price, features, and reviews.

[0879] "In-store movement" refers to the movement of customers as they move around the store, including browsing and purchasing products.

[0880] "Adjustment" refers to updating plans and content in accordance with progress and circumstances, and keeping them in the optimal state.

[0881] This invention is a system that provides a personalized experience according to the individual characteristics and interests of learners and customers. It utilizes servers and terminals to optimize learning support for learners and the shopping experience in physical stores.

[0882] Server Operation and Configuration

[0883] The server includes the following means:

[0884] 1. Data collection method: The server collects learners' or customers' past behavioral data (learning history, purchasing history, interests, and strengths) from multiple databases and online platforms. Learning history includes test results and study time, and purchasing history includes purchase dates and times and purchased products.

[0885] 2. Data analysis method: The collected data is preprocessed and analyzed using machine learning algorithms. During the analysis process, programming languages ​​such as Python and libraries such as scikit-learn and Pandas are used to identify and identify users' learning and purchasing patterns.

[0886] 3. Personalized plan generation: Based on the analysis results, a personalized learning plan or product recommendation list is generated for each user. The learning plan includes a curriculum and a learning schedule.

[0887] 4. Providing appropriate educational materials and product information: Based on the generated plan, the most suitable educational materials and product information are provided to the user, including educational material lists and product offers.

[0888] 5. Progress and movement tracking: Learners' learning progress and customers' movements within the store are tracked in real time. IoT devices and GPS tracking are used for real-time tracking.

[0889] 6. Plan and information adjustment: Automatically adjust learning plans and content offerings based on tracking data, including adding more basic learning materials and updating product information to better suit customer preferences.

[0890] Terminal operation and configuration

[0891] The terminal works as follows:

[0892] 1. Data display means: Links to educational materials and product information provided by the server are displayed to the user. Specifically, this is done through a smartphone application or the display of smart glasses.

[0893] 2. Data collection method: Collects user operation and behavior data and sends it to the server in real time.

[0894] Specific examples

[0895] Specific examples of learning

[0896] Learner A is good at math but weak at English. The server collects data on this learner and analyzes their characteristics. Based on the analysis results, the server generates a plan to provide advanced math learning materials and basic English learning materials. As Learner A progresses in his / her studies, the server tracks his / her progress in real time and adjusts the learning materials accordingly.

[0897] Shopping examples

[0898] Customer B has spent a lot of money on electronic devices in the past. The server analyzes this customer's purchasing history and generates a list of products suitable for Customer B. As Customer B moves through the store, the server tracks his movements in real time and provides specific product information in the relevant product section.

[0899] Prompt Sentence Examples

[0900] Learner prompt:

[0901] "Please provide me with additional basic learning materials to enhance my English studies."

[0902] Shopping customer prompt:

[0903] "Can you recommend the latest electronic gadgets based on your recent purchasing history?"

[0904] The system allows learners and customers to enjoy an experience that is tailored to their individual characteristics and interests.

[0905] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0906] Step 1:

[0907] Data collection

[0908] The server collects learner or customer data from existing databases and online platforms.

[0909] Input: Learning history, interests, strengths, purchasing history, preference data.

[0910] Output: The collected dataset.

[0911] Specific operation: The server retrieves user data from the database using an SQL query and saves it as a data frame.

[0912] Step 2:

[0913] Data Preprocessing

[0914] The server preprocesses the collected data and formats it into a form suitable for analysis.

[0915] Input: The collected dataset.

[0916] Output: The preprocessed dataset.

[0917] Specific operation: The server performs preprocessing such as imputing missing values, scaling, and encoding categorical data.

[0918] Step 3:

[0919] Data analysis

[0920] The server uses the pre-processed data and analyzes it with machine learning algorithms to identify user patterns.

[0921] Input: The preprocessed dataset.

[0922] Output: Learning patterns and purchasing patterns.

[0923] Specific operation: The server applies PCA (principal component analysis) and clustering algorithms (such as KMeans) to extract specific patterns.

[0924] Step 4:

[0925] Personalized Plan Generation

[0926] Based on the analysis results, the server generates a personalized study plan or product recommendation list for each user.

[0927] Input: Learning patterns and purchasing patterns.

[0928] Output: A personalized study plan or product list.

[0929] How it works: The server uses a specific algorithm to generate a plan or list optimized for each user, and then registers it in the user profile.

[0930] Step 5:

[0931] Providing appropriate educational materials and product information

[0932] The server provides the user with appropriate educational materials and product information based on the generated plan.

[0933] Input: A personalized study plan or product list.

[0934] Output: Teaching material links, product information.

[0935] Specific operation: The server generates links and information and sends them to the user's device.

[0936] Step 6:

[0937] Progress and movement tracking

[0938] The terminal tracks the user's learning and shopping progress in real time and transmits the data to a server.

[0939] Input: Learning progress data, in-store movement data.

[0940] Output: Real-time tracking data.

[0941] Specific operation: The device collects data using sensors (such as eye tracking in smart glasses or GPS in a smartphone) and sends it to a server.

[0942] Step 7:

[0943] Planning and Information Coordination

[0944] The server adjusts the lesson plan and product information based on the tracking data.

[0945] Input: Real-time tracking data.

[0946] Output: Tailored lesson plan, product list.

[0947] Specific operation: The server analyzes the collected data, regenerates appropriate content and schedules, and provides them to the user.

[0948] As described above, this system provides an optimized experience based on the characteristics of learners and customers. However, it is essential that the server performs appropriate processing at each step and updates and provides information in real time in cooperation with the device.

[0949] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0950] This invention is a system for providing a personalized learning experience according to the characteristics of each learner, and also combines it with an emotion engine that recognizes the user's emotions. This system is composed of a server, a terminal, a learner (user), and an emotion engine.

[0951] Server Operation

[0952] The server performs the following sequence of actions:

[0953] 1. Data Collection: The server collects data about learners' learning history, interests, and strengths from multiple databases and online learning platforms, including the learning materials they have viewed, test results, and study time.

[0954] 2. Emotion data collection: The server uses an emotion engine to collect the learner's emotional data, which is obtained from facial expressions, voice tones, pulse rates, etc.

[0955] 3. Data Analysis: Based on the collected learning data and emotional data, the server uses machine learning algorithms to preprocess the data and analyze the learner's learning patterns. For example, it identifies which subjects the learner is strong in, which subjects they are interested in, and which subjects they struggle with. It also evaluates the learner's motivation and stress level during learning based on emotional data.

[0956] 4. Learning plan generation: Based on the analysis results, the server automatically generates an optimized learning plan for each learner. This plan includes a specific curriculum and learning schedule designed to strengthen the learner's strengths and address their weaknesses. Based on emotional data, the server also suggests learning plans for times when students are least stressed.

[0957] 5. Providing learning materials: Based on the generated learning plan, the server selects appropriate learning materials and sends links to the learner's device, such as online lectures, exercises, and simulation materials for a specific unit.

[0958] 6. Progress Tracking: As learners progress through their studies, their devices transmit real-time learning progress data to the server, including study time, correct answer rate, and learning material browsing history.

[0959] 7. Emotional Data Collection: Collect emotional data in real time using an emotion engine, along with the learner's progress, for example, by analyzing facial expressions and vocal tones during learning.

[0960] 8. Content Adjustment: The server analyzes the received progress and emotion data to evaluate the learner's progress and learning effectiveness. If necessary, it automatically adjusts the learning plan and materials. For example, if the learner is tired or stressed, it provides more relaxing materials and methods.

[0961] Device behavior

[0962] The terminal works as follows:

[0963] 1. Displaying learning materials: Receives the link to the learning materials sent from the server and displays it so that learners can access it. For example, the learning materials are provided through a web browser or a dedicated learning application.

[0964] 2. Learning data collection: Learners' learning activities are tracked in real time and the data is sent to the server. This data includes information such as which learning materials the learner studied, how long they spent studying them, and the percentage of questions they answered correctly.

[0965] 3. Emotion data transmission: The emotion engine transmits the collected emotion data to the server in real time.

[0966] Specific examples

[0967] As a concrete example, consider the case of Learner A. Learner A is good at math and has an interest in science, but feels that he or she is not good at English. The server collects data on Learner A and identifies these characteristics. As a result, the learning plan provides Learner A with a focus on math and science materials, and also includes additional materials to strengthen his or her English foundation. Furthermore, if the emotion engine detects stress while Learner A is studying, it immediately adjusts the learning plan and inserts short breaks and relaxation materials to help him or her relax. In this way, the system can provide a personalized learning experience that is tailored to the learner's characteristics and emotions, maximizing learning effectiveness.

[0968] The processing flow will be explained below.

[0969] Step 1:

[0970] The server collects the learner's learning history, interests, and strengths. Specifically, it accesses the online learning platform and the school's academic performance database and retrieves data such as the learner's past learning activities, test results, and learning materials used based on the learner's login information.

[0971] Step 2:

[0972] The server collects emotional data using an emotion engine. Specifically, it monitors the learner's facial expressions, voice tone, pulse rate, etc., and records them as emotional data. For example, a learner may use a webcam to analyze changes in their facial expressions in real time.

[0973] Step 3:

[0974] The server preprocesses the collected learning and sentiment data, cleansing the data, filling in missing data, and integrating learner profiles, such as standardizing the format of test results and organizing learner interests into categories.

[0975] Step 4:

[0976] The server analyzes the data using machine learning algorithms, such as cluster analysis and pattern recognition, to identify learners' learning patterns, strengths (e.g., math), interests (e.g., science), and weaknesses (e.g., English), and also assesses their stress levels and motivation during learning based on emotional data.

[0977] Step 5:

[0978] The server then generates a personalized learning plan based on the analysis results, designing a specific curriculum and study schedule, such as providing high-intensity learning at times when stress is low and lighter learning materials when relaxation is needed.

[0979] Step 6:

[0980] The server selects appropriate learning materials based on the generated learning plan and sends them to the learner's device. For example, it selects online lectures, exercises, simulation materials, etc. for a specific unit and generates links to them.

[0981] Step 7:

[0982] The terminal receives the learning material link sent from the server and displays it to the learner, who then uses it to begin the instructed learning activity, for example, by viewing the learning material through a dedicated learning application or a web browser.

[0983] Step 8:

[0984] Users (learners) use their devices to carry out learning activities according to the provided learning materials, such as watching online lectures, answering exercises, and recording their results.

[0985] Step 9:

[0986] The device tracks the learner's progress in real time and sends it to the server, specifically recording the study time, answer results, and learning material browsing history, and uploading it immediately.

[0987] Step 10:

[0988] At the same time, the device uses an emotion engine to collect emotional data and transmit it to the server in real time, for example, analyzing facial expressions and voice tones during learning.

[0989] Step 11:

[0990] The server analyzes the received progress and emotion data to assess the learner's progress and learning effectiveness, and automatically adjusts the learning plan and materials as needed. For example, if the learner is feeling stressed, it will insert short breaks or relaxation materials to help them relax.

[0991] Step 12:

[0992] The server then sends the updated learning plan and materials back to the device to coordinate the learner's next learning experience, and the learner again accesses the new plan and materials through the device to continue their learning.

[0993] Example 2

[0994] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0995] Traditional learning systems provide personalized learning plans based only on a learner's learning history, interests, and strengths, but do not take into account the learner's emotional state or stress level, making it difficult to achieve optimal learning results. It is also difficult to easily track progress and adjust learning plans in real time.

[0996] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0997] In this invention, the server includes means for collecting a learner's learning history, interests, and strengths, means for collecting emotional data during learning, means for analyzing the collected learning history, interests, strengths, and emotional data to identify a learning pattern and an emotional state, means for generating a personalized learning plan based on the identified learning pattern and emotional state, means for providing appropriate learning materials based on the generated learning plan, means for tracking the learner's progress and emotional data during learning in real time, and means for adjusting the learning plan and learning materials based on the tracked progress and emotional data. This makes it possible to provide an optimal learning experience that takes into account the learner's individual characteristics and emotional state.

[0998] "Learning history" refers to a record of all of a learner's past learning activities, including the materials studied, test results, and study time.

[0999] "Interests" refer to areas or subjects that a learner is interested in and wants to learn about, such as a particular field of science or history.

[1000] "Strengths" refer to areas or subjects in which a learner is particularly good, and are assessed based on test results and learning outcomes.

[1001] "Emotional data" refers to information about the emotions of learners obtained from facial expressions, vocal tones, pulse rates, etc., displayed during learning. For example, happiness, sadness, stress, etc.

[1002] "Analysis" refers to the process of analyzing collected data using statistical methods and machine learning algorithms to extract meaningful information.

[1003] A "learning pattern" refers to a set of tendencies and behaviors identified based on a learner's learning history, interests, and strengths, including which subjects they frequently study and how they progress through their studies.

[1004] "Emotional state" refers to the emotions a learner is feeling at a particular time, including their motivation and stress levels while learning.

[1005] A "personalized learning plan" refers to a learning schedule and curriculum that is optimized based on the learner's individual characteristics and emotional state.

[1006] "Learning materials" refers to the materials and content that learners use to carry out learning activities, such as textbooks, video lectures, and exercises.

[1007] "Progress" refers to information that shows where a learner is at and how they are progressing in their studies, including the amount of time spent studying and the percentage of questions answered correctly.

[1008] "Real-time tracking" refers to the process of instantly monitoring learner activity and instantly collecting and analyzing that data.

[1009] "Adjustment" refers to the process of modifying or optimizing existing learning plans and materials based on the learner's progress and emotional state.

[1010] This invention is a system that provides a personalized learning experience according to the characteristics of each learner, and further combines it with an emotion engine that recognizes the user's emotions. This system is composed of a server, a terminal, a learner (user), and an emotion engine.

[1011] Server Operation

[1012] The server collects learners' learning history, interests, and strengths from multiple databases and online learning platforms. Specific platforms used include online learning platforms and databases. Collected data includes the learning materials viewed by the learner, test results, and study time. It also uses an emotion engine to collect learners' emotional data. Emotional data is obtained from facial expressions, voice tone, pulse rate, and other factors while studying. For this purpose, emotion recognition software and sensors are used.

[1013] The server preprocesses and analyzes the collected learning data and emotional data using machine learning algorithms (e.g., TensorFlow). This analysis identifies the learner's learning patterns and emotional state. For example, it identifies which subjects the learner is strong in, which subjects they are interested in, and which subjects they are weak in. It also evaluates the learner's motivation and stress level during learning based on the emotional data.

[1014] Based on the analysis results, the server automatically generates an optimized learning plan for each learner. This plan includes a curriculum and learning schedule designed to strengthen the learner's strengths and shore up their weaknesses. It also suggests learning plans based on emotional data and timelines when students are least stressed. Based on the generated learning plan, the server selects appropriate learning materials and sends links to the learner's device. For example, online lectures, practice questions, and simulation materials for specific units are provided.

[1015] Device behavior

[1016] The device receives the learning material link sent from the server and displays it so that the learner can access it. Specifically, the learning material is provided using a web browser or a dedicated learning application. The device also tracks the learner's learning activities in real time and sends the data to the server. This data includes information such as how much of each learning material the learner has studied and the percentage of questions they have answered correctly. The device's emotion engine collects emotional data such as facial expressions and tone of voice during learning and sends this data to the server in real time.

[1017] Content Adjustment

[1018] The server analyzes the user's progress and emotional data and automatically adjusts the learning plan and materials as needed, for example, suggesting more relaxing materials or breaks if the learner is fatigued or stressed.

[1019] Specific examples

[1020] For example, suppose Learner A is good at math and interested in science, but feels that he or she is not good at English. The server collects data on Learner A and identifies these characteristics. As a result, the learning plan provides a focus on math and science materials, and includes additional materials to strengthen the foundations of English. Also, if the emotion engine detects stress while Learner A is studying, the learning plan will be immediately adjusted to include short breaks or videos for relaxation.

[1021] Prompt Sentence Examples

[1022] Examples of prompts to input into a generative AI model include:

[1023] "If Learner A is feeling stressed, how can I provide learning materials that will help him relax?"

[1024] In this way, this system can provide a personalized learning experience that is tailored to the learner's characteristics and emotions, maximizing learning effectiveness.

[1025] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1026] Step 1: Data collection

[1027] The server collects data on learners' learning history, interests, and strengths from multiple databases and online learning platforms. Specifically, it uses APIs to obtain data such as learning material browsing history, test results, and study time from the learning platforms. The input is the raw data obtained from the API, and the output is the collected learning dataset.

[1028] Step 2: Collecting Emotional Data

[1029] Once the user starts learning, the device's emotion engine collects emotion data using a facial recognition camera, microphone, pulse sensor, etc. Specifically, emotion recognition software is used to analyze the user's facial expressions in real time. The input is raw sensor data, and the output is analyzed emotion data. This data is sent to the server in real time.

[1030] Step 3: Data Preprocessing

[1031] The server uses statistical analysis tools and machine learning algorithms to perform preprocessing based on the collected training data and emotion data. Specifically, it complements missing data and corrects outliers. The input is the collected raw data, and the output is the preprocessed data.

[1032] Step 4: Data analysis

[1033] The server analyzes the preprocessed data and identifies the learner's learning patterns and emotional state. It uses machine learning algorithms (e.g., TensorFlow) to identify which subjects the learner has strengths and interests in, as well as other learning characteristics. The input is the preprocessed dataset, and the output is the analysis results.

[1034] Step 5: Generate a lesson plan

[1035] Based on the analysis results, the server generates an optimized learning plan for each learner. This plan includes a specific curriculum and study schedule to utilize the learner's strengths and overcome their weaknesses. It also reflects emotional data to create a study schedule for times when students are least stressed. The input is the analyzed learning patterns and emotional data, and the output is a personalized learning plan.

[1036] Step 6: Provide teaching materials

[1037] The server selects appropriate learning materials based on the generated learning plan and sends links to the learner's device. For example, it provides online lectures and exercises for a specific unit. The input is the learning plan, and the output is the learning material links.

[1038] Step 7: Track progress

[1039] The device sends real-time progress data to the server as the learner progresses through the study, including study time, percentage of correct answers to questions, and materials viewed. The input is the learner's behavioral data, and the output is updated progress data.

[1040] Step 8: Collect emotional data

[1041] The device's emotion engine analyzes the user's facial expressions and tone of voice during training and sends the emotion data to the server. The input is emotion data collected in real time, and the output is the analyzed emotion data.

[1042] Step 9: Content Adjustment

[1043] The server evaluates the learner's progress and learning effectiveness based on the received progress and emotion data, and adjusts the learning plan and learning materials as needed. For example, if the learner is fatigued, it provides relaxation materials. The input is the updated progress and emotion data, and the output is the adjusted learning plan and learning materials.

[1044] (Application example 2)

[1045] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1046] Conventional learning and sales systems have struggled to provide personalized services based on the characteristics and emotions of individual learners and customers. This has led to problems such as lower customer satisfaction during learning and purchasing, and a lack of appropriate product suggestions. As a result, the effectiveness of learning and purchasing experiences are not maximized, and users' expectations cannot be met.

[1047] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1048] In this invention, the server includes means for collecting a learner's learning history, interests, and strengths, means for analyzing the collected learning history, interests, and strengths to identify a learning pattern, means for generating a personalized learning plan based on the identified learning pattern, means for providing appropriate learning materials based on the generated learning plan, means for tracking the learner's progress in real time, means for adjusting the learning plan and learning materials based on the tracked progress, means for selecting products based on the customer's purchasing history and interests, means for collecting and analyzing customer emotional data at the time of purchase, and means for providing personalized product suggestions and services based on the analyzed emotional data. This makes it possible to provide a personalized learning experience and purchasing experience according to the characteristics and emotions of each learner or customer, maximizing learning effectiveness and improving customer satisfaction.

[1049] "Learning history" is a record of a learner's past learning activities and course status.

[1050] "Interest" is the state in which a learner or customer has interest in a particular field or product.

[1051] "Strengths" are subjects or skills that a learner excels at, or characteristics of a product that make it easy for customers to buy.

[1052] A "learning pattern" is a tendency or characteristic that is derived from an analysis of a learner's learning behavior and learning history.

[1053] A "personalized learning plan" is a learning schedule and curriculum that is optimized according to the characteristics of each individual learner.

[1054] "Learning Materials" refers to learning content and resources provided to learners, including online lectures, exercises, and simulation materials.

[1055] "Progress" refers to the status or assessment that indicates how far a learner has progressed in their studies.

[1056] "Purchase history" is a record of products and services that a customer has purchased in the past.

[1057] "Emotional data" is data that represents the emotional state of a customer or learner, and is obtained from facial expressions, tone of voice, pulse rate, etc.

[1058] A "product suggestion" is a recommendation of a specific product or service to a customer to encourage them to purchase it.

[1059] "Services" are value-added activities and support provided to Customers.

[1060] A "machine learning algorithm" is a computational method used to analyze large amounts of data and extract patterns and features.

[1061] "Analysis" is the process of extracting meaningful information from collected data.

[1062] The present invention provides a system for providing a personalized experience according to the characteristics and emotional state of individual learners and customers. The system includes a server, a terminal, a user, and an emotion engine.

[1063] Server Operation

[1064] 1. User Identification and Login

[1065] The server authenticates the user using the user ID, sends a request to the API server, and retrieves the user information after authentication.

[1066] 2. Data Collection

[1067] The server collects data about learners' learning history, interests, and strengths, as well as customer purchasing history and interests. This information is collected automatically from multiple databases and online platforms.

[1068] 3. Emotional Data Analysis

[1069] The server analyzes audio and video data in real time using an emotion engine (e.g., EmotionEngine library) to acquire and analyze emotional data of customers and learners.

[1070] 4. Generating personalized suggestions

[1071] The server uses machine learning algorithms to generate personalized learning plans and product recommendations based on the collected data and analyzed emotional data.

[1072] 5. Real-time feedback

[1073] The server sends the generated learning plans and product suggestions to the learner's or customer's device in real time, enabling dynamic adjustments based on the user's progress and emotions.

[1074] Device behavior

[1075] 1. Display of educational materials or product suggestions

[1076] The terminal receives links to educational materials and product suggestions sent from the server and displays them so that the user can access them. For this purpose, a web browser or a dedicated application is used.

[1077] 2. Learning and purchasing data collection

[1078] The device tracks data in real time as users study materials or view products and transmits it to a server.

[1079] 3. Emotional Data Transmission

[1080] The device is equipped with an emotion engine that collects emotional data from the user's facial expressions and voice tone during training and transmits it to the server in real time.

[1081] Specific examples

[1082] For example, suppose Customer A visits a physical store and logs in to a dedicated app. Their past purchase history reveals that they are interested in health foods, and the app also analyzes their facial expressions and tone of voice to sense fatigue. In this case, the server suggests products with a relaxing effect and sends this information to Customer A's device in real time.

[1083] In this way, the system, which is composed of a server, a terminal, a user, and an emotion engine, provides a personalized experience according to individual characteristics and emotions, solving the conventional problem.

[1084] Prompt Sentence Examples

[1085] Develop a strategy for recommending specific products to customers based on their past purchase history and current emotional data. For example, if a customer is feeling stressed, suggest products that will help them relax.

[1086] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1087] Step 1:

[1088] User Awareness and Login

[1089] The server receives the user ID and sends a request to the authentication API server. The input is the user ID, and the output is the authenticated user information. This information includes past learning history, purchase history, interests, etc. The server stores this information and prepares it for data collection in the next step.

[1090] Step 2:

[1091] Data collection

[1092] The server retrieves data about the target user's past learning history, interests, and strengths, as well as customer purchasing history and interests, from databases and online platforms. In this step, the server accesses various databases to obtain and aggregate information about each user. The input is the user ID and authentication information, and the output is all associated historical information.

[1093] Step 3:

[1094] Emotional Data Analysis

[1095] The device used by the user captures the user's voice and video data in real time and analyzes it using an emotion engine. The server receives the analysis results and identifies the user's emotional state. The input is voice and video data, and the output is emotional data (e.g., stress level, enjoyment, tiredness, etc.). This emotional data is processed by the emotion engine.

[1096] Step 4:

[1097] Generate personalized suggestions

[1098] The server uses a machine learning algorithm to generate personalized learning plans and product suggestions based on the collected data (learning history, purchasing history, interests, strengths) and emotional data. The input here is all of the above data (learning history, interests, strengths, emotional data), and the output is individually optimized learning plans and product suggestions. The specific suggestions are generated using a generative AI model.

[1099] Step 5:

[1100] Real-time feedback

[1101] The server sends the generated learning plan and product suggestions to the user's device in real time. The device receives them and visually presents them to the user. The input is the generated optimization plan, and the output is the suggestions or plan displayed on the user's device. At this point, the user's learning and purchasing progress is tracked and analyzed again and adjusted as necessary.

[1102] Step 6:

[1103] Collecting progress and sentiment data

[1104] The device tracks the user's learning or purchasing progress in real time and sends new data to the server, including study time, accuracy rate of questions answered, purchase history, information about products viewed, and emotional changes during learning or purchasing. The input is the user's real-time activity data, and the output is a new dataset for analysis.

[1105] Step 7:

[1106] Coordination of plans and proposals

[1107] The server reevaluates the learning plan and product recommendations based on progress and new emotional data, making adjustments as needed. The input is real-time collected progress and emotional data, and the output is a re-adjusted personalized plan, ensuring that users always receive an optimized experience.

[1108] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1109] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1110] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1111] [Fourth embodiment]

[1112] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1113] 7, a 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.

[1114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1115] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1116] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1119] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1120] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1121] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1123] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1124] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1125] The present invention is a system for providing a personalized learning experience according to the characteristics of each learner. The system includes a server, a terminal, and a learner (user).

[1126] Server Operation

[1127] The server performs the following sequence of actions:

[1128] 1. Data Collection: The server collects data about learners' learning history, interests, and strengths from multiple databases and online learning platforms, including the learning materials they have viewed, test results, and study time.

[1129] 2. Data analysis: Based on the collected data, the server uses machine learning algorithms to preprocess the data and analyze the learner's learning patterns, for example, identifying which subjects the learner has strengths, interests, or weaknesses in.

[1130] 3. Learning plan generation: Based on the analysis results, the server automatically generates an optimized learning plan for each learner, including a curriculum and learning schedule to further develop the learner's strengths and strengthen their weaknesses.

[1131] 4. Providing learning materials: Based on the generated learning plan, the server selects appropriate learning materials and sends links to the learner's device, such as online lectures, exercises, and simulation materials for a specific unit.

[1132] 5. Progress Tracking: As learners progress through their studies, their devices transmit real-time learning progress data to the server, including study time, correct answer rate, and learning material browsing history.

[1133] 6. Content Adjustment: The server analyzes the received progress data, evaluates the learner's progress and learning effectiveness, and automatically adjusts the learning plan and materials as needed. For example, if a learner is struggling with English, the server may provide additional, more basic learning materials.

[1134] Device behavior

[1135] The terminal works as follows:

[1136] 1. Displaying learning materials: Receives the link to the learning materials sent from the server and displays it so that learners can access it. For example, the learning materials are provided through a web browser or a dedicated learning application.

[1137] 2. Learning data collection: Learners' learning activities are tracked in real time and the data is sent to the server. This data includes information such as which learning materials the learner studied, how long they spent studying them, and the percentage of questions they answered correctly.

[1138] Specific examples

[1139] As a concrete example, let's take the case of Learner A. Learner A is good at math and has an interest in science, but feels that he or she is not good at English. The server collects data on Learner A and identifies these characteristics. As a result, the learning plan provides an emphasis on providing math and science materials, and also includes additional materials to strengthen the learner's English basics. Furthermore, as the learner progresses, the English materials are adjusted to gradually increase in difficulty, starting with more basic materials. In this way, the system can provide a personalized learning experience that is tailored to the learner's characteristics, maximizing learning effectiveness.

[1140] The processing flow will be explained below.

[1141] Step 1:

[1142] The server collects the learner's learning history, interests, and strengths. Specifically, it accesses the online learning platform and the school's academic performance database and retrieves data such as the learner's past learning activities, test results, and learning materials used based on the learner's login information.

[1143] Step 2:

[1144] The server preprocesses the collected data, cleansing it, filling in missing data, and integrating learner profiles, such as standardizing the format of test results and organizing learner interests into categories.

[1145] Step 3:

[1146] The server analyzes the data using machine learning algorithms, specifically cluster analysis and pattern recognition, to identify learners' learning patterns, strengths (e.g., math), interests (e.g., science), and weaknesses (e.g., English), thereby identifying the characteristics and trends of individual learners.

[1147] Step 4:

[1148] The server then generates a personalized learning plan based on the analysis, designing a specific curriculum and study schedule to strengthen the learner's strengths and address their weaknesses, for example, providing additional applied math problems each week and setting up special sessions to strengthen the learner's English fundamentals.

[1149] Step 5:

[1150] The server selects appropriate learning materials based on the generated learning plan and sends them to the learner's device. Specifically, it selects online learning materials, practice questions, simulation tools, etc. and generates links to them.

[1151] Step 6:

[1152] The terminal receives the learning material link sent from the server and displays it to the learner, who then uses it to begin the instructed learning activity, for example, by viewing the learning material through a dedicated learning application or a web browser.

[1153] Step 7:

[1154] Users (learners) use their devices to carry out learning activities according to the provided learning materials, such as watching online lectures, answering exercises, and recording their results.

[1155] Step 8:

[1156] The device tracks the learner's progress in real time and sends it to the server, specifically recording the study time, answer results, and learning material browsing history, and uploading it immediately.

[1157] Step 9:

[1158] The server analyzes the received progress data and evaluates the learner's progress and learning effectiveness, including learning speed, correct answer rate, and duration of learning.

[1159] Step 10:

[1160] The server adjusts the learning plan and materials based on the progress data. For example, if a learner is struggling with English material, it can add easier questions or provide supplementary materials, helping the learner reach their learning goals more effectively.

[1161] Example 1

[1162] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1163] There is a demand for providing a personalized learning experience tailored to each learner's characteristics in order to maximize learning effectiveness. However, conventional systems only collect data on learning history, interests, and strengths, but are unable to effectively utilize this information in learning plans and providing learning materials. Furthermore, there is a lack of means to track learners' progress in real time and adjust learning plans and materials as needed. As a result, it is difficult to provide optimal learning plans tailored to each learner's individual needs.

[1164] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1165] In this invention, the server includes means for collecting the learning history, interests, and strengths of a learner, means for analyzing the collected learning history, interests, and strengths to identify a learning pattern, means for generating a personalized learning plan based on the identified learning pattern, means for providing appropriate learning materials based on the generated learning plan, means for tracking the learner's progress in real time, means for adjusting the learning plan and learning materials based on the tracked progress, and means for evaluating each learner's progress and automatically redesigning and providing the learning materials and learning plan as necessary. This makes it possible to provide an optimal learning plan according to the individual needs of the learner and maximize the learning effect.

[1166] A "learning history" is a record of all learning activities that a learner has performed in the past, including information such as the learning materials viewed, study time, and test results.

[1167] "Interest" refers to the interest or curiosity a learner has in a particular field of study or subject.

[1168] "Strengths" refer to the outstanding abilities and achievements a learner has in a particular field or subject.

[1169] "Data collection means" refers to the functions and methods for collecting data from learners, such as their learning history, interests, and strengths.

[1170] "Data analysis means" refers to functions and methods for analyzing learners' learning patterns based on collected data.

[1171] "Learning patterns" refer to the characteristics and tendencies that indicate the type of learning behavior a learner will exhibit.

[1172] A "learning plan" refers to an optimal curriculum and schedule designed based on the learner's characteristics.

[1173] "Means for providing learning materials" refers to the functions and methods for delivering appropriate learning materials to learners based on their learning plans.

[1174] "Progress tracking means" refers to functions and methods for tracking the progress of learners' learning activities in real time.

[1175] "Progress" refers to information that indicates whether a learner is progressing according to their learning plan.

[1176] "Content adjustment means" refers to functions and methods for redesigning and adjusting learning plans and materials based on progress.

[1177] A "machine learning algorithm" refers to a computational method for automatically extracting and analyzing learning patterns from data.

[1178] "Evaluation tools" refer to functions and methods for analyzing learners' progress and judging the effectiveness of the learning content based on the results.

[1179] This invention is a system that provides a personalized learning experience tailored to the individual characteristics of each learner. This system includes a server, a terminal, and a learner (user). Specific embodiments of each element are described below.

[1180] Server Operation

[1181] The server performs the following series of operations to collect and analyze the learner's data and generate an individualized learning plan.

[1182] 1. Data Collection

[1183] The server collects data on the learner's learning history, interests, and strengths from multiple databases and online learning platforms, specifically by retrieving data from online learning platforms via APIs (e.g., online learning platform APIs) and querying internal databases.

[1184] 2. Data Analysis

[1185] Based on the collected data, the server uses machine learning algorithms to preprocess and analyze the data and identify the learner's learning patterns. Techniques such as data cleaning, feature engineering, and clustering are used. Examples of machine learning algorithms used include clustering and classification algorithms.

[1186] 3. Learning plan generation

[1187] Based on the analysis results, the server automatically generates an optimal learning plan for each learner. The learning plan includes learning progress, curriculum, and schedule. A rule-based engine and scheduler are used for automatic generation.

[1188] 4. Providing teaching materials

[1189] The server selects appropriate learning material links based on the generated learning plan and sends them to the terminal.A content management system is used to provide links to online lectures, exercises, simulation materials, etc.

[1190] 5. Progress Tracking

[1191] As the learner progresses with their studies, progress data is received in real time from the device, including study time, correct answer rate, and learning material viewing history. The progress data is automatically saved in a database and used for analysis.

[1192] 6. Content Adjustment

[1193] The server analyzes the received progress data and automatically redesigns and adjusts the learning plan and materials as needed, using machine learning models to adapt content to reinforce the learner's weaknesses.

[1194] Device behavior

[1195] The terminal is a device that receives the learning material link sent from the server and allows the user to carry out learning activities.

[1196] 1. Teaching material display

[1197] The device receives the link to the learning material sent from the server and displays it to the user in a browser or dedicated application. For example, software such as a web browser or a mobile application is used.

[1198] 2. Learning data collection

[1199] The device tracks learners' learning activity data in real time and sends it to a server. This data includes study time, correct answer rate, and learning material viewing history. A learning management system (LMS) or tracking software is used.

[1200] Specific examples

[1201] As a concrete example, consider the case of Learner A. Learner A is good at math and has an interest in science, but feels that he is not good at English. The server collects data on Learner A and identifies these characteristics. As a result, the learning plan provides him with a focus on math and science materials, and also includes additional materials to strengthen his English foundations. Furthermore, as he progresses in his studies, the English materials are adjusted to gradually increase in difficulty, starting with more basic ones.

[1202] This allows the system to provide a personalized learning experience tailored to each learner's characteristics, maximizing learning effectiveness.

[1203] Example prompts for generative AI models

[1204] Please provide a detailed description of how your program automatically generates a personalized learning plan based on data from a specific learner. Also, please specify how the program adjusts content based on the learner's progress, and what hardware and software are required. Please provide an example of how your program works for a specific learner.

[1205] This prompt sentence serves as a guideline for the generative AI model to understand the specific processing content of the system and generate it accurately.

[1206] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1207] System program processing flow

[1208] Step 1: Data collection

[1209] The server collects data on learners' learning history, interests, and strengths from multiple databases and online learning platforms. Input data is obtained from the learning platform API and internal databases. This data includes the learning materials viewed by the learner, test results, study time, etc. The server obtains this data and stores it in an initial database. The output is the collected learning data. Specifically, for example, learner A's test scores are obtained from the online learning platform.

[1210] Step 2: Data Preprocessing

[1211] The server performs data preprocessing based on the collected data. The input is the raw data collected in step 1, and the output is the clean data after preprocessing. Data preprocessing includes tasks such as imputing missing values, removing outliers, and standardizing. For example, the missing learning time data for learner A is imputed with the average value.

[1212] Step 3: Data analysis

[1213] The server analyzes the preprocessed data using a machine learning algorithm. The input is the preprocessed data, and the output is the analysis results that show the learner's learning patterns. Clustering and classification algorithms are used for the analysis. Specifically, from the data of Learner A, it is determined that he is strong in mathematics but weak in English.

[1214] Step 4: Generate a lesson plan

[1215] Based on the analysis results, the server generates an optimized learning plan for each learner. The input is the analysis results, and the output is the generated learning plan. The learning plan includes learning progress, curriculum, and schedule. For example, for Learner A, a plan is generated that strengthens math and science materials while also strengthening the basics of English.

[1216] Step 5: Provide teaching materials

[1217] The server selects appropriate learning material links based on the generated learning plan and sends them to the terminal. The input is the learning plan, and the output is the learning material links. Specifically, it provides links to online lectures and practice questions to Learner A.

[1218] Step 6: Implementing the learning activity

[1219] Users access learning materials provided by the server through their terminals and perform learning activities. The input is the link to the learning materials provided by the server, and the output is learning progress data. Learning activities include viewing learning materials, taking tests, and answering practice problems. For example, Learner A answers the provided math practice problems.

[1220] Step 7: Track your progress

[1221] The device collects learner activity data in real time and sends it to the server. The input is the learner's activity data, and the output is progress data. Specifically, data such as the study time and correct answer rate of Learner A is collected.

[1222] Step 8: Analyze progress and adjust content

[1223] The server analyzes the received progress data and redesigns and adjusts the learning plan and materials as necessary. The input is progress data, and the output is adjusted learning plans and materials. Analysis includes evaluating learning effectiveness and adding content to strengthen weak points. For example, if Learner A is struggling with English, additional materials at the basic level will be provided.

[1224] Through this series of processes, the system can provide a personalized learning experience tailored to the individual needs of each learner, maximizing learning effectiveness.

[1225] (Application example 1)

[1226] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1227] Conventional learning support systems have proven effective in providing personalized learning experiences based on the individual characteristics of each learner. However, when it comes to shopping experiences in brick-and-mortar stores, there is a need for personalization based on individual customers' purchasing histories and interests, and the technology to achieve this in real time has not yet been fully established. Therefore, there is a need for a system that can instantly grasp customers' movements and interests within a store and provide them with product information tailored to their needs.

[1228] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1229] In this invention, the server includes means for collecting a learner's learning history, interests, and strengths, means for analyzing the collected learning history, interests, and strengths to identify a learning pattern, means for generating a personalized learning plan based on the identified learning pattern, means for providing appropriate learning materials based on the generated learning plan, means for tracking the learner's progress in real time, means for adjusting the learning plan and learning materials based on the tracked progress, means for collecting a purchase history, interests, and preferences, means for analyzing the collected purchase history, interests, and preferences to identify a purchasing pattern, means for generating a personalized product recommendation list based on the identified purchasing pattern, means for providing appropriate product information based on the generated product recommendation list, means for tracking a customer's movement within a store in real time, and means for adjusting the product information based on the tracked movement status. This makes it possible to provide optimal learning materials and product information according to individual characteristics and interests in both learning and shopping experiences.

[1230] - "Learning history" refers to the overall record of a learner's past learning activities, including the materials viewed, test results, study time, etc.

[1231] "Interest" refers to the degree of interest or attention a learner has in a particular field or topic.

[1232] "Strengths" refer to areas or subjects in which a learner excels, including specific skills and areas of knowledge.

[1233] "Analysis" is the process of identifying specific patterns or characteristics from collected data.

[1234] "Personalization" refers to providing experiences and content that are optimized to the characteristics and preferences of individual learners or customers.

[1235] A "learning plan" refers to a plan for learning progress based on the characteristics and needs of the learner, and includes a curriculum and learning schedule.

[1236] "Instructional materials" refers to resources such as textbooks, online lectures, and exercises that learners use to advance their learning.

[1237] "Progress" refers to the current state of a learner's learning, including the amount of time spent studying and the degree of completion of tasks.

[1238] "Purchase history" refers to a record of a customer's past purchasing activities, including the products purchased, the time of purchase, and the frequency of purchases.

[1239] "Preferences" refer to the preferences that customers have for specific products or categories.

[1240] A "product recommendation list" is a list of products suggested to a customer based on analyzed purchasing patterns.

[1241] "Product Information" refers to detailed information about a product, including price, features, and reviews.

[1242] "In-store movement" refers to the movement of customers as they move around the store, including browsing and purchasing products.

[1243] "Adjustment" refers to updating plans and content in accordance with progress and circumstances, and keeping them in the optimal state.

[1244] This invention is a system that provides a personalized experience according to the individual characteristics and interests of learners and customers. It utilizes servers and terminals to optimize learning support for learners and the shopping experience in physical stores.

[1245] Server Operation and Configuration

[1246] The server includes the following means:

[1247] 1. Data collection method: The server collects learners' or customers' past behavioral data (learning history, purchasing history, interests, and strengths) from multiple databases and online platforms. Learning history includes test results and study time, and purchasing history includes purchase dates and times and purchased products.

[1248] 2. Data analysis method: The collected data is preprocessed and analyzed using machine learning algorithms. During the analysis process, programming languages ​​such as Python and libraries such as scikit-learn and Pandas are used to identify and identify users' learning and purchasing patterns.

[1249] 3. Personalized plan generation: Based on the analysis results, a personalized learning plan or product recommendation list is generated for each user. The learning plan includes a curriculum and a learning schedule.

[1250] 4. Providing appropriate educational materials and product information: Based on the generated plan, the most suitable educational materials and product information are provided to the user, including educational material lists and product offers.

[1251] 5. Progress and movement tracking: Learners' learning progress and customers' movements within the store are tracked in real time. IoT devices and GPS tracking are used for real-time tracking.

[1252] 6. Plan and information adjustment: Automatically adjust learning plans and content offerings based on tracking data, including adding more basic learning materials and updating product information to better suit customer preferences.

[1253] Terminal operation and configuration

[1254] The terminal works as follows:

[1255] 1. Data display means: Links to educational materials and product information provided by the server are displayed to the user. Specifically, this is done through a smartphone application or the display of smart glasses.

[1256] 2. Data collection method: Collects user operation and behavior data and sends it to the server in real time.

[1257] Specific examples

[1258] Specific examples of learning

[1259] Learner A is good at math but weak at English. The server collects data on this learner and analyzes their characteristics. Based on the analysis results, the server generates a plan to provide advanced math learning materials and basic English learning materials. As Learner A progresses in his / her studies, the server tracks his / her progress in real time and adjusts the learning materials accordingly.

[1260] Shopping examples

[1261] Customer B has spent a lot of money on electronic devices in the past. The server analyzes this customer's purchasing history and generates a list of products suitable for Customer B. As Customer B moves through the store, the server tracks his movements in real time and provides specific product information in the relevant product section.

[1262] Prompt Sentence Examples

[1263] Learner prompt:

[1264] "Please provide me with additional basic learning materials to enhance my English studies."

[1265] Shopping customer prompt:

[1266] "Can you recommend the latest electronic gadgets based on your recent purchasing history?"

[1267] The system allows learners and customers to enjoy an experience that is tailored to their individual characteristics and interests.

[1268] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1269] Step 1:

[1270] Data collection

[1271] The server collects learner or customer data from existing databases and online platforms.

[1272] Input: Learning history, interests, strengths, purchasing history, preference data.

[1273] Output: The collected dataset.

[1274] Specific operation: The server retrieves user data from the database using an SQL query and saves it as a data frame.

[1275] Step 2:

[1276] Data Preprocessing

[1277] The server preprocesses the collected data and formats it into a form suitable for analysis.

[1278] Input: The collected dataset.

[1279] Output: The preprocessed dataset.

[1280] Specific operation: The server performs preprocessing such as imputing missing values, scaling, and encoding categorical data.

[1281] Step 3:

[1282] Data analysis

[1283] The server uses the pre-processed data and analyzes it with machine learning algorithms to identify user patterns.

[1284] Input: The preprocessed dataset.

[1285] Output: Learning patterns and purchasing patterns.

[1286] Specific operation: The server applies PCA (principal component analysis) and clustering algorithms (such as KMeans) to extract specific patterns.

[1287] Step 4:

[1288] Personalized Plan Generation

[1289] Based on the analysis results, the server generates a personalized study plan or product recommendation list for each user.

[1290] Input: Learning patterns and purchasing patterns.

[1291] Output: A personalized study plan or product list.

[1292] How it works: The server uses a specific algorithm to generate a plan or list optimized for each user, and then registers it in the user profile.

[1293] Step 5:

[1294] Providing appropriate educational materials and product information

[1295] The server provides the user with appropriate educational materials and product information based on the generated plan.

[1296] Input: A personalized study plan or product list.

[1297] Output: Teaching material links, product information.

[1298] Specific operation: The server generates links and information and sends them to the user's device.

[1299] Step 6:

[1300] Progress and movement tracking

[1301] The terminal tracks the user's learning and shopping progress in real time and transmits the data to a server.

[1302] Input: Learning progress data, in-store movement data.

[1303] Output: Real-time tracking data.

[1304] Specific operation: The device collects data using sensors (such as eye tracking in smart glasses or GPS in a smartphone) and sends it to a server.

[1305] Step 7:

[1306] Planning and Information Coordination

[1307] The server adjusts the lesson plan and product information based on the tracking data.

[1308] Input: Real-time tracking data.

[1309] Output: Tailored lesson plan, product list.

[1310] Specific operation: The server analyzes the collected data, regenerates appropriate content and schedules, and provides them to the user.

[1311] As described above, this system provides an optimized experience based on the characteristics of learners and customers. However, it is essential that the server performs appropriate processing at each step and updates and provides information in real time in cooperation with the device.

[1312] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1313] This invention is a system for providing a personalized learning experience according to the characteristics of each learner, and also combines it with an emotion engine that recognizes the user's emotions. This system is composed of a server, a terminal, a learner (user), and an emotion engine.

[1314] Server Operation

[1315] The server performs the following sequence of actions:

[1316] 1. Data Collection: The server collects data about learners' learning history, interests, and strengths from multiple databases and online learning platforms, including the learning materials they have viewed, test results, and study time.

[1317] 2. Emotion data collection: The server uses an emotion engine to collect the learner's emotional data, which is obtained from facial expressions, voice tones, pulse rates, etc.

[1318] 3. Data Analysis: Based on the collected learning data and emotional data, the server uses machine learning algorithms to preprocess the data and analyze the learner's learning patterns. For example, it identifies which subjects the learner is strong in, which subjects they are interested in, and which subjects they struggle with. It also evaluates the learner's motivation and stress level during learning based on emotional data.

[1319] 4. Learning plan generation: Based on the analysis results, the server automatically generates an optimized learning plan for each learner. This plan includes a specific curriculum and learning schedule designed to strengthen the learner's strengths and address their weaknesses. Based on emotional data, the server also suggests learning plans for times when students are least stressed.

[1320] 5. Providing learning materials: Based on the generated learning plan, the server selects appropriate learning materials and sends links to the learner's device, such as online lectures, exercises, and simulation materials for a specific unit.

[1321] 6. Progress Tracking: As learners progress through their studies, their devices transmit real-time learning progress data to the server, including study time, correct answer rate, and learning material browsing history.

[1322] 7. Emotional Data Collection: Collect emotional data in real time using an emotion engine, along with the learner's progress, for example, by analyzing facial expressions and vocal tones during learning.

[1323] 8. Content Adjustment: The server analyzes the received progress and emotion data to evaluate the learner's progress and learning effectiveness. If necessary, it automatically adjusts the learning plan and materials. For example, if the learner is tired or stressed, it provides more relaxing materials and methods.

[1324] Device behavior

[1325] The terminal works as follows:

[1326] 1. Displaying learning materials: Receives the link to the learning materials sent from the server and displays it so that learners can access it. For example, the learning materials are provided through a web browser or a dedicated learning application.

[1327] 2. Learning data collection: Learners' learning activities are tracked in real time and the data is sent to the server. This data includes information such as which learning materials the learner studied, how long they spent studying them, and the percentage of questions they answered correctly.

[1328] 3. Emotion data transmission: The emotion engine transmits the collected emotion data to the server in real time.

[1329] Specific examples

[1330] As a concrete example, consider the case of Learner A. Learner A is good at math and has an interest in science, but feels that he or she is not good at English. The server collects data on Learner A and identifies these characteristics. As a result, the learning plan provides Learner A with a focus on math and science materials, and also includes additional materials to strengthen his or her English foundation. Furthermore, if the emotion engine detects stress while Learner A is studying, it immediately adjusts the learning plan and inserts short breaks and relaxation materials to help him or her relax. In this way, the system can provide a personalized learning experience that is tailored to the learner's characteristics and emotions, maximizing learning effectiveness.

[1331] The processing flow will be explained below.

[1332] Step 1:

[1333] The server collects the learner's learning history, interests, and strengths. Specifically, it accesses the online learning platform and the school's academic performance database and retrieves data such as the learner's past learning activities, test results, and learning materials used based on the learner's login information.

[1334] Step 2:

[1335] The server collects emotional data using an emotion engine. Specifically, it monitors the learner's facial expressions, voice tone, pulse rate, etc., and records them as emotional data. For example, a learner may use a webcam to analyze changes in their facial expressions in real time.

[1336] Step 3:

[1337] The server preprocesses the collected learning and sentiment data, cleansing the data, filling in missing data, and integrating learner profiles, such as standardizing the format of test results and organizing learner interests into categories.

[1338] Step 4:

[1339] The server analyzes the data using machine learning algorithms, such as cluster analysis and pattern recognition, to identify learners' learning patterns, strengths (e.g., math), interests (e.g., science), and weaknesses (e.g., English), and also assesses their stress levels and motivation during learning based on emotional data.

[1340] Step 5:

[1341] The server then generates a personalized learning plan based on the analysis results, designing a specific curriculum and study schedule, such as providing high-intensity learning at times when stress is low and lighter learning materials when relaxation is needed.

[1342] Step 6:

[1343] The server selects appropriate learning materials based on the generated learning plan and sends them to the learner's device. For example, it selects online lectures, exercises, simulation materials, etc. for a specific unit and generates links to them.

[1344] Step 7:

[1345] The terminal receives the learning material link sent from the server and displays it to the learner, who then uses it to begin the instructed learning activity, for example, by viewing the learning material through a dedicated learning application or a web browser.

[1346] Step 8:

[1347] Users (learners) use their devices to carry out learning activities according to the provided learning materials, such as watching online lectures, answering exercises, and recording their results.

[1348] Step 9:

[1349] The device tracks the learner's progress in real time and sends it to the server, specifically recording the study time, answer results, and learning material browsing history, and uploading it immediately.

[1350] Step 10:

[1351] At the same time, the device uses an emotion engine to collect emotional data and transmit it to the server in real time, for example, analyzing facial expressions and voice tones during learning.

[1352] Step 11:

[1353] The server analyzes the received progress and emotion data to assess the learner's progress and learning effectiveness, and automatically adjusts the learning plan and materials as needed. For example, if the learner is feeling stressed, it will insert short breaks or relaxation materials to help them relax.

[1354] Step 12:

[1355] The server then sends the updated learning plan and materials back to the device to coordinate the learner's next learning experience, and the learner again accesses the new plan and materials through the device to continue their learning.

[1356] Example 2

[1357] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1358] Traditional learning systems provide personalized learning plans based only on a learner's learning history, interests, and strengths, but do not take into account the learner's emotional state or stress level, making it difficult to achieve optimal learning results. It is also difficult to easily track progress and adjust learning plans in real time.

[1359] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1360] In this invention, the server includes means for collecting a learner's learning history, interests, and strengths, means for collecting emotional data during learning, means for analyzing the collected learning history, interests, strengths, and emotional data to identify a learning pattern and an emotional state, means for generating a personalized learning plan based on the identified learning pattern and emotional state, means for providing appropriate learning materials based on the generated learning plan, means for tracking the learner's progress and emotional data during learning in real time, and means for adjusting the learning plan and learning materials based on the tracked progress and emotional data. This makes it possible to provide an optimal learning experience that takes into account the learner's individual characteristics and emotional state.

[1361] "Learning history" refers to a record of all of a learner's past learning activities, including the materials studied, test results, and study time.

[1362] "Interests" refer to areas or subjects that a learner is interested in and wants to learn about, such as a particular field of science or history.

[1363] "Strengths" refer to areas or subjects in which a learner is particularly good, and are assessed based on test results and learning outcomes.

[1364] "Emotional data" refers to information about the emotions of learners obtained from facial expressions, vocal tones, pulse rates, etc., displayed during learning. For example, happiness, sadness, stress, etc.

[1365] "Analysis" refers to the process of analyzing collected data using statistical methods and machine learning algorithms to extract meaningful information.

[1366] A "learning pattern" refers to a set of tendencies and behaviors identified based on a learner's learning history, interests, and strengths, including which subjects they frequently study and how they progress through their studies.

[1367] "Emotional state" refers to the emotions a learner is feeling at a particular time, including their motivation and stress levels while learning.

[1368] A "personalized learning plan" refers to a learning schedule and curriculum that is optimized based on the learner's individual characteristics and emotional state.

[1369] "Learning materials" refers to the materials and content that learners use to carry out learning activities, such as textbooks, video lectures, and exercises.

[1370] "Progress" refers to information that shows where a learner is at and how they are progressing in their studies, including the amount of time spent studying and the percentage of questions answered correctly.

[1371] "Real-time tracking" refers to the process of instantly monitoring learner activity and instantly collecting and analyzing that data.

[1372] "Adjustment" refers to the process of modifying or optimizing existing learning plans and materials based on the learner's progress and emotional state.

[1373] This invention is a system that provides a personalized learning experience according to the characteristics of each learner, and further combines it with an emotion engine that recognizes the user's emotions. This system is composed of a server, a terminal, a learner (user), and an emotion engine.

[1374] Server Operation

[1375] The server collects learners' learning history, interests, and strengths from multiple databases and online learning platforms. Specific platforms used include online learning platforms and databases. Collected data includes the learning materials viewed by the learner, test results, and study time. It also uses an emotion engine to collect learners' emotional data. Emotional data is obtained from facial expressions, voice tone, pulse rate, and other factors while studying. For this purpose, emotion recognition software and sensors are used.

[1376] The server preprocesses and analyzes the collected learning data and emotional data using machine learning algorithms (e.g., TensorFlow). This analysis identifies the learner's learning patterns and emotional state. For example, it identifies which subjects the learner is strong in, which subjects they are interested in, and which subjects they are weak in. It also evaluates the learner's motivation and stress level during learning based on the emotional data.

[1377] Based on the analysis results, the server automatically generates an optimized learning plan for each learner. This plan includes a curriculum and learning schedule designed to strengthen the learner's strengths and shore up their weaknesses. It also suggests learning plans based on emotional data and timelines when students are least stressed. Based on the generated learning plan, the server selects appropriate learning materials and sends links to the learner's device. For example, online lectures, practice questions, and simulation materials for specific units are provided.

[1378] Device behavior

[1379] The device receives the learning material link sent from the server and displays it so that the learner can access it. Specifically, the learning material is provided using a web browser or a dedicated learning application. The device also tracks the learner's learning activities in real time and sends the data to the server. This data includes information such as how much of each learning material the learner has studied and the percentage of questions they have answered correctly. The device's emotion engine collects emotional data such as facial expressions and tone of voice during learning and sends this data to the server in real time.

[1380] Content Adjustment

[1381] The server analyzes the user's progress and emotional data and automatically adjusts the learning plan and materials as needed, for example, suggesting more relaxing materials or breaks if the learner is fatigued or stressed.

[1382] Specific examples

[1383] For example, suppose Learner A is good at math and interested in science, but feels that he or she is not good at English. The server collects data on Learner A and identifies these characteristics. As a result, the learning plan provides a focus on math and science materials, and includes additional materials to strengthen the foundations of English. Also, if the emotion engine detects stress while Learner A is studying, the learning plan will be immediately adjusted to include short breaks or videos for relaxation.

[1384] Prompt Sentence Examples

[1385] Examples of prompts to input into a generative AI model include:

[1386] "If Learner A is feeling stressed, how can I provide learning materials that will help him relax?"

[1387] In this way, this system can provide a personalized learning experience that is tailored to the learner's characteristics and emotions, maximizing learning effectiveness.

[1388] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1389] Step 1: Data collection

[1390] The server collects data on learners' learning history, interests, and strengths from multiple databases and online learning platforms. Specifically, it uses APIs to obtain data such as learning material browsing history, test results, and study time from the learning platforms. The input is the raw data obtained from the API, and the output is the collected learning dataset.

[1391] Step 2: Collecting Emotional Data

[1392] Once the user starts learning, the device's emotion engine collects emotion data using a facial recognition camera, microphone, pulse sensor, etc. Specifically, emotion recognition software is used to analyze the user's facial expressions in real time. The input is raw sensor data, and the output is analyzed emotion data. This data is sent to the server in real time.

[1393] Step 3: Data Preprocessing

[1394] The server uses statistical analysis tools and machine learning algorithms to perform preprocessing based on the collected training data and emotion data. Specifically, it complements missing data and corrects outliers. The input is the collected raw data, and the output is the preprocessed data.

[1395] Step 4: Data analysis

[1396] The server analyzes the preprocessed data and identifies the learner's learning patterns and emotional state. It uses machine learning algorithms (e.g., TensorFlow) to identify which subjects the learner has strengths and interests in, as well as other learning characteristics. The input is the preprocessed dataset, and the output is the analysis results.

[1397] Step 5: Generate a lesson plan

[1398] Based on the analysis results, the server generates an optimized learning plan for each learner. This plan includes a specific curriculum and study schedule to utilize the learner's strengths and overcome their weaknesses. It also reflects emotional data to create a study schedule for times when students are least stressed. The input is the analyzed learning patterns and emotional data, and the output is a personalized learning plan.

[1399] Step 6: Provide teaching materials

[1400] The server selects appropriate learning materials based on the generated learning plan and sends links to the learner's device. For example, it provides online lectures and exercises for a specific unit. The input is the learning plan, and the output is the learning material links.

[1401] Step 7: Track progress

[1402] The device sends real-time progress data to the server as the learner progresses through the study, including study time, percentage of correct answers to questions, and materials viewed. The input is the learner's behavioral data, and the output is updated progress data.

[1403] Step 8: Collect emotional data

[1404] The device's emotion engine analyzes the user's facial expressions and tone of voice during training and sends the emotion data to the server. The input is emotion data collected in real time, and the output is the analyzed emotion data.

[1405] Step 9: Content Adjustment

[1406] The server evaluates the learner's progress and learning effectiveness based on the received progress and emotion data, and adjusts the learning plan and learning materials as needed. For example, if the learner is fatigued, it provides relaxation materials. The input is the updated progress and emotion data, and the output is the adjusted learning plan and learning materials.

[1407] (Application example 2)

[1408] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1409] Conventional learning and sales systems have struggled to provide personalized services based on the characteristics and emotions of individual learners and customers. This has led to problems such as lower customer satisfaction during learning and purchasing, and a lack of appropriate product suggestions. As a result, the effectiveness of learning and purchasing experiences are not maximized, and users' expectations cannot be met.

[1410] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1411] In this invention, the server includes means for collecting a learner's learning history, interests, and strengths, means for analyzing the collected learning history, interests, and strengths to identify a learning pattern, means for generating a personalized learning plan based on the identified learning pattern, means for providing appropriate learning materials based on the generated learning plan, means for tracking the learner's progress in real time, means for adjusting the learning plan and learning materials based on the tracked progress, means for selecting products based on the customer's purchasing history and interests, means for collecting and analyzing customer emotional data at the time of purchase, and means for providing personalized product suggestions and services based on the analyzed emotional data. This makes it possible to provide a personalized learning experience and purchasing experience according to the characteristics and emotions of each learner or customer, maximizing learning effectiveness and improving customer satisfaction.

[1412] "Learning history" is a record of a learner's past learning activities and course status.

[1413] "Interest" is the state in which a learner or customer has interest in a particular field or product.

[1414] "Strengths" are subjects or skills that a learner excels at, or characteristics of a product that make it easy for customers to buy.

[1415] A "learning pattern" is a tendency or characteristic that is derived from an analysis of a learner's learning behavior and learning history.

[1416] A "personalized learning plan" is a learning schedule and curriculum that is optimized according to the characteristics of each individual learner.

[1417] "Learning Materials" refers to learning content and resources provided to learners, including online lectures, exercises, and simulation materials.

[1418] "Progress" refers to the status or assessment that indicates how far a learner has progressed in their studies.

[1419] "Purchase history" is a record of products and services that a customer has purchased in the past.

[1420] "Emotional data" is data that represents the emotional state of a customer or learner, and is obtained from facial expressions, tone of voice, pulse rate, etc.

[1421] A "product suggestion" is a recommendation of a specific product or service to a customer to encourage them to purchase it.

[1422] "Services" are value-added activities and support provided to Customers.

[1423] A "machine learning algorithm" is a computational method used to analyze large amounts of data and extract patterns and features.

[1424] "Analysis" is the process of extracting meaningful information from collected data.

[1425] The present invention provides a system for providing a personalized experience according to the characteristics and emotional state of individual learners and customers. The system includes a server, a terminal, a user, and an emotion engine.

[1426] Server Operation

[1427] 1. User Identification and Login

[1428] The server authenticates the user using the user ID, sends a request to the API server, and retrieves the user information after authentication.

[1429] 2. Data Collection

[1430] The server collects data about learners' learning history, interests, and strengths, as well as customer purchasing history and interests. This information is collected automatically from multiple databases and online platforms.

[1431] 3. Emotional Data Analysis

[1432] The server analyzes audio and video data in real time using an emotion engine (e.g., EmotionEngine library) to acquire and analyze emotional data of customers and learners.

[1433] 4. Generating personalized suggestions

[1434] The server uses machine learning algorithms to generate personalized learning plans and product recommendations based on the collected data and analyzed emotional data.

[1435] 5. Real-time feedback

[1436] The server sends the generated learning plans and product suggestions to the learner's or customer's device in real time, enabling dynamic adjustments based on the user's progress and emotions.

[1437] Device behavior

[1438] 1. Display of educational materials or product suggestions

[1439] The terminal receives links to educational materials and product suggestions sent from the server and displays them so that the user can access them. For this purpose, a web browser or a dedicated application is used.

[1440] 2. Learning and purchasing data collection

[1441] The device tracks data in real time as users study materials or view products and transmits it to a server.

[1442] 3. Emotional Data Transmission

[1443] The device is equipped with an emotion engine that collects emotional data from the user's facial expressions and voice tone during training and transmits it to the server in real time.

[1444] Specific examples

[1445] For example, suppose Customer A visits a physical store and logs in to a dedicated app. Their past purchase history reveals that they are interested in health foods, and the app also analyzes their facial expressions and tone of voice to sense fatigue. In this case, the server suggests products with a relaxing effect and sends this information to Customer A's device in real time.

[1446] In this way, the system, which is composed of a server, a terminal, a user, and an emotion engine, provides a personalized experience according to individual characteristics and emotions, solving the conventional problem.

[1447] Prompt Sentence Examples

[1448] Develop a strategy for recommending specific products to customers based on their past purchase history and current emotional data. For example, if a customer is feeling stressed, suggest products that will help them relax.

[1449] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1450] Step 1:

[1451] User Awareness and Login

[1452] The server receives the user ID and sends a request to the authentication API server. The input is the user ID, and the output is the authenticated user information. This information includes past learning history, purchase history, interests, etc. The server stores this information and prepares it for data collection in the next step.

[1453] Step 2:

[1454] Data collection

[1455] The server retrieves data about the target user's past learning history, interests, and strengths, as well as customer purchasing history and interests, from databases and online platforms. In this step, the server accesses various databases to obtain and aggregate information about each user. The input is the user ID and authentication information, and the output is all associated historical information.

[1456] Step 3:

[1457] Emotional Data Analysis

[1458] The device used by the user captures the user's voice and video data in real time and analyzes it using an emotion engine. The server receives the analysis results and identifies the user's emotional state. The input is voice and video data, and the output is emotional data (e.g., stress level, enjoyment, tiredness, etc.). This emotional data is processed by the emotion engine.

[1459] Step 4:

[1460] Generate personalized suggestions

[1461] The server uses a machine learning algorithm to generate personalized learning plans and product suggestions based on the collected data (learning history, purchasing history, interests, strengths) and emotional data. The input here is all of the above data (learning history, interests, strengths, emotional data), and the output is individually optimized learning plans and product suggestions. The specific suggestions are generated using a generative AI model.

[1462] Step 5:

[1463] Real-time feedback

[1464] The server sends the generated learning plan and product suggestions to the user's device in real time. The device receives them and visually presents them to the user. The input is the generated optimization plan, and the output is the suggestions or plan displayed on the user's device. At this point, the user's learning and purchasing progress is tracked and analyzed again and adjusted as necessary.

[1465] Step 6:

[1466] Collecting progress and sentiment data

[1467] The device tracks the user's learning or purchasing progress in real time and sends new data to the server, including study time, accuracy rate of questions answered, purchase history, information about products viewed, and emotional changes during learning or purchasing. The input is the user's real-time activity data, and the output is a new dataset for analysis.

[1468] Step 7:

[1469] Coordination of plans and proposals

[1470] The server reevaluates the learning plan and product recommendations based on progress and new emotional data, making adjustments as needed. The input is real-time collected progress and emotional data, and the output is a re-adjusted personalized plan, ensuring that users always receive an optimized experience.

[1471] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1472] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1473] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1474] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1475] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1476] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1477] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1478] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1479] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1480] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1481] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1482] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1483] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1484] 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.

[1485] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1486] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1487] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1488] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1489] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1490] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1491] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1492] The following is further disclosed regarding the above embodiment.

[1493] (Claim 1)

[1494] A means of collecting learners' learning history, interests and strengths;

[1495] a means of analyzing the collected learning history, interests and strengths to identify learning patterns;

[1496] means for generating a personalized learning plan based on the identified learning patterns;

[1497] a means for providing appropriate learning materials based on the generated learning plan;

[1498] a means of tracking learner progress in real time;

[1499] a means of adjusting learning plans and materials based on tracked progress;

[1500] A system including:

[1501] (Claim 2)

[1502] 10. The system of claim 1, further comprising means for pre-processing the collected data to make it suitable for analysis.

[1503] (Claim 3)

[1504] 10. The system of claim 1, further comprising means for analyzing the learned patterns using a machine learning algorithm.

[1505] "Example 1"

[1506] (Claim 1)

[1507] A means of collecting learners' learning history, interests and strengths;

[1508] a means of analyzing the collected learning history, interests and strengths to identify learning patterns;

[1509] means for generating a personalized learning plan based on the identified learning patterns;

[1510] a means for providing appropriate learning materials based on the generated learning plan;

[1511] a means of tracking learner progress in real time;

[1512] a means of adjusting learning plans and materials based on tracked progress;

[1513] A means of assessing each learner's progress and automatically redesigning and providing learning materials and lesson plans as needed;

[1514] A system including:

[1515] (Claim 2)

[1516] 10. The system of claim 1, further comprising means for pre-processing the collected data to make it suitable for analysis.

[1517] (Claim 3)

[1518] 10. The system of claim 1, further comprising means for analyzing the learned patterns using a machine learning algorithm.

[1519] "Application Example 1"

[1520] (Claim 1)

[1521] A means of collecting learners' learning history, interests and strengths;

[1522] a means of analyzing the collected learning history, interests and strengths to identify learning patterns;

[1523] means for generating a personalized learning plan based on the identified learning patterns;

[1524] a means for providing appropriate learning materials based on the generated learning plan;

[1525] a means of tracking learner progress in real time;

[1526] a means of adjusting learning plans and materials based on tracked progress;

[1527] means of collecting purchasing history, interests and preferences;

[1528] means for analyzing collected purchasing histories, interests and preferences to identify purchasing patterns;

[1529] means for generating a personalized product recommendation list based on the identified purchasing patterns;

[1530] A means for providing appropriate product information based on the generated product recommendation list;

[1531] A means of tracking customer movements within the store in real time;

[1532] means for adjusting product information based on the tracked movement status;

[1533] A system including:

[1534] (Claim 2)

[1535] 10. The system of claim 1, further comprising means for pre-processing the collected data to make it suitable for analysis.

[1536] (Claim 3)

[1537] 10. The system of claim 1, further comprising means for analyzing learning and purchasing patterns using machine learning algorithms.

[1538] "Example 2: Combining Emotion Engines"

[1539] (Claim 1)

[1540] A means of collecting learners' learning history, interests and strengths;

[1541] A means for collecting emotional data during training;

[1542] means for analyzing the collected learning history, interests, strengths and emotional data to identify learning patterns and emotional states;

[1543] means for generating a personalized learning plan based on the identified learning patterns and emotional state;

[1544] a means for providing appropriate learning materials based on the generated learning plan;

[1545] A means of tracking learner progress and learning emotions in real time;

[1546] a means of adjusting learning plans and materials based on tracked progress and sentiment data;

[1547] A system including:

[1548] (Claim 2)

[1549] 10. The system of claim 1, further comprising means for pre-processing the collected data to make it suitable for analysis.

[1550] (Claim 3)

[1551] 10. The system of claim 1, further comprising means for analyzing learned patterns and emotional states using machine learning algorithms.

[1552] "Application example 2 when combining emotion engines"

[1553] (Claim 1)

[1554] A means of collecting learners' learning history, interests and strengths;

[1555] a means of analyzing the collected learning history, interests and strengths to identify learning patterns;

[1556] means for generating a personalized learning plan based on the identified learning patterns;

[1557] a means for providing appropriate learning materials based on the generated learning plan;

[1558] a means of tracking learner progress in real time;

[1559] a means of adjusting learning plans and materials based on tracked progress;

[1560] a means for selecting products based on a customer's purchasing history and interests;

[1561] A means for collecting and analyzing customer emotional data at the time of purchase;

[1562] a means for providing personalized product recommendations and services based on the analyzed emotion data;

[1563] A system including:

[1564] (Claim 2)

[1565] 10. The system of claim 1, further comprising means for pre-processing the collected data to make it suitable for analysis.

[1566] (Claim 3)

[1567] 10. The system of claim 1, further comprising means for analyzing the learned patterns using a machine learning algorithm. [Explanation of symbols]

[1568] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting learners' learning history, interests and strengths; a means of analyzing the collected learning history, interests and strengths to identify learning patterns; means for generating a personalized learning plan based on the identified learning patterns; a means for providing appropriate learning materials based on the generated learning plan; a means of tracking learner progress in real time; a means of adjusting learning plans and materials based on tracked progress; A system including:

2. The system of claim 1 further comprising means for pre-processing the collected data to make it suitable for analysis.

3. The system of claim 1 , further comprising means for analyzing the learned patterns using a machine learning algorithm.

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