system

Abstract

We provide the system. [Solution] A means of collecting and analyzing learners' cognitive abilities, interests, learning styles, and potential talents, A means for generating a dynamic curriculum optimized for learners based on the analysis results, A means of providing an immersive learning environment based on the generated curriculum, A means of tracking learners' progress in real time and generating feedback, Means for adjusting learning content in feedback, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional educational systems, there is a problem that individual learners' cognitive abilities, interests, learning styles, and potential talents are often overlooked, resulting in a decline in learning effects or a decrease in learning motivation due to uniform education. In addition, there is a problem that skill formation lags behind the rapidly changing needs of society, and education provision according to individual levels of understanding and learning paces is not sufficiently carried out.

Means for Solving the Problems

[0005] This invention provides a means for analyzing learners' cognitive abilities, interests, learning styles, and potential talents in detail using a generative AI model. It then automatically generates a dynamic curriculum based on the analysis results and provides an immersive learning environment using VR / AR technology, enabling a learning experience that seamlessly connects theory and practice. Furthermore, it includes a means for tracking learning progress in real time and providing immediate feedback, thereby adjusting the curriculum to suit individual learning paces and levels of understanding, and ultimately maximizing the learner's potential.

[0006] A "learner" refers to a person who participates in an educational program to acquire specific knowledge or skills.

[0007] "Cognitive ability" refers to a learner's capacity to understand, process, remember, and judge information.

[0008] "Interest" refers to a learner's natural fascination with things or themes.

[0009] "Learning style" refers to the specific methods and approaches that learners use when gathering, understanding, and remembering information.

[0010] "Latent talent" refers to the potential abilities or skills that a learner possesses but have not yet fully developed.

[0011] A "generative AI model" refers to an algorithm or program that uses machine learning to analyze learner data and create individually optimized curricula.

[0012] A "dynamic curriculum" refers to an educational program that includes learning content that can be adapted to each individual learner and adjusted in real time based on their progress.

[0013] An "immersive learning environment" refers to an environment in which learners can gain a realistic learning experience through virtual reality (VR) and augmented reality (AR) technologies.

[0014] "Real-time tracking" refers to a state where the progress of learning is constantly monitored and immediate feedback can be obtained.

[0015] "Feedback" refers to information and advice provided based on the learner's understanding and performance.

Brief Description of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Embodiments for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disk (e.g., hard disk), or magnetic tape, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is a system that uses a generative AI model to analyze in detail a learner's cognitive abilities, interests, learning style, and potential talents, and generates a dynamic curriculum optimized for each individual learner. The system operates primarily through the cooperation of three parties: a server, a terminal, and a user.

[0038] The server first receives data sent by the user. This data includes basic profile information, past learning history, and information about the user's current interests and learning goals. The server uses this data to run a generative AI model and build a detailed profile of the user.

[0039] Next, the server selects the most suitable learning content for the user based on the constructed profile and generates a dynamic curriculum. This curriculum changes according to the learner's progress and provides a learning experience that connects theory and practice, particularly by incorporating immersive content using VR / AR technology.

[0040] The device displays learning content to learners in a VR / AR environment based on a dynamic curriculum provided by the server. This allows learners to engage in more intuitive and immersive learning. The device also plays a role in transmitting learning progress data to the server in real time.

[0041] Users can learn at their own pace while receiving feedback through their device. This feedback is generated by a server and provides appropriate advice and next steps based on the user's performance.

[0042] As a concrete example, suppose a user expresses interest in a history topic. The server analyzes the user's past learning patterns and interests and creates a curriculum that includes VR simulations of relevant historical events. The terminal provides the user with simulations based on this curriculum and sends the learning progress back to the server. Based on this data, the server suggests additional learning content or an improved curriculum tailored to the user's level of understanding.

[0043] In this way, by implementing the invention, it becomes possible to provide advanced educational services tailored to each individual learner.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] Users log in using their devices and answer questionnaires regarding their profile information and learning objectives. The collected data is then sent to the server.

[0047] Step 2:

[0048] Based on the received user data, the server uses a generative AI model to analyze the user's cognitive abilities, interests, learning style, and potential talents. As a result of the analysis, a detailed profile of the user's characteristics is generated.

[0049] Step 3:

[0050] Based on the user's profile, the server selects appropriate learning content and generates a dynamic curriculum. This curriculum is then organized into a learning plan incorporating the selected content.

[0051] Step 4:

[0052] The server sends the generated curriculum to the terminal, which then prepares to provide learners with immersive learning content in a VR / AR environment based on this curriculum.

[0053] Step 5:

[0054] Users progress through their learning using the provided VR / AR content via their device. Learning progress and interaction data are continuously transmitted to the server by the device.

[0055] Step 6:

[0056] The server evaluates the learner's performance based on the received progress data and generates real-time feedback. This feedback is used to adjust the learning content according to the user's level of understanding.

[0057] Step 7:

[0058] The server makes any necessary adjustments to the learning curriculum and provides the next learning step to the terminal. The terminal then presents the adjusted curriculum to the user and continues to support their learning.

[0059] Step 8:

[0060] Users receive feedback from the server and use it to improve their job performance by checking their learning progress and identifying the next steps they should take.

[0061] (Example 1)

[0062] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0063] Providing an optimal educational experience tailored to individual learners is difficult, and at the same time, there are challenges in appropriately tracking learners' progress and immediately adjusting learning content based on their understanding and abilities. Furthermore, it is difficult for learners to receive timely feedback while progressing at their own pace.

[0064] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0065] In this invention, the server includes means for collecting and analyzing a learner's cognitive abilities, interests, learning methods, and potential abilities; means for generating a dynamic instruction plan optimized for the learner based on the analysis results; and means for providing an experiential learning environment based on the generated instruction plan. This makes it possible to provide an individually optimized educational experience tailored to each learner, and to establish real-time feedback and an effective learning process.

[0066] A "learner" refers to an individual who is the target of acquiring knowledge and skills in educational activities.

[0067] "Cognitive ability" refers to the ability to understand, remember, analyze, and evaluate information, and is a characteristic that plays an important role in learners' intellectual activities.

[0068] "Interest" refers to the degree of interest or curiosity a learner has in a particular field or activity.

[0069] "Learning method" refers to the specific style or approach that learners use when acquiring information.

[0070] "Latent ability" refers to the abilities and talents that learners possess that are not yet apparent but have the potential to be demonstrated.

[0071] A "dynamic teaching plan" refers to the design of an educational curriculum that is updated as needed based on the learners' learning progress and level of understanding.

[0072] An "experiential learning environment" refers to a learning environment that utilizes technologies such as virtual reality and augmented reality, allowing learners to learn through actual experience.

[0073] "Real-time tracking" refers to the process of instantly monitoring a learner's learning progress and recording it as data.

[0074] "Feedback" refers to the evaluation and guidance provided for improvement regarding a learner's learning activities.

[0075] A "generative AI model" refers to an artificial intelligence system that uses machine learning algorithms to make predictions and perform analyses based on data.

[0076] This invention presents an information processing system that effectively collects and analyzes learners' cognitive abilities, interests, learning methods, and potential abilities. This system operates through the coordinated efforts of a server, a terminal, and a user.

[0077] The server first receives data sent by the user. This includes the user's basic information, past learning history, interests, and learning objectives. The received data is stored in a database on the server and then analyzed by a generative AI model. This generative AI model is an artificial intelligence system that implements common machine learning algorithms. For example, natural language processing models used in text generation and data analysis can be applied.

[0078] Next, the server generates a dynamic instruction plan optimized for the user based on the analysis results. This instruction plan is adjusted according to the learner's progress and is designed to provide an experiential learning environment, particularly by utilizing virtual reality and augmented reality technologies. In this process, hardware such as VR devices and dedicated software environments are used to generate simulations and interactive content.

[0079] The terminal receives the lesson plan provided by the server and displays it to the user. Learners can progress through the lesson plan on the terminal and reinforce their knowledge through hands-on experience. The terminal also supports the user's learning progress and provides real-time feedback on their progress to the server.

[0080] Users utilize the system at their own pace and complete the necessary learning. They receive feedback from their device, allowing them to adjust their learning methods and next steps as needed. For example, a user interested in history can experience a VR simulation themed around the French Revolution and learn about its historical context through the storyline.

[0081] As an example of a prompt, by sending the following text to the AI ​​model, "Generate a VR simulation to teach about the historical importance of a certain era. Target user's learning history: detailed history information, area of ​​interest: history, current level of understanding: intermediate," a relevant learning plan can be constructed.

[0082] Thus, by implementing the present invention, it becomes possible to provide learners with individually optimized educational experiences and to realize independent and efficient learning.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] Users enter basic information, past learning history, current interests, and learning objectives via their device. This input data includes age, past subjects studied, and target skills. This data is then sent to the server.

[0086] Step 2:

[0087] The server stores the data received from the user in a database. Next, the stored data is input into a generating AI model to create a detailed profile of the user's cognitive abilities and learning style. Natural language processing techniques are used to analyze the data. The output is profile information that describes the user's characteristics in detail.

[0088] Step 3:

[0089] The server generates a dynamic lesson plan based on the generated profile information. This is the process of creating a curriculum customized to the user's level of understanding, interests, and learning style. The prompt poses the question to the AI ​​model: "Suggest how to design the optimal learning experience based on the user's areas of interest." The output is a lesson plan that includes a learning environment using virtual reality and augmented reality technologies.

[0090] Step 4:

[0091] The terminal receives the lesson plan provided by the server and displays it to the user. The terminal controls a VR device to provide learners with immersive simulation content. The user can perform operations such as starting and ending the simulation.

[0092] Step 5:

[0093] The device records the user's actions and inputs during the learning session. It collects data such as eye-tracking and selected options, and sends this data to the server in real time. The input here is the user's actions and progress, and the output is the tracked user data.

[0094] Step 6:

[0095] The server analyzes the transmitted data and evaluates the user's performance. It then generates feedback based on the evaluation, determining suggestions for improvement and next learning tasks. The output is a feedback message presented to the user.

[0096] Step 7:

[0097] Users receive feedback displayed on their device and adjust their learning plan accordingly. As a result, learners can understand their own level of comprehension and choose to relearn or tackle new challenges as needed.

[0098] (Application Example 1)

[0099] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0100] When consumers shop online, it is difficult to efficiently find products that match their interests and style from a vast selection of goods. Furthermore, the lack of personalized shopping experiences makes improving consumer satisfaction a challenge.

[0101] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0102] In this invention, the server includes means for collecting and analyzing a consumer's interests, purchase history, and style; means for generating a dynamic product suggestion list optimized for the consumer based on the analysis results; and means for providing a virtual shopping environment based on the generated product suggestions. This enables consumers to effectively find products that are best suited to them and to try them out in a virtual environment.

[0103] "Consumer" refers to an individual or group that purchases goods or services.

[0104] "Interest" refers to the degree of interest an individual consumer has in a particular product or service.

[0105] "Purchase history" refers to a record of goods and services that a consumer has purchased in the past.

[0106] "Style" refers to the tendency of consumers to choose products and services based on their preferences and tastes.

[0107] A "dynamic product suggestion list" refers to a list that suggests optimized products and services based on consumers' interests, purchase history, and style.

[0108] A "virtual shopping environment" refers to a virtual purchasing space where consumers can browse products online and try them out interactively as needed.

[0109] One embodiment of this invention is a system that uses a generative AI model to process data in order to understand consumer purchasing behavior and provide an optimal shopping experience. This system consists of a server, terminals, and users.

[0110] First, the server collects data on consumers' interests, purchase history, and style, and uses this data to build a detailed consumer profile using a generative AI model. The server then uses machine learning frameworks such as TENSORFLOW® to generate a dynamic product suggestion list based on the profile. This list allows consumers to quickly find products that match their specific interests and style.

[0111] Next, the device functions as a wearable device such as smart glasses, providing consumers with a virtual shopping environment. The device utilizes a game engine like Unity to allow consumers to visually try out products in a virtual space. It also transmits consumer behavior data to a server in real time to support the updating of product suggestion lists.

[0112] Users can browse and try out products offered within the virtual shopping environment. Furthermore, based on user feedback, the server automatically adjusts subsequent recommendation lists to improve the next shopping experience.

[0113] For example, if a consumer is looking for new sports shoes, they can enter a virtual store wearing smart glasses, and recommended shoes that match their previously purchased running gear and style will be displayed. The generative AI model makes these suggestions based on the consumer's interests and past purchasing patterns.

[0114] An example of a prompt is as follows: "Please recommend shoes from our new summer sports collection to a male customer in his 20s who enjoys running as a hobby."

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The server receives data from consumers regarding their interests, purchase history, and style. This input data is collected, irrelevant information is filtered, and essential features are extracted. This enables the construction of accurate consumer profiles.

[0118] Step 2:

[0119] The server uses a generative AI model to build consumer profiles based on the received data. Specifically, it analyzes the data using TensorFlow to generate profiles that reflect consumers' interests, styles, and past purchasing behavior. As a result of profile generation, prompts are created for product recommendations tailored to each consumer.

[0120] Step 3:

[0121] The server generates a dynamic product suggestion list optimized for the consumer based on the generated profile. In this step, relevant product information is retrieved from the database using the prompts mentioned above. The AI ​​model selects and ranks relevant products and outputs them as an optimal suggestion list.

[0122] Step 4:

[0123] The terminal uses a dynamic product suggestion list received from the server to build a virtual shopping environment. In this process, it uses the Unity engine to render 3D models, providing an interface that allows users to visually try out products. This environment is designed to have broad compatibility with user interactions.

[0124] Step 5:

[0125] Users enter a virtual shopping environment on their device and select items of interest from the presented products. During this process, the system tracks the user's gaze and selection history, collecting data to improve future recommendations. After the trial, users provide feedback and evaluation information.

[0126] Step 6:

[0127] The device sends user feedback to the server, which then uses it to improve the product suggestion list. This feedback data is used as input to rerun the generative AI model and update the consumer profile and product suggestion list. This iterative cycle leads to an improved consumer experience.

[0128] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0129] This invention is an educational system that combines a generative AI model and an emotion engine to provide an optimized learning experience by analyzing in detail the learner's cognitive abilities, interests, learning style, potential talents, and emotional state. The system operates by efficiently exchanging information among three parties: the server, the terminal, and the user.

[0130] The server first receives cognitive and emotional data sent from the user. This includes information about the user's basic characteristics and ongoing learning activities, as well as emotional state data collected by the emotion engine. The server processes this data using a generative AI model to build a detailed profile tailored to the user's individual characteristics.

[0131] Subsequently, the server selects and organizes learning content optimized for the user based on the generated profile and emotional state, generating a dynamic curriculum. The curriculum is structured to take the learner's psychological state into account and to evoke positive emotions. In particular, it enables an immersive learning experience for the user by leveraging VR / AR technology to connect theory and practice.

[0132] The device provides learners with learning materials in a VR / AR environment, following a dynamic curriculum received from the server. The device also monitors the user's emotional responses during learning via an emotion engine and sends that data back to the server in real time.

[0133] Users progress through their learning using learning content provided via their devices. The learning experience is adjusted in a timely manner based on the user's current emotional state. For example, if a user is feeling discouraged with their learning, the server receives feedback from the emotion engine, adjusts the curriculum, and sends content and encouraging messages to motivate the user.

[0134] For example, if a user shows signs of frustration while learning a math curriculum, the device detects this change in emotion through its emotion engine. The server immediately receives this data and responds by restructuring the curriculum to provide interactive and visual learning materials to calm the learner.

[0135] In this way, this system, which integrates an emotional engine, can unlock learners' potential while providing personalized learning experiences that respond to their emotions.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] Users log in using their devices and fill out questionnaires about their profile information and learning goals. Simultaneously, user emotion data is collected through sensors such as the device's camera and microphone.

[0139] Step 2:

[0140] The device sends the collected user profile information and emotional data to the server. This includes emotional information derived from the user's facial expressions and tone of voice.

[0141] Step 3:

[0142] The server uses a generative AI model to analyze the received data. This analyzes the user's cognitive abilities, interests, learning style, potential talents, and current emotional state, generating a detailed profile.

[0143] Step 4:

[0144] Based on profile results and sentiment data, the server selects the most suitable learning content for the user and creates a dynamic curriculum. This curriculum optimizes the order and format of the content, taking the user's emotions into consideration.

[0145] Step 5:

[0146] The server sends the generated curriculum to the terminal, which then prepares to provide learning content to the learner in a VR / AR environment based on this curriculum.

[0147] Step 6:

[0148] Users engage in personalized learning using content provided through their devices. During learning, the emotion engine continuously monitors the user's emotions and sends emotion data to the server in real time as needed.

[0149] Step 7:

[0150] The server evaluates the user's learning progress based on sentiment data and generates real-time feedback. The feedback is designed to maintain or improve the user's motivation and adjusts the learning content as needed.

[0151] Step 8:

[0152] The server provides the adjusted curriculum to the terminal, and the terminal continues to present it to the user, thereby facilitating a smooth learning experience.

[0153] Step 9:

[0154] Users can use the feedback provided by the server to advance their learning and improve their abilities and acquire new skills.

[0155] (Example 2)

[0156] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0157] Traditional education systems face the challenge of providing individualized learning experiences that take into account each learner's cognitive characteristics and emotional state. As a result, many learners are unable to maximize their learning efficiency within a standardized curriculum-based learning environment and tend to lose interest in learning. Furthermore, the lack of sufficient real-time feedback and adjustments to learning content based on emotional responses limits the ability to improve learner motivation and unlock their potential.

[0158] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0159] In this invention, the server includes means for collecting and analyzing the learner's cognitive abilities, interests, learning style, potential talents, and emotional state; means for generating a dynamic curriculum optimized for the learner based on the analysis results using a generative AI model; and means for providing an immersive learning environment based on the generated curriculum using VR / AR technology. This enables the provision of a personalized learning experience in real time that is tailored to the learner's individual characteristics and emotional state, thereby improving learning efficiency and maintaining motivation.

[0160] A "learner" is an individual who acquires knowledge and skills through the use of an educational system.

[0161] "Cognitive ability" refers to intellectual functions such as a learner's comprehension, memory, and thinking skills.

[0162] "Interest" refers to the degree to which a learner shows interest in a particular topic or activity.

[0163] "Learning style" refers to the tendencies and methods by which learners most effectively understand and process information.

[0164] "Latent talent" refers to untapped abilities or qualities that a learner may possess in a particular field or activity.

[0165] "Emotional state" refers to the learner's emotional situation or changes in mood.

[0166] A "generative AI model" refers to an artificial intelligence algorithm that performs predictions and generation based on large amounts of data.

[0167] A "dynamic curriculum" is a learning plan and material structure that is adjusted in real time according to the characteristics and needs of the learners.

[0168] "VR / AR technology" is a technology that uses virtual reality and augmented reality to provide information visually.

[0169] An "immersive learning environment" is a highly interactive learning space designed to allow learners to concentrate and participate in learning activities.

[0170] "Feedback" refers to evaluations and advice provided based on a learner's learning activities and emotional state.

[0171] This invention is an educational system designed to provide learners with personalized learning experiences. The central elements of the invention are a server integrating a generative AI model and an emotion engine, and terminals that receive learning content from this server. The entire system is designed to provide an interactive and immersive learning environment.

[0172] The server collects data submitted by learners and analyzes their cognitive abilities, interests, learning styles, potential talents, and emotional states. This process utilizes a generative AI model, and data analysis tools such as Python and R are used for data analysis. This generates a detailed profile based on the learner's characteristics. For example, the server inputs a prompt such as "Suggest the optimal teaching method based on the user profile" into the generative AI model, generating a curriculum suited to that profile.

[0173] The server then builds a customized dynamic curriculum based on the generated profile and sends it to the terminal. This curriculum generation process utilizes VR / AR technology as interactive learning materials to create an immersive learning environment where learners can connect theory and practice.

[0174] The terminal provides learners with educational content based on a dynamic curriculum provided by the server. The terminal is equipped with sensors that can track learners' movements and gaze in real time, allowing for monitoring of emotional responses during learning. This emotional data is continuously transmitted to the server and used to adjust the overall system.

[0175] Users progress through their learning using a curriculum delivered via their device. As users learn, the content is dynamically adjusted according to their emotional state, ensuring a consistently engaging learning experience. For example, if a user is feeling discouraged about learning, the device can identify this emotion through an emotion engine, and the server can adjust the content to improve motivation.

[0176] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0177] Step 1:

[0178] Users log in to the system via their device and enter basic information to begin learning. This includes information about their current learning subject, objectives, and mood. This input data is sent from the device to the server.

[0179] Step 2:

[0180] The server uses a generative AI model to analyze data based on basic information, cognitive data, and emotional data received from the user. Input data includes the user's past learning history and real-time emotional data. This data is analyzed to generate a profile tailored to the user's learning characteristics and emotional state. The output is a detailed profile that optimally reflects the user's characteristics.

[0181] Step 3:

[0182] The server generates a dynamic curriculum based on the generated profile. This curriculum is created by inputting the prompt "Suggest the optimal learning method based on the user profile" into the generating AI model. The input data is profile information, and the output is a detailed curriculum that outlines what learning content the user is interested in and how they should proceed with their learning.

[0183] Step 4:

[0184] The device provides users with learning content using VR / AR technology based on a customized curriculum received from the server. The input data is curriculum information from the server, and the device provides learning materials to the user according to that information. As output, the user can obtain an immersive learning experience.

[0185] Step 5:

[0186] The device monitors the user's emotional state in real time during the learning process and feeds this information back to the server. The input data consists of the user's biometric information and emotional responses during the learning process, which are sent to the server via the emotion engine. The output is the user's emotional data during the learning process.

[0187] Step 6:

[0188] The server adjusts the curriculum as needed based on emotional data received from the device. The input data is user emotional information processed through an emotion engine. Based on this information, a generative AI model creates optimal feedback and updates content to maintain the user's interest and motivation. As output, a new learning strategy and an adjusted curriculum are generated and sent to the device.

[0189] (Application Example 2)

[0190] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0191] In modern industry, workers are required to quickly and effectively acquire skills in complex machinery and technology. However, traditional methods often fail to adequately consider the individual cognitive characteristics and emotional states of learners, resulting in inefficient learning and increased stress. To address these challenges, there is a need for a system that provides an individualized learning environment and real-time feedback.

[0192] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0193] In this invention, the server includes means for collecting and analyzing the learner's cognitive functions, interests, learning style, and potential abilities; means for generating an adaptive curriculum optimized for the learner based on the analysis results; and means for sensing emotional states and displaying interactive content that takes the learner's psychological state into consideration in real time. This provides an immersive learning experience tailored to the individual needs and emotions of the learner, enabling efficient skill acquisition and stress reduction.

[0194] A "learner" is an individual who utilizes an educational system to acquire specific knowledge or skills.

[0195] "Cognitive function" refers to the brain's processes necessary to understand, analyze, and remember information.

[0196] "Interest" refers to the degree of interest or curiosity a learner has towards a particular topic or activity.

[0197] "Learning format" refers to a category of methods and approaches that enable learners to absorb information most effectively.

[0198] "Latent abilities" refer to skills and talents that learners have not yet manifested but have the potential to develop.

[0199] An "adaptive curriculum" is an educational plan that is dynamically adjusted based on the individual characteristics and emotional state of each learner.

[0200] "Emotional state" refers to the emotions and psychological responses that learners experience in a particular situation.

[0201] "Interactive content" refers to educational materials that are interactive, allowing learners to directly manipulate them and receive feedback.

[0202] An "immersive learning experience" is a learning environment designed to allow learners to become so deeply engrossed in learning activities that they forget their surroundings.

[0203] "Skill acquisition" is the process of acquiring the techniques and knowledge necessary to perform a specific job or task professionally.

[0204] The system that realizes this invention mainly consists of a server, a terminal, and a user. The server collects and analyzes the learner's cognitive functions, interests, learning style, and potential abilities. This uses an advanced data processing technique called a generative AI model. The generative AI model is built using software such as Python or TensorFlow and is capable of precisely identifying the learner's profile.

[0205] Based on the analysis, the server generates an adaptive curriculum optimized for the learner and sends it to the terminal. The terminal then uses VR / AR technology to provide the learner with an immersive learning environment based on the received curriculum. Furthermore, the terminal monitors the user's emotional state using an emotion engine and sends data back to the server in real time based on predetermined indicators. Building a VR / AR environment using Unity is recommended for the implementation of this terminal.

[0206] As a concrete example, consider a scenario where a user is learning to operate a new welding machine. The server analyzes the acquired emotional data in real time, and if the learner feels anxious, it sends interactive content tailored to that emotion to the user's device. The user receives visual guidelines through the device, enabling them to efficiently improve their skills.

[0207] An example of a prompt message is as follows: "Analyze real-time data from workers who want to improve their welding machine operation skills and provide an optimal training simulation."

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

[0209] Step 1:

[0210] The server receives cognitive and emotional state data submitted by the user. This input data includes the learner's cognitive functions, interests, learning style, and potential abilities. The server collects the data, performs initial analysis, and then sends it as input to a generative AI model after data cleansing and normalization.

[0211] Step 2:

[0212] The server uses a generative AI model to analyze the input data in detail and build learner profiles. This process identifies the characteristics of each learner through data calculations and outputs profile data. Specifically, it performs weighting based on each characteristic and organizes the analysis results.

[0213] Step 3:

[0214] The server generates an adaptive curriculum optimized for the learner based on profile data. This curriculum is dynamic, taking into account the user's current situation, and is intended for use in a VR / AR environment. The generated curriculum is sent to the device, enabling execution on the device.

[0215] Step 4:

[0216] The device builds an immersive learning environment using VR / AR technology based on an adaptive curriculum received from the server. This environment is designed using Unity and other tools, and is interactive. The device monitors the learner's emotional state in real time and sends this data to an emotion engine to obtain feedback.

[0217] Step 5:

[0218] Users immerse themselves in the learning environment provided through the device and perform actions. The device detects the user's responses and emotional state and sends data to the server as needed. The emotion engine returns the results of the user's psychological state analysis to the server, providing feedback for further curriculum adjustments.

[0219] Step 6:

[0220] The server receives feedback from the emotion engine and adjusts the curriculum accordingly. This allows it to provide relaxing content when the user is stressed, or adjust the difficulty level when the user is highly motivated.

[0221] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0222] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0223] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0227] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0228] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0229] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0230] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0231] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0232] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0233] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0234] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0235] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0236] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0237] This invention is a system that uses a generative AI model to analyze in detail a learner's cognitive abilities, interests, learning style, and potential talents, and generates a dynamic curriculum optimized for each individual learner. The system operates primarily through the cooperation of three parties: a server, a terminal, and a user.

[0238] The server first receives data sent by the user. This data includes basic profile information, past learning history, and information about the user's current interests and learning goals. The server uses this data to run a generative AI model and build a detailed profile of the user.

[0239] Next, the server selects the most suitable learning content for the user based on the constructed profile and generates a dynamic curriculum. This curriculum changes according to the learner's progress and provides a learning experience that connects theory and practice, particularly by incorporating immersive content using VR / AR technology.

[0240] The device displays learning content to learners in a VR / AR environment based on a dynamic curriculum provided by the server. This allows learners to engage in more intuitive and immersive learning. The device also plays a role in transmitting learning progress data to the server in real time.

[0241] Users can learn at their own pace while receiving feedback through their device. This feedback is generated by a server and provides appropriate advice and next steps based on the user's performance.

[0242] As a concrete example, suppose a user expresses interest in a history topic. The server analyzes the user's past learning patterns and interests and creates a curriculum that includes VR simulations of relevant historical events. The terminal provides the user with simulations based on this curriculum and sends the learning progress back to the server. Based on this data, the server suggests additional learning content or an improved curriculum tailored to the user's level of understanding.

[0243] In this way, by implementing the invention, it becomes possible to provide advanced educational services tailored to each individual learner.

[0244] The following describes the processing flow.

[0245] Step 1:

[0246] Users log in using their devices and answer questionnaires regarding their profile information and learning objectives. The collected data is then sent to the server.

[0247] Step 2:

[0248] Based on the received user data, the server uses a generative AI model to analyze the user's cognitive abilities, interests, learning style, and potential talents. As a result of the analysis, a detailed profile of the user's characteristics is generated.

[0249] Step 3:

[0250] Based on the user's profile, the server selects appropriate learning content and generates a dynamic curriculum. This curriculum is then organized into a learning plan incorporating the selected content.

[0251] Step 4:

[0252] The server sends the generated curriculum to the terminal, which then prepares to provide learners with immersive learning content in a VR / AR environment based on this curriculum.

[0253] Step 5:

[0254] Users progress through their learning using the provided VR / AR content via their device. Learning progress and interaction data are continuously transmitted to the server by the device.

[0255] Step 6:

[0256] The server evaluates the learner's performance based on the received progress data and generates real-time feedback. This feedback is used to adjust the learning content according to the user's level of understanding.

[0257] Step 7:

[0258] The server makes any necessary adjustments to the learning curriculum and provides the next learning step to the terminal. The terminal then presents the adjusted curriculum to the user and continues to support their learning.

[0259] Step 8:

[0260] Users receive feedback from the server and use it to improve their job performance by checking their learning progress and identifying the next steps they should take.

[0261] (Example 1)

[0262] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0263] Providing an optimal educational experience tailored to individual learners is difficult, and at the same time, there are challenges in appropriately tracking learners' progress and immediately adjusting learning content based on their understanding and abilities. Furthermore, it is difficult for learners to receive timely feedback while progressing at their own pace.

[0264] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0265] In this invention, the server includes means for collecting and analyzing a learner's cognitive abilities, interests, learning methods, and potential abilities; means for generating a dynamic instruction plan optimized for the learner based on the analysis results; and means for providing an experiential learning environment based on the generated instruction plan. This makes it possible to provide an individually optimized educational experience tailored to each learner, and to establish real-time feedback and an effective learning process.

[0266] A "learner" refers to an individual who is the target of acquiring knowledge and skills in educational activities.

[0267] "Cognitive ability" refers to the ability to understand, remember, analyze, and evaluate information, and is a characteristic that plays an important role in learners' intellectual activities.

[0268] "Interest" refers to the degree of interest or curiosity a learner has in a particular field or activity.

[0269] "Learning method" refers to the specific style or approach that learners use when acquiring information.

[0270] "Latent ability" refers to the abilities and talents that learners possess that are not yet apparent but have the potential to be demonstrated.

[0271] A "dynamic teaching plan" refers to the design of an educational curriculum that is updated as needed based on the learners' learning progress and level of understanding.

[0272] An "experiential learning environment" refers to a learning environment that utilizes technologies such as virtual reality and augmented reality, allowing learners to learn through actual experience.

[0273] "Real-time tracking" refers to the process of instantly monitoring a learner's learning progress and recording it as data.

[0274] "Feedback" refers to the evaluation and guidance provided for improvement regarding a learner's learning activities.

[0275] A "generative AI model" refers to an artificial intelligence system that uses machine learning algorithms to make predictions and perform analyses based on data.

[0276] This invention presents an information processing system that effectively collects and analyzes learners' cognitive abilities, interests, learning methods, and potential abilities. This system operates through the coordinated efforts of a server, a terminal, and a user.

[0277] The server first receives data sent by the user. This includes the user's basic information, past learning history, interests, and learning objectives. The received data is stored in a database on the server and then analyzed by a generative AI model. This generative AI model is an artificial intelligence system that implements common machine learning algorithms. For example, natural language processing models used in text generation and data analysis can be applied.

[0278] Next, the server generates a dynamic instruction plan optimized for the user based on the analysis results. This instruction plan is adjusted according to the learner's progress and is designed to provide an experiential learning environment, particularly by utilizing virtual reality and augmented reality technologies. In this process, hardware such as VR devices and dedicated software environments are used to generate simulations and interactive content.

[0279] The terminal receives the lesson plan provided by the server and displays it to the user. Learners can progress through the lesson plan on the terminal and reinforce their knowledge through hands-on experience. The terminal also supports the user's learning progress and provides real-time feedback on their progress to the server.

[0280] Users utilize the system at their own pace and complete the necessary learning. They receive feedback from their device, allowing them to adjust their learning methods and next steps as needed. For example, a user interested in history can experience a VR simulation themed around the French Revolution and learn about its historical context through the storyline.

[0281] As an example of a prompt, by sending the following text to the AI ​​model, "Generate a VR simulation to teach about the historical importance of a certain era. Target user's learning history: detailed history information, area of ​​interest: history, current level of understanding: intermediate," a relevant learning plan can be constructed.

[0282] By implementing the present invention in this way, it becomes possible to provide an education experience optimized individually for learners and to realize autonomous and efficient learning.

[0283] The flow of specific processing in Example 1 will be described using FIG. 11.

[0284] Step 1:

[0285] The user inputs basic information, past learning history, current interests, and learning objectives via a terminal. This input data includes age, past learning subjects, target skills, etc. This data is transmitted to the server.

[0286] Step 2:

[0287] The server stores the data received from the user in a database. Next, the stored data is input into a generated AI model to generate a detailed profile regarding the user's cognitive ability and learning style. Here, natural language processing technology is used to analyze the data. The output is profile information that describes the user's characteristics in detail.

[0288] Step 3:

[0289] The server generates a dynamic guidance plan based on the generated profile information. This is the process of creating a curriculum customized according to the user's comprehension level, interests, and learning style. As a prompt sentence, a question "Please propose a method for designing an optimal learning experience based on the user's field of interest" is posed to the AI model. The output is a guidance plan including a learning environment using virtual reality or augmented reality technology.

[0290] Step 4:

[0291] The terminal receives the lesson plan provided by the server and displays it to the user. The terminal controls a VR device to provide learners with immersive simulation content. The user can perform operations such as starting and ending the simulation.

[0292] Step 5:

[0293] The device records the user's actions and inputs during the learning session. It collects data such as eye-tracking and selected options, and sends this data to the server in real time. The input here is the user's actions and progress, and the output is the tracked user data.

[0294] Step 6:

[0295] The server analyzes the transmitted data and evaluates the user's performance. It then generates feedback based on the evaluation, determining suggestions for improvement and next learning tasks. The output is a feedback message presented to the user.

[0296] Step 7:

[0297] Users receive feedback displayed on their device and adjust their learning plan accordingly. As a result, learners can understand their own level of comprehension and choose to relearn or tackle new challenges as needed.

[0298] (Application Example 1)

[0299] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0300] When consumers shop online, it is difficult to efficiently find products that match their interests and style from a vast selection of goods. Furthermore, the lack of personalized shopping experiences makes improving consumer satisfaction a challenge.

[0301] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means.

[0302] In this invention, the server includes means for collecting and analyzing the interests, purchase history, and style of consumers, means for generating a dynamically optimized product proposal list for consumers based on the analysis results, and means for providing a virtual shopping environment based on the generated product proposals. As a result, consumers can effectively find products optimized for themselves and try products in a virtual environment.

[0303] "Consumer" refers to an individual or group that purchases goods or services.

[0304] "Interest" refers to the degree of interest of an individual consumer in products or services in which they are particularly interested.

[0305] "Purchase history" refers to the records of goods or services purchased by a consumer in the past.

[0306] "Style" refers to the tendency of a consumer to choose products or services based on their preferences and tastes.

[0307] "Dynamically optimized product proposal list" refers to a list that proposes optimized products or services based on the interests, purchase history, and style of consumers.

[0308] "Virtual shopping environment" refers to a virtual purchasing space where consumers can view products online and interactively try them if necessary.

[0309] The mode for implementing this invention is a system that uses a generative AI model to perform data processing in order to understand consumer purchasing behavior and provide an optimal shopping experience. This system is composed of a server, a terminal, and a user.

[0310] First, the server collects data on consumers' interests, purchase history, and style, and uses this data to build a detailed consumer profile using a generative AI model. The server then uses machine learning frameworks such as TensorFlow to generate a dynamic product suggestion list based on the profile. This list allows consumers to quickly find products that match their specific interests and style.

[0311] Next, the device functions as a wearable device such as smart glasses, providing consumers with a virtual shopping environment. The device utilizes a game engine like Unity to allow consumers to visually try out products in a virtual space. It also transmits consumer behavior data to a server in real time to support the updating of product suggestion lists.

[0312] Users can browse and try out products offered within the virtual shopping environment. Furthermore, based on user feedback, the server automatically adjusts subsequent recommendation lists to improve the next shopping experience.

[0313] For example, if a consumer is looking for new sports shoes, they can enter a virtual store wearing smart glasses, and recommended shoes that match their previously purchased running gear and style will be displayed. The generative AI model makes these suggestions based on the consumer's interests and past purchasing patterns.

[0314] An example of a prompt is as follows: "Please recommend shoes from our new summer sports collection to a male customer in his 20s who enjoys running as a hobby."

[0315] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0316] Step 1:

[0317] The server receives data from consumers regarding their interests, purchase history, and style. This input data is collected, irrelevant information is filtered, and essential features are extracted. This enables the construction of accurate consumer profiles.

[0318] Step 2:

[0319] The server uses a generative AI model to build consumer profiles based on the received data. Specifically, it analyzes the data using TensorFlow to generate profiles that reflect consumers' interests, styles, and past purchasing behavior. As a result of profile generation, prompts are created for product recommendations tailored to each consumer.

[0320] Step 3:

[0321] The server generates a dynamic product suggestion list optimized for the consumer based on the generated profile. In this step, relevant product information is retrieved from the database using the prompts mentioned above. The AI ​​model selects and ranks relevant products and outputs them as an optimal suggestion list.

[0322] Step 4:

[0323] The terminal uses a dynamic product suggestion list received from the server to build a virtual shopping environment. In this process, it uses the Unity engine to render 3D models, providing an interface that allows users to visually try out products. This environment is designed to have broad compatibility with user interactions.

[0324] Step 5:

[0325] Users enter a virtual shopping environment on their device and select items of interest from the presented products. During this process, the system tracks the user's gaze and selection history, collecting data to improve future recommendations. After the trial, users provide feedback and evaluation information.

[0326] Step 6:

[0327] The device sends user feedback to the server, which then uses it to improve the product suggestion list. This feedback data is used as input to rerun the generative AI model and update the consumer profile and product suggestion list. This iterative cycle leads to an improved consumer experience.

[0328] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0329] This invention is an educational system that combines a generative AI model and an emotion engine to provide an optimized learning experience by analyzing in detail the learner's cognitive abilities, interests, learning style, potential talents, and emotional state. The system operates by efficiently exchanging information among three parties: the server, the terminal, and the user.

[0330] The server first receives cognitive and emotional data sent from the user. This includes information about the user's basic characteristics and ongoing learning activities, as well as emotional state data collected by the emotion engine. The server processes this data using a generative AI model to build a detailed profile tailored to the user's individual characteristics.

[0331] Subsequently, the server selects and organizes learning content optimized for the user based on the generated profile and emotional state, generating a dynamic curriculum. The curriculum is structured to take the learner's psychological state into account and to evoke positive emotions. In particular, it enables an immersive learning experience for the user by leveraging VR / AR technology to connect theory and practice.

[0332] The device provides learners with learning materials in a VR / AR environment, following a dynamic curriculum received from the server. The device also monitors the user's emotional responses during learning via an emotion engine and sends that data back to the server in real time.

[0333] Users progress through their learning using learning content provided via their devices. The learning experience is adjusted in a timely manner based on the user's current emotional state. For example, if a user is feeling discouraged with their learning, the server receives feedback from the emotion engine, adjusts the curriculum, and sends content and encouraging messages to motivate the user.

[0334] For example, if a user shows signs of frustration while learning a math curriculum, the device detects this change in emotion through its emotion engine. The server immediately receives this data and responds by restructuring the curriculum to provide interactive and visual learning materials to calm the learner.

[0335] In this way, this system, which integrates an emotional engine, can unlock learners' potential while providing personalized learning experiences that respond to their emotions.

[0336] The following describes the processing flow.

[0337] Step 1:

[0338] Users log in using their devices and fill out questionnaires about their profile information and learning goals. Simultaneously, user emotion data is collected through sensors such as the device's camera and microphone.

[0339] Step 2:

[0340] The device sends the collected user profile information and emotional data to the server. This includes emotional information derived from the user's facial expressions and tone of voice.

[0341] Step 3:

[0342] The server uses a generative AI model to analyze the received data. This analyzes the user's cognitive abilities, interests, learning style, potential talents, and current emotional state, generating a detailed profile.

[0343] Step 4:

[0344] Based on profile results and sentiment data, the server selects the most suitable learning content for the user and creates a dynamic curriculum. This curriculum optimizes the order and format of the content, taking the user's emotions into consideration.

[0345] Step 5:

[0346] The server sends the generated curriculum to the terminal, which then prepares to provide learning content to the learner in a VR / AR environment based on this curriculum.

[0347] Step 6:

[0348] Users engage in personalized learning using content provided through their devices. During learning, the emotion engine continuously monitors the user's emotions and sends emotion data to the server in real time as needed.

[0349] Step 7:

[0350] The server evaluates the user's learning progress based on sentiment data and generates real-time feedback. The feedback is designed to maintain or improve the user's motivation and adjusts the learning content as needed.

[0351] Step 8:

[0352] The server provides the adjusted curriculum to the terminal, and the terminal continues to present it to the user, thereby facilitating a smooth learning experience.

[0353] Step 9:

[0354] Users can use the feedback provided by the server to advance their learning and improve their abilities and acquire new skills.

[0355] (Example 2)

[0356] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0357] Traditional education systems face the challenge of providing individualized learning experiences that take into account each learner's cognitive characteristics and emotional state. As a result, many learners are unable to maximize their learning efficiency within a standardized curriculum-based learning environment and tend to lose interest in learning. Furthermore, the lack of sufficient real-time feedback and adjustments to learning content based on emotional responses limits the ability to improve learner motivation and unlock their potential.

[0358] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0359] In this invention, the server includes means for collecting and analyzing the learner's cognitive abilities, interests, learning style, potential talents, and emotional state; means for generating a dynamic curriculum optimized for the learner based on the analysis results using a generative AI model; and means for providing an immersive learning environment based on the generated curriculum using VR / AR technology. This enables the provision of a personalized learning experience in real time that is tailored to the learner's individual characteristics and emotional state, thereby improving learning efficiency and maintaining motivation.

[0360] A "learner" is an individual who acquires knowledge and skills through the use of an educational system.

[0361] "Cognitive ability" refers to intellectual functions such as a learner's comprehension, memory, and thinking skills.

[0362] "Interest" refers to the degree to which a learner shows interest in a particular topic or activity.

[0363] "Learning style" refers to the tendencies and methods by which learners most effectively understand and process information.

[0364] "Latent talent" refers to untapped abilities or qualities that a learner may possess in a particular field or activity.

[0365] "Emotional state" refers to the learner's emotional situation or changes in mood.

[0366] A "generative AI model" refers to an artificial intelligence algorithm that performs predictions and generation based on large amounts of data.

[0367] A "dynamic curriculum" is a learning plan and material structure that is adjusted in real time according to the characteristics and needs of the learners.

[0368] "VR / AR technology" is a technology that uses virtual reality and augmented reality to provide information visually.

[0369] An "immersive learning environment" is a highly interactive learning space designed to allow learners to concentrate and participate in learning activities.

[0370] "Feedback" refers to evaluations and advice provided based on a learner's learning activities and emotional state.

[0371] This invention is an educational system designed to provide learners with personalized learning experiences. The central elements of the invention are a server integrating a generative AI model and an emotion engine, and terminals that receive learning content from this server. The entire system is designed to provide an interactive and immersive learning environment.

[0372] The server collects data submitted by learners and analyzes their cognitive abilities, interests, learning styles, potential talents, and emotional states. This process utilizes a generative AI model, and data analysis tools such as Python and R are used for data analysis. This generates a detailed profile based on the learner's characteristics. For example, the server inputs a prompt such as "Suggest the optimal teaching method based on the user profile" into the generative AI model, generating a curriculum suited to that profile.

[0373] The server then builds a customized dynamic curriculum based on the generated profile and sends it to the terminal. This curriculum generation process utilizes VR / AR technology as interactive learning materials to create an immersive learning environment where learners can connect theory and practice.

[0374] The terminal provides learners with educational content based on a dynamic curriculum provided by the server. The terminal is equipped with sensors that can track learners' movements and gaze in real time, allowing for monitoring of emotional responses during learning. This emotional data is continuously transmitted to the server and used to adjust the overall system.

[0375] Users progress through their learning using a curriculum delivered via their device. As users learn, the content is dynamically adjusted according to their emotional state, ensuring a consistently engaging learning experience. For example, if a user is feeling discouraged about learning, the device can identify this emotion through an emotion engine, and the server can adjust the content to improve motivation.

[0376] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0377] Step 1:

[0378] Users log in to the system via their device and enter basic information to begin learning. This includes information about their current learning subject, objectives, and mood. This input data is sent from the device to the server.

[0379] Step 2:

[0380] The server uses a generative AI model to analyze data based on basic information, cognitive data, and emotional data received from the user. Input data includes the user's past learning history and real-time emotional data. This data is analyzed to generate a profile tailored to the user's learning characteristics and emotional state. The output is a detailed profile that optimally reflects the user's characteristics.

[0381] Step 3:

[0382] The server generates a dynamic curriculum based on the generated profile. This curriculum is created by inputting the prompt "Suggest the optimal learning method based on the user profile" into the generating AI model. The input data is profile information, and the output is a detailed curriculum that outlines what learning content the user is interested in and how they should proceed with their learning.

[0383] Step 4:

[0384] The device provides users with learning content using VR / AR technology based on a customized curriculum received from the server. The input data is curriculum information from the server, and the device provides learning materials to the user according to that information. As output, the user can obtain an immersive learning experience.

[0385] Step 5:

[0386] The device monitors the user's emotional state in real time during the learning process and feeds this information back to the server. The input data consists of the user's biometric information and emotional responses during the learning process, which are sent to the server via the emotion engine. The output is the user's emotional data during the learning process.

[0387] Step 6:

[0388] The server adjusts the curriculum as needed based on emotional data received from the device. The input data is user emotional information processed through an emotion engine. Based on this information, a generative AI model creates optimal feedback and updates content to maintain the user's interest and motivation. As output, a new learning strategy and an adjusted curriculum are generated and sent to the device.

[0389] (Application Example 2)

[0390] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0391] In modern industry, workers are required to quickly and effectively acquire skills in complex machinery and technology. However, traditional methods often fail to adequately consider the individual cognitive characteristics and emotional states of learners, resulting in inefficient learning and increased stress. To address these challenges, there is a need for a system that provides an individualized learning environment and real-time feedback.

[0392] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0393] In this invention, the server includes means for collecting and analyzing the learner's cognitive functions, interests, learning style, and potential abilities; means for generating an adaptive curriculum optimized for the learner based on the analysis results; and means for sensing emotional states and displaying interactive content that takes the learner's psychological state into consideration in real time. This provides an immersive learning experience tailored to the individual needs and emotions of the learner, enabling efficient skill acquisition and stress reduction.

[0394] A "learner" is an individual who utilizes an educational system to acquire specific knowledge or skills.

[0395] "Cognitive function" refers to the brain's processes necessary to understand, analyze, and remember information.

[0396] "Interest" refers to the degree of interest or curiosity a learner has towards a particular topic or activity.

[0397] "Learning format" refers to a category of methods and approaches that enable learners to absorb information most effectively.

[0398] "Latent abilities" refer to skills and talents that learners have not yet manifested but have the potential to develop.

[0399] An "adaptive curriculum" is an educational plan that is dynamically adjusted based on the individual characteristics and emotional state of each learner.

[0400] "Emotional state" refers to the emotions and psychological responses that learners experience in a particular situation.

[0401] "Interactive content" refers to educational materials that are interactive, allowing learners to directly manipulate them and receive feedback.

[0402] An "immersive learning experience" is a learning environment designed to allow learners to become so deeply engrossed in learning activities that they forget their surroundings.

[0403] "Skill acquisition" is the process of acquiring the techniques and knowledge necessary to perform a specific job or task professionally.

[0404] The system that realizes this invention mainly consists of a server, a terminal, and a user. The server collects and analyzes the learner's cognitive functions, interests, learning style, and potential abilities. This uses an advanced data processing technique called a generative AI model. The generative AI model is built using software such as Python or TensorFlow and is capable of precisely identifying the learner's profile.

[0405] Based on the analysis, the server generates an adaptive curriculum optimized for the learner and sends it to the terminal. The terminal then uses VR / AR technology to provide the learner with an immersive learning environment based on the received curriculum. Furthermore, the terminal monitors the user's emotional state using an emotion engine and sends data back to the server in real time based on predetermined indicators. Building a VR / AR environment using Unity is recommended for the implementation of this terminal.

[0406] As a concrete example, consider a scenario where a user is learning to operate a new welding machine. The server analyzes the acquired emotional data in real time, and if the learner feels anxious, it sends interactive content tailored to that emotion to the user's device. The user receives visual guidelines through the device, enabling them to efficiently improve their skills.

[0407] An example of a prompt message is as follows: "Analyze real-time data from workers who want to improve their welding machine operation skills and provide an optimal training simulation."

[0408] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0409] Step 1:

[0410] The server receives cognitive and emotional state data submitted by the user. This input data includes the learner's cognitive functions, interests, learning style, and potential abilities. The server collects the data, performs initial analysis, and then sends it as input to a generative AI model after data cleansing and normalization.

[0411] Step 2:

[0412] The server uses a generative AI model to analyze the input data in detail and build learner profiles. This process identifies the characteristics of each learner through data calculations and outputs profile data. Specifically, it performs weighting based on each characteristic and organizes the analysis results.

[0413] Step 3:

[0414] The server generates an adaptive curriculum optimized for the learner based on profile data. This curriculum is dynamic, taking into account the user's current situation, and is intended for use in a VR / AR environment. The generated curriculum is sent to the device, enabling execution on the device.

[0415] Step 4:

[0416] The device builds an immersive learning environment using VR / AR technology based on an adaptive curriculum received from the server. This environment is designed using Unity and other tools, and is interactive. The device monitors the learner's emotional state in real time and sends this data to an emotion engine to obtain feedback.

[0417] Step 5:

[0418] Users immerse themselves in the learning environment provided through the device and perform actions. The device detects the user's responses and emotional state and sends data to the server as needed. The emotion engine returns the results of the user's psychological state analysis to the server, providing feedback for further curriculum adjustments.

[0419] Step 6:

[0420] The server receives feedback from the emotion engine and adjusts the curriculum accordingly. This allows it to provide relaxing content when the user is stressed, or adjust the difficulty level when the user is highly motivated.

[0421] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0422] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0423] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0424] [Third Embodiment]

[0425] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0426] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0427] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0428] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0429] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0430] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0431] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0432] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0433] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0434] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0435] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0436] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0437] This invention is a system that uses a generative AI model to analyze in detail a learner's cognitive abilities, interests, learning style, and potential talents, and generates a dynamic curriculum optimized for each individual learner. The system operates primarily through the cooperation of three parties: a server, a terminal, and a user.

[0438] The server first receives data sent by the user. This data includes basic profile information, past learning history, and information about the user's current interests and learning goals. The server uses this data to run a generative AI model and build a detailed profile of the user.

[0439] Next, the server selects the most suitable learning content for the user based on the constructed profile and generates a dynamic curriculum. This curriculum changes according to the learner's progress and provides a learning experience that connects theory and practice, particularly by incorporating immersive content using VR / AR technology.

[0440] The device displays learning content to learners in a VR / AR environment based on a dynamic curriculum provided by the server. This allows learners to engage in more intuitive and immersive learning. The device also plays a role in transmitting learning progress data to the server in real time.

[0441] Users can learn at their own pace while receiving feedback through their device. This feedback is generated by a server and provides appropriate advice and next steps based on the user's performance.

[0442] As a concrete example, suppose a user expresses interest in a history topic. The server analyzes the user's past learning patterns and interests and creates a curriculum that includes VR simulations of relevant historical events. The terminal provides the user with simulations based on this curriculum and sends the learning progress back to the server. Based on this data, the server suggests additional learning content or an improved curriculum tailored to the user's level of understanding.

[0443] In this way, by implementing the invention, it becomes possible to provide advanced educational services tailored to each individual learner.

[0444] The following describes the processing flow.

[0445] Step 1:

[0446] Users log in using their devices and answer questionnaires regarding their profile information and learning objectives. The collected data is then sent to the server.

[0447] Step 2:

[0448] Based on the received user data, the server uses a generative AI model to analyze the user's cognitive abilities, interests, learning style, and potential talents. As a result of the analysis, a detailed profile of the user's characteristics is generated.

[0449] Step 3:

[0450] Based on the user's profile, the server selects appropriate learning content and generates a dynamic curriculum. This curriculum is then organized into a learning plan incorporating the selected content.

[0451] Step 4:

[0452] The server sends the generated curriculum to the terminal, which then prepares to provide learners with immersive learning content in a VR / AR environment based on this curriculum.

[0453] Step 5:

[0454] Users progress through their learning using the provided VR / AR content via their device. Learning progress and interaction data are continuously transmitted to the server by the device.

[0455] Step 6:

[0456] The server evaluates the learner's performance based on the received progress data and generates real-time feedback. This feedback is used to adjust the learning content according to the user's level of understanding.

[0457] Step 7:

[0458] The server makes any necessary adjustments to the learning curriculum and provides the next learning step to the terminal. The terminal then presents the adjusted curriculum to the user and continues to support their learning.

[0459] Step 8:

[0460] Users receive feedback from the server and use it to improve their job performance by checking their learning progress and identifying the next steps they should take.

[0461] (Example 1)

[0462] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0463] Providing an optimal educational experience tailored to individual learners is difficult, and at the same time, there are challenges in appropriately tracking learners' progress and immediately adjusting learning content based on their understanding and abilities. Furthermore, it is difficult for learners to receive timely feedback while progressing at their own pace.

[0464] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0465] In this invention, the server includes means for collecting and analyzing a learner's cognitive abilities, interests, learning methods, and potential abilities; means for generating a dynamic instruction plan optimized for the learner based on the analysis results; and means for providing an experiential learning environment based on the generated instruction plan. This makes it possible to provide an individually optimized educational experience tailored to each learner, and to establish real-time feedback and an effective learning process.

[0466] A "learner" refers to an individual who is the target of acquiring knowledge and skills in educational activities.

[0467] "Cognitive ability" refers to the ability to understand, remember, analyze, and evaluate information, and is a characteristic that plays an important role in learners' intellectual activities.

[0468] "Interest" refers to the degree of interest or curiosity a learner has in a particular field or activity.

[0469] "Learning method" refers to the specific style or approach that learners use when acquiring information.

[0470] "Latent ability" refers to the abilities and talents that learners possess that are not yet apparent but have the potential to be demonstrated.

[0471] A "dynamic teaching plan" refers to the design of an educational curriculum that is updated as needed based on the learners' learning progress and level of understanding.

[0472] An "experiential learning environment" refers to a learning environment that utilizes technologies such as virtual reality and augmented reality, allowing learners to learn through actual experience.

[0473] "Real-time tracking" refers to the process of instantly monitoring a learner's learning progress and recording it as data.

[0474] "Feedback" refers to the evaluation and guidance provided for improvement regarding a learner's learning activities.

[0475] A "generative AI model" refers to an artificial intelligence system that uses machine learning algorithms to make predictions and perform analyses based on data.

[0476] This invention presents an information processing system that effectively collects and analyzes learners' cognitive abilities, interests, learning methods, and potential abilities. This system operates through the coordinated efforts of a server, a terminal, and a user.

[0477] The server first receives data sent by the user. This includes the user's basic information, past learning history, interests, and learning objectives. The received data is stored in a database on the server and then analyzed by a generative AI model. This generative AI model is an artificial intelligence system that implements common machine learning algorithms. For example, natural language processing models used in text generation and data analysis can be applied.

[0478] Next, the server generates a dynamic instruction plan optimized for the user based on the analysis results. This instruction plan is adjusted according to the learner's progress and is designed to provide an experiential learning environment, particularly by utilizing virtual reality and augmented reality technologies. In this process, hardware such as VR devices and dedicated software environments are used to generate simulations and interactive content.

[0479] The terminal receives the lesson plan provided by the server and displays it to the user. Learners can progress through the lesson plan on the terminal and reinforce their knowledge through hands-on experience. The terminal also supports the user's learning progress and provides real-time feedback on their progress to the server.

[0480] Users utilize the system at their own pace and complete the necessary learning. They receive feedback from their device, allowing them to adjust their learning methods and next steps as needed. For example, a user interested in history can experience a VR simulation themed around the French Revolution and learn about its historical context through the storyline.

[0481] As an example of a prompt, by sending the following text to the AI ​​model, "Generate a VR simulation to teach about the historical importance of a certain era. Target user's learning history: detailed history information, area of ​​interest: history, current level of understanding: intermediate," a relevant learning plan can be constructed.

[0482] Thus, by implementing the present invention, it becomes possible to provide learners with individually optimized educational experiences and to realize independent and efficient learning.

[0483] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0484] Step 1:

[0485] Users enter basic information, past learning history, current interests, and learning objectives via their device. This input data includes age, past subjects studied, and target skills. This data is then sent to the server.

[0486] Step 2:

[0487] The server stores the data received from the user in a database. Next, the stored data is input into a generating AI model to create a detailed profile of the user's cognitive abilities and learning style. Natural language processing techniques are used to analyze the data. The output is profile information that describes the user's characteristics in detail.

[0488] Step 3:

[0489] The server generates a dynamic lesson plan based on the generated profile information. This is the process of creating a curriculum customized to the user's level of understanding, interests, and learning style. The prompt poses the question to the AI ​​model: "Suggest how to design the optimal learning experience based on the user's areas of interest." The output is a lesson plan that includes a learning environment using virtual reality and augmented reality technologies.

[0490] Step 4:

[0491] The terminal receives the lesson plan provided by the server and displays it to the user. The terminal controls a VR device to provide learners with immersive simulation content. The user can perform operations such as starting and ending the simulation.

[0492] Step 5:

[0493] The device records the user's actions and inputs during the learning session. It collects data such as eye-tracking and selected options, and sends this data to the server in real time. The input here is the user's actions and progress, and the output is the tracked user data.

[0494] Step 6:

[0495] The server analyzes the transmitted data and evaluates the user's performance. It then generates feedback based on the evaluation, determining suggestions for improvement and next learning tasks. The output is a feedback message presented to the user.

[0496] Step 7:

[0497] Users receive feedback displayed on their device and adjust their learning plan accordingly. As a result, learners can understand their own level of comprehension and choose to relearn or tackle new challenges as needed.

[0498] (Application Example 1)

[0499] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0500] When consumers shop online, it is difficult to efficiently find products that match their interests and style from a vast selection of goods. Furthermore, the lack of personalized shopping experiences makes improving consumer satisfaction a challenge.

[0501] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0502] In this invention, the server includes means for collecting and analyzing a consumer's interests, purchase history, and style; means for generating a dynamic product suggestion list optimized for the consumer based on the analysis results; and means for providing a virtual shopping environment based on the generated product suggestions. This enables consumers to effectively find products that are best suited to them and to try them out in a virtual environment.

[0503] "Consumer" refers to an individual or group that purchases goods or services.

[0504] "Interest" refers to the degree of interest an individual consumer has in a particular product or service.

[0505] "Purchase history" refers to a record of goods and services that a consumer has purchased in the past.

[0506] "Style" refers to the tendency of consumers to choose products and services based on their preferences and tastes.

[0507] A "dynamic product suggestion list" refers to a list that suggests optimized products and services based on consumers' interests, purchase history, and style.

[0508] A "virtual shopping environment" refers to a virtual purchasing space where consumers can browse products online and try them out interactively as needed.

[0509] One embodiment of this invention is a system that uses a generative AI model to process data in order to understand consumer purchasing behavior and provide an optimal shopping experience. This system consists of a server, terminals, and users.

[0510] First, the server collects data on consumers' interests, purchase history, and style, and uses this data to build a detailed consumer profile using a generative AI model. The server then uses machine learning frameworks such as TensorFlow to generate a dynamic product suggestion list based on the profile. This list allows consumers to quickly find products that match their specific interests and style.

[0511] Next, the device functions as a wearable device such as smart glasses, providing consumers with a virtual shopping environment. The device utilizes a game engine like Unity to allow consumers to visually try out products in a virtual space. It also transmits consumer behavior data to a server in real time to support the updating of product suggestion lists.

[0512] Users can browse and try out products offered within the virtual shopping environment. Furthermore, based on user feedback, the server automatically adjusts subsequent recommendation lists to improve the next shopping experience.

[0513] For example, if a consumer is looking for new sports shoes, they can enter a virtual store wearing smart glasses, and recommended shoes that match their previously purchased running gear and style will be displayed. The generative AI model makes these suggestions based on the consumer's interests and past purchasing patterns.

[0514] An example of a prompt is as follows: "Please recommend shoes from our new summer sports collection to a male customer in his 20s who enjoys running as a hobby."

[0515] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0516] Step 1:

[0517] The server receives data from consumers regarding their interests, purchase history, and style. This input data is collected, irrelevant information is filtered, and essential features are extracted. This enables the construction of accurate consumer profiles.

[0518] Step 2:

[0519] The server uses a generative AI model to build consumer profiles based on the received data. Specifically, it analyzes the data using TensorFlow to generate profiles that reflect consumers' interests, styles, and past purchasing behavior. As a result of profile generation, prompts are created for product recommendations tailored to each consumer.

[0520] Step 3:

[0521] The server generates a dynamic product suggestion list optimized for the consumer based on the generated profile. In this step, relevant product information is retrieved from the database using the prompts mentioned above. The AI ​​model selects and ranks relevant products and outputs them as an optimal suggestion list.

[0522] Step 4:

[0523] The terminal uses a dynamic product suggestion list received from the server to build a virtual shopping environment. In this process, it uses the Unity engine to render 3D models, providing an interface that allows users to visually try out products. This environment is designed to have broad compatibility with user interactions.

[0524] Step 5:

[0525] Users enter a virtual shopping environment on their device and select items of interest from the presented products. During this process, the system tracks the user's gaze and selection history, collecting data to improve future recommendations. After the trial, users provide feedback and evaluation information.

[0526] Step 6:

[0527] The device sends user feedback to the server, which then uses it to improve the product suggestion list. This feedback data is used as input to rerun the generative AI model and update the consumer profile and product suggestion list. This iterative cycle leads to an improved consumer experience.

[0528] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0529] This invention is an educational system that combines a generative AI model and an emotion engine to provide an optimized learning experience by analyzing in detail the learner's cognitive abilities, interests, learning style, potential talents, and emotional state. The system operates by efficiently exchanging information among three parties: the server, the terminal, and the user.

[0530] The server first receives cognitive and emotional data sent from the user. This includes information about the user's basic characteristics and ongoing learning activities, as well as emotional state data collected by the emotion engine. The server processes this data using a generative AI model to build a detailed profile tailored to the user's individual characteristics.

[0531] Subsequently, the server selects and organizes learning content optimized for the user based on the generated profile and emotional state, generating a dynamic curriculum. The curriculum is structured to take the learner's psychological state into account and to evoke positive emotions. In particular, it enables an immersive learning experience for the user by leveraging VR / AR technology to connect theory and practice.

[0532] The device provides learners with learning materials in a VR / AR environment, following a dynamic curriculum received from the server. The device also monitors the user's emotional responses during learning via an emotion engine and sends that data back to the server in real time.

[0533] Users progress through their learning using learning content provided via their devices. The learning experience is adjusted in a timely manner based on the user's current emotional state. For example, if a user is feeling discouraged with their learning, the server receives feedback from the emotion engine, adjusts the curriculum, and sends content and encouraging messages to motivate the user.

[0534] For example, if a user shows signs of frustration while learning a math curriculum, the device detects this change in emotion through its emotion engine. The server immediately receives this data and responds by restructuring the curriculum to provide interactive and visual learning materials to calm the learner.

[0535] In this way, this system, which integrates an emotional engine, can unlock learners' potential while providing personalized learning experiences that respond to their emotions.

[0536] The following describes the processing flow.

[0537] Step 1:

[0538] Users log in using their devices and fill out questionnaires about their profile information and learning goals. Simultaneously, user emotion data is collected through sensors such as the device's camera and microphone.

[0539] Step 2:

[0540] The device sends the collected user profile information and emotional data to the server. This includes emotional information derived from the user's facial expressions and tone of voice.

[0541] Step 3:

[0542] The server uses a generative AI model to analyze the received data. This analyzes the user's cognitive abilities, interests, learning style, potential talents, and current emotional state, generating a detailed profile.

[0543] Step 4:

[0544] Based on profile results and sentiment data, the server selects the most suitable learning content for the user and creates a dynamic curriculum. This curriculum optimizes the order and format of the content, taking the user's emotions into consideration.

[0545] Step 5:

[0546] The server sends the generated curriculum to the terminal, which then prepares to provide learning content to the learner in a VR / AR environment based on this curriculum.

[0547] Step 6:

[0548] Users engage in personalized learning using content provided through their devices. During learning, the emotion engine continuously monitors the user's emotions and sends emotion data to the server in real time as needed.

[0549] Step 7:

[0550] The server evaluates the user's learning progress based on sentiment data and generates real-time feedback. The feedback is designed to maintain or improve the user's motivation and adjusts the learning content as needed.

[0551] Step 8:

[0552] The server provides the adjusted curriculum to the terminal, and the terminal continues to present it to the user, thereby facilitating a smooth learning experience.

[0553] Step 9:

[0554] Users can use the feedback provided by the server to advance their learning and improve their abilities and acquire new skills.

[0555] (Example 2)

[0556] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0557] Traditional education systems face the challenge of providing individualized learning experiences that take into account each learner's cognitive characteristics and emotional state. As a result, many learners are unable to maximize their learning efficiency within a standardized curriculum-based learning environment and tend to lose interest in learning. Furthermore, the lack of sufficient real-time feedback and adjustments to learning content based on emotional responses limits the ability to improve learner motivation and unlock their potential.

[0558] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0559] In this invention, the server includes means for collecting and analyzing the learner's cognitive abilities, interests, learning style, potential talents, and emotional state; means for generating a dynamic curriculum optimized for the learner based on the analysis results using a generative AI model; and means for providing an immersive learning environment based on the generated curriculum using VR / AR technology. This enables the provision of a personalized learning experience in real time that is tailored to the learner's individual characteristics and emotional state, thereby improving learning efficiency and maintaining motivation.

[0560] A "learner" is an individual who acquires knowledge and skills through the use of an educational system.

[0561] "Cognitive ability" refers to intellectual functions such as a learner's comprehension, memory, and thinking skills.

[0562] "Interest" refers to the degree to which a learner shows interest in a particular topic or activity.

[0563] "Learning style" refers to the tendencies and methods by which learners most effectively understand and process information.

[0564] "Latent talent" refers to untapped abilities or qualities that a learner may possess in a particular field or activity.

[0565] "Emotional state" refers to the learner's emotional situation or changes in mood.

[0566] A "generative AI model" refers to an artificial intelligence algorithm that performs predictions and generation based on large amounts of data.

[0567] A "dynamic curriculum" is a learning plan and material structure that is adjusted in real time according to the characteristics and needs of the learners.

[0568] "VR / AR technology" is a technology that uses virtual reality and augmented reality to provide information visually.

[0569] An "immersive learning environment" is a highly interactive learning space designed to allow learners to concentrate and participate in learning activities.

[0570] "Feedback" refers to evaluations and advice provided based on a learner's learning activities and emotional state.

[0571] This invention is an educational system designed to provide learners with personalized learning experiences. The central elements of the invention are a server integrating a generative AI model and an emotion engine, and terminals that receive learning content from this server. The entire system is designed to provide an interactive and immersive learning environment.

[0572] The server collects data submitted by learners and analyzes their cognitive abilities, interests, learning styles, potential talents, and emotional states. This process utilizes a generative AI model, and data analysis tools such as Python and R are used for data analysis. This generates a detailed profile based on the learner's characteristics. For example, the server inputs a prompt such as "Suggest the optimal teaching method based on the user profile" into the generative AI model, generating a curriculum suited to that profile.

[0573] The server then builds a customized dynamic curriculum based on the generated profile and sends it to the terminal. This curriculum generation process utilizes VR / AR technology as interactive learning materials to create an immersive learning environment where learners can connect theory and practice.

[0574] The terminal provides learners with educational content based on a dynamic curriculum provided by the server. The terminal is equipped with sensors that can track learners' movements and gaze in real time, allowing for monitoring of emotional responses during learning. This emotional data is continuously transmitted to the server and used to adjust the overall system.

[0575] Users progress through their learning using a curriculum delivered via their device. As users learn, the content is dynamically adjusted according to their emotional state, ensuring a consistently engaging learning experience. For example, if a user is feeling discouraged about learning, the device can identify this emotion through an emotion engine, and the server can adjust the content to improve motivation.

[0576] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0577] Step 1:

[0578] Users log in to the system via their device and enter basic information to begin learning. This includes information about their current learning subject, objectives, and mood. This input data is sent from the device to the server.

[0579] Step 2:

[0580] The server uses a generative AI model to analyze data based on basic information, cognitive data, and emotional data received from the user. Input data includes the user's past learning history and real-time emotional data. This data is analyzed to generate a profile tailored to the user's learning characteristics and emotional state. The output is a detailed profile that optimally reflects the user's characteristics.

[0581] Step 3:

[0582] The server generates a dynamic curriculum based on the generated profile. This curriculum is created by inputting the prompt "Suggest the optimal learning method based on the user profile" into the generating AI model. The input data is profile information, and the output is a detailed curriculum that outlines what learning content the user is interested in and how they should proceed with their learning.

[0583] Step 4:

[0584] The device provides users with learning content using VR / AR technology based on a customized curriculum received from the server. The input data is curriculum information from the server, and the device provides learning materials to the user according to that information. As output, the user can obtain an immersive learning experience.

[0585] Step 5:

[0586] The device monitors the user's emotional state in real time during the learning process and feeds this information back to the server. The input data consists of the user's biometric information and emotional responses during the learning process, which are sent to the server via the emotion engine. The output is the user's emotional data during the learning process.

[0587] Step 6:

[0588] The server adjusts the curriculum as needed based on emotional data received from the device. The input data is user emotional information processed through an emotion engine. Based on this information, a generative AI model creates optimal feedback and updates content to maintain the user's interest and motivation. As output, a new learning strategy and an adjusted curriculum are generated and sent to the device.

[0589] (Application Example 2)

[0590] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0591] In modern industry, workers are required to quickly and effectively acquire skills in complex machinery and technology. However, traditional methods often fail to adequately consider the individual cognitive characteristics and emotional states of learners, resulting in inefficient learning and increased stress. To address these challenges, there is a need for a system that provides an individualized learning environment and real-time feedback.

[0592] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0593] In this invention, the server includes means for collecting and analyzing the learner's cognitive functions, interests, learning style, and potential abilities; means for generating an adaptive curriculum optimized for the learner based on the analysis results; and means for sensing emotional states and displaying interactive content that takes the learner's psychological state into consideration in real time. This provides an immersive learning experience tailored to the individual needs and emotions of the learner, enabling efficient skill acquisition and stress reduction.

[0594] A "learner" is an individual who utilizes an educational system to acquire specific knowledge or skills.

[0595] "Cognitive function" refers to the brain's processes necessary to understand, analyze, and remember information.

[0596] "Interest" refers to the degree of interest or curiosity a learner has towards a particular topic or activity.

[0597] "Learning format" refers to a category of methods and approaches that enable learners to absorb information most effectively.

[0598] "Latent abilities" refer to skills and talents that learners have not yet manifested but have the potential to develop.

[0599] An "adaptive curriculum" is an educational plan that is dynamically adjusted based on the individual characteristics and emotional state of each learner.

[0600] "Emotional state" refers to the emotions and psychological responses that learners experience in a particular situation.

[0601] "Interactive content" refers to educational materials that are interactive, allowing learners to directly manipulate them and receive feedback.

[0602] An "immersive learning experience" is a learning environment designed to allow learners to become so deeply engrossed in learning activities that they forget their surroundings.

[0603] "Skill acquisition" is the process of acquiring the techniques and knowledge necessary to perform a specific job or task professionally.

[0604] The system that realizes this invention mainly consists of a server, a terminal, and a user. The server collects and analyzes the learner's cognitive functions, interests, learning style, and potential abilities. This uses an advanced data processing technique called a generative AI model. The generative AI model is built using software such as Python or TensorFlow and is capable of precisely identifying the learner's profile.

[0605] Based on the analysis, the server generates an adaptive curriculum optimized for the learner and sends it to the terminal. The terminal then uses VR / AR technology to provide the learner with an immersive learning environment based on the received curriculum. Furthermore, the terminal monitors the user's emotional state using an emotion engine and sends data back to the server in real time based on predetermined indicators. Building a VR / AR environment using Unity is recommended for the implementation of this terminal.

[0606] As a concrete example, consider a scenario where a user is learning to operate a new welding machine. The server analyzes the acquired emotional data in real time, and if the learner feels anxious, it sends interactive content tailored to that emotion to the user's device. The user receives visual guidelines through the device, enabling them to efficiently improve their skills.

[0607] An example of a prompt message is as follows: "Analyze real-time data from workers who want to improve their welding machine operation skills and provide an optimal training simulation."

[0608] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0609] Step 1:

[0610] The server receives cognitive and emotional state data submitted by the user. This input data includes the learner's cognitive functions, interests, learning style, and potential abilities. The server collects the data, performs initial analysis, and then sends it as input to a generative AI model after data cleansing and normalization.

[0611] Step 2:

[0612] The server uses a generative AI model to analyze the input data in detail and build learner profiles. This process identifies the characteristics of each learner through data calculations and outputs profile data. Specifically, it performs weighting based on each characteristic and organizes the analysis results.

[0613] Step 3:

[0614] The server generates an adaptive curriculum optimized for the learner based on profile data. This curriculum is dynamic, taking into account the user's current situation, and is intended for use in a VR / AR environment. The generated curriculum is sent to the device, enabling execution on the device.

[0615] Step 4:

[0616] The device builds an immersive learning environment using VR / AR technology based on an adaptive curriculum received from the server. This environment is designed using Unity and other tools, and is interactive. The device monitors the learner's emotional state in real time and sends this data to an emotion engine to obtain feedback.

[0617] Step 5:

[0618] Users immerse themselves in the learning environment provided through the device and perform actions. The device detects the user's responses and emotional state and sends data to the server as needed. The emotion engine returns the results of the user's psychological state analysis to the server, providing feedback for further curriculum adjustments.

[0619] Step 6:

[0620] The server receives feedback from the emotion engine and adjusts the curriculum accordingly. This allows it to provide relaxing content when the user is stressed, or adjust the difficulty level when the user is highly motivated.

[0621] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0622] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0623] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0624] [Fourth Embodiment]

[0625] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0626] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0627] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0628] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0629] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0630] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0631] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0632] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0633] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0634] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0635] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0636] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0637] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0638] This invention is a system that uses a generative AI model to analyze in detail a learner's cognitive abilities, interests, learning style, and potential talents, and generates a dynamic curriculum optimized for each individual learner. The system operates primarily through the cooperation of three parties: a server, a terminal, and a user.

[0639] The server first receives data sent by the user. This data includes basic profile information, past learning history, and information about the user's current interests and learning goals. The server uses this data to run a generative AI model and build a detailed profile of the user.

[0640] Next, the server selects the most suitable learning content for the user based on the constructed profile and generates a dynamic curriculum. This curriculum changes according to the learner's progress and provides a learning experience that connects theory and practice, particularly by incorporating immersive content using VR / AR technology.

[0641] The device displays learning content to learners in a VR / AR environment based on a dynamic curriculum provided by the server. This allows learners to engage in more intuitive and immersive learning. The device also plays a role in transmitting learning progress data to the server in real time.

[0642] Users can learn at their own pace while receiving feedback through their device. This feedback is generated by a server and provides appropriate advice and next steps based on the user's performance.

[0643] As a concrete example, suppose a user expresses interest in a history topic. The server analyzes the user's past learning patterns and interests and creates a curriculum that includes VR simulations of relevant historical events. The terminal provides the user with simulations based on this curriculum and sends the learning progress back to the server. Based on this data, the server suggests additional learning content or an improved curriculum tailored to the user's level of understanding.

[0644] In this way, by implementing the invention, it becomes possible to provide advanced educational services tailored to each individual learner.

[0645] The following describes the processing flow.

[0646] Step 1:

[0647] Users log in using their devices and answer questionnaires regarding their profile information and learning objectives. The collected data is then sent to the server.

[0648] Step 2:

[0649] Based on the received user data, the server uses a generative AI model to analyze the user's cognitive abilities, interests, learning style, and potential talents. As a result of the analysis, a detailed profile of the user's characteristics is generated.

[0650] Step 3:

[0651] Based on the user's profile, the server selects appropriate learning content and generates a dynamic curriculum. This curriculum is then organized into a learning plan incorporating the selected content.

[0652] Step 4:

[0653] The server sends the generated curriculum to the terminal, which then prepares to provide learners with immersive learning content in a VR / AR environment based on this curriculum.

[0654] Step 5:

[0655] Users progress through their learning using the provided VR / AR content via their device. Learning progress and interaction data are continuously transmitted to the server by the device.

[0656] Step 6:

[0657] The server evaluates the learner's performance based on the received progress data and generates real-time feedback. This feedback is used to adjust the learning content according to the user's level of understanding.

[0658] Step 7:

[0659] The server makes any necessary adjustments to the learning curriculum and provides the next learning step to the terminal. The terminal then presents the adjusted curriculum to the user and continues to support their learning.

[0660] Step 8:

[0661] Users receive feedback from the server and use it to improve their job performance by checking their learning progress and identifying the next steps they should take.

[0662] (Example 1)

[0663] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0664] Providing an optimal educational experience tailored to individual learners is difficult, and at the same time, there are challenges in appropriately tracking learners' progress and immediately adjusting learning content based on their understanding and abilities. Furthermore, it is difficult for learners to receive timely feedback while progressing at their own pace.

[0665] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0666] In this invention, the server includes means for collecting and analyzing a learner's cognitive abilities, interests, learning methods, and potential abilities; means for generating a dynamic instruction plan optimized for the learner based on the analysis results; and means for providing an experiential learning environment based on the generated instruction plan. This makes it possible to provide an individually optimized educational experience tailored to each learner, and to establish real-time feedback and an effective learning process.

[0667] A "learner" refers to an individual who is the target of acquiring knowledge and skills in educational activities.

[0668] "Cognitive ability" refers to the ability to understand, remember, analyze, and evaluate information, and is a characteristic that plays an important role in learners' intellectual activities.

[0669] "Interest" refers to the degree of interest or curiosity a learner has in a particular field or activity.

[0670] "Learning method" refers to the specific style or approach that learners use when acquiring information.

[0671] "Latent ability" refers to the abilities and talents that learners possess that are not yet apparent but have the potential to be demonstrated.

[0672] A "dynamic teaching plan" refers to the design of an educational curriculum that is updated as needed based on the learners' learning progress and level of understanding.

[0673] An "experiential learning environment" refers to a learning environment that utilizes technologies such as virtual reality and augmented reality, allowing learners to learn through actual experience.

[0674] "Real-time tracking" refers to the process of instantly monitoring a learner's learning progress and recording it as data.

[0675] "Feedback" refers to the evaluation and guidance provided for improvement regarding a learner's learning activities.

[0676] A "generative AI model" refers to an artificial intelligence system that uses machine learning algorithms to make predictions and perform analyses based on data.

[0677] This invention presents an information processing system that effectively collects and analyzes learners' cognitive abilities, interests, learning methods, and potential abilities. This system operates through the coordinated efforts of a server, a terminal, and a user.

[0678] The server first receives data sent by the user. This includes the user's basic information, past learning history, interests, and learning objectives. The received data is stored in a database on the server and then analyzed by a generative AI model. This generative AI model is an artificial intelligence system that implements common machine learning algorithms. For example, natural language processing models used in text generation and data analysis can be applied.

[0679] Next, the server generates a dynamic instruction plan optimized for the user based on the analysis results. This instruction plan is adjusted according to the learner's progress and is designed to provide an experiential learning environment, particularly by utilizing virtual reality and augmented reality technologies. In this process, hardware such as VR devices and dedicated software environments are used to generate simulations and interactive content.

[0680] The terminal receives the lesson plan provided by the server and displays it to the user. Learners can progress through the lesson plan on the terminal and reinforce their knowledge through hands-on experience. The terminal also supports the user's learning progress and provides real-time feedback on their progress to the server.

[0681] Users utilize the system at their own pace and complete the necessary learning. They receive feedback from their device, allowing them to adjust their learning methods and next steps as needed. For example, a user interested in history can experience a VR simulation themed around the French Revolution and learn about its historical context through the storyline.

[0682] As an example of a prompt, by sending the following text to the AI ​​model, "Generate a VR simulation to teach about the historical importance of a certain era. Target user's learning history: detailed history information, area of ​​interest: history, current level of understanding: intermediate," a relevant learning plan can be constructed.

[0683] Thus, by implementing the present invention, it becomes possible to provide learners with individually optimized educational experiences and to realize independent and efficient learning.

[0684] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0685] Step 1:

[0686] Users enter basic information, past learning history, current interests, and learning objectives via their device. This input data includes age, past subjects studied, and target skills. This data is then sent to the server.

[0687] Step 2:

[0688] The server stores the data received from the user in a database. Next, the stored data is input into a generating AI model to create a detailed profile of the user's cognitive abilities and learning style. Natural language processing techniques are used to analyze the data. The output is profile information that describes the user's characteristics in detail.

[0689] Step 3:

[0690] The server generates a dynamic lesson plan based on the generated profile information. This is the process of creating a curriculum customized to the user's level of understanding, interests, and learning style. The prompt poses the question to the AI ​​model: "Suggest how to design the optimal learning experience based on the user's areas of interest." The output is a lesson plan that includes a learning environment using virtual reality and augmented reality technologies.

[0691] Step 4:

[0692] The terminal receives the lesson plan provided by the server and displays it to the user. The terminal controls a VR device to provide learners with immersive simulation content. The user can perform operations such as starting and ending the simulation.

[0693] Step 5:

[0694] The device records the user's actions and inputs during the learning session. It collects data such as eye-tracking and selected options, and sends this data to the server in real time. The input here is the user's actions and progress, and the output is the tracked user data.

[0695] Step 6:

[0696] The server analyzes the transmitted data and evaluates the user's performance. It then generates feedback based on the evaluation, determining suggestions for improvement and next learning tasks. The output is a feedback message presented to the user.

[0697] Step 7:

[0698] Users receive feedback displayed on their device and adjust their learning plan accordingly. As a result, learners can understand their own level of comprehension and choose to relearn or tackle new challenges as needed.

[0699] (Application Example 1)

[0700] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0701] When consumers shop online, it is difficult to efficiently find products that match their interests and style from a vast selection of goods. Furthermore, the lack of personalized shopping experiences makes improving consumer satisfaction a challenge.

[0702] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0703] In this invention, the server includes means for collecting and analyzing a consumer's interests, purchase history, and style; means for generating a dynamic product suggestion list optimized for the consumer based on the analysis results; and means for providing a virtual shopping environment based on the generated product suggestions. This enables consumers to effectively find products that are best suited to them and to try them out in a virtual environment.

[0704] "Consumer" refers to an individual or group that purchases goods or services.

[0705] "Interest" refers to the degree of interest an individual consumer has in a particular product or service.

[0706] "Purchase history" refers to a record of goods and services that a consumer has purchased in the past.

[0707] "Style" refers to the tendency of consumers to choose products and services based on their preferences and tastes.

[0708] A "dynamic product suggestion list" refers to a list that suggests optimized products and services based on consumers' interests, purchase history, and style.

[0709] A "virtual shopping environment" refers to a virtual purchasing space where consumers can browse products online and try them out interactively as needed.

[0710] One embodiment of this invention is a system that uses a generative AI model to process data in order to understand consumer purchasing behavior and provide an optimal shopping experience. This system consists of a server, terminals, and users.

[0711] First, the server collects data on consumers' interests, purchase history, and style, and uses this data to build a detailed consumer profile using a generative AI model. The server then uses machine learning frameworks such as TensorFlow to generate a dynamic product suggestion list based on the profile. This list allows consumers to quickly find products that match their specific interests and style.

[0712] Next, the device functions as a wearable device such as smart glasses, providing consumers with a virtual shopping environment. The device utilizes a game engine like Unity to allow consumers to visually try out products in a virtual space. It also transmits consumer behavior data to a server in real time to support the updating of product suggestion lists.

[0713] Users can browse and try out products offered within the virtual shopping environment. Furthermore, based on user feedback, the server automatically adjusts subsequent recommendation lists to improve the next shopping experience.

[0714] For example, if a consumer is looking for new sports shoes, they can enter a virtual store wearing smart glasses, and recommended shoes that match their previously purchased running gear and style will be displayed. The generative AI model makes these suggestions based on the consumer's interests and past purchasing patterns.

[0715] An example of a prompt is as follows: "Please recommend shoes from our new summer sports collection to a male customer in his 20s who enjoys running as a hobby."

[0716] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0717] Step 1:

[0718] The server receives data from consumers regarding their interests, purchase history, and style. This input data is collected, irrelevant information is filtered, and essential features are extracted. This enables the construction of accurate consumer profiles.

[0719] Step 2:

[0720] The server uses a generative AI model to build consumer profiles based on the received data. Specifically, it analyzes the data using TensorFlow to generate profiles that reflect consumers' interests, styles, and past purchasing behavior. As a result of profile generation, prompts are created for product recommendations tailored to each consumer.

[0721] Step 3:

[0722] The server generates a dynamic product suggestion list optimized for the consumer based on the generated profile. In this step, relevant product information is retrieved from the database using the prompts mentioned above. The AI ​​model selects and ranks relevant products and outputs them as an optimal suggestion list.

[0723] Step 4:

[0724] The terminal uses a dynamic product suggestion list received from the server to build a virtual shopping environment. In this process, it uses the Unity engine to render 3D models, providing an interface that allows users to visually try out products. This environment is designed to have broad compatibility with user interactions.

[0725] Step 5:

[0726] Users enter a virtual shopping environment on their device and select items of interest from the presented products. During this process, the system tracks the user's gaze and selection history, collecting data to improve future recommendations. After the trial, users provide feedback and evaluation information.

[0727] Step 6:

[0728] The device sends user feedback to the server, which then uses it to improve the product suggestion list. This feedback data is used as input to rerun the generative AI model and update the consumer profile and product suggestion list. This iterative cycle leads to an improved consumer experience.

[0729] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0730] This invention is an educational system that combines a generative AI model and an emotion engine to provide an optimized learning experience by analyzing in detail the learner's cognitive abilities, interests, learning style, potential talents, and emotional state. The system operates by efficiently exchanging information among three parties: the server, the terminal, and the user.

[0731] The server first receives cognitive and emotional data sent from the user. This includes information about the user's basic characteristics and ongoing learning activities, as well as emotional state data collected by the emotion engine. The server processes this data using a generative AI model to build a detailed profile tailored to the user's individual characteristics.

[0732] Subsequently, the server selects and organizes learning content optimized for the user based on the generated profile and emotional state, generating a dynamic curriculum. The curriculum is structured to take the learner's psychological state into account and to evoke positive emotions. In particular, it enables an immersive learning experience for the user by leveraging VR / AR technology to connect theory and practice.

[0733] The device provides learners with learning materials in a VR / AR environment, following a dynamic curriculum received from the server. The device also monitors the user's emotional responses during learning via an emotion engine and sends that data back to the server in real time.

[0734] Users progress through their learning using learning content provided via their devices. The learning experience is adjusted in a timely manner based on the user's current emotional state. For example, if a user is feeling discouraged with their learning, the server receives feedback from the emotion engine, adjusts the curriculum, and sends content and encouraging messages to motivate the user.

[0735] For example, if a user shows signs of frustration while learning a math curriculum, the device detects this change in emotion through its emotion engine. The server immediately receives this data and responds by restructuring the curriculum to provide interactive and visual learning materials to calm the learner.

[0736] In this way, this system, which integrates an emotional engine, can unlock learners' potential while providing personalized learning experiences that respond to their emotions.

[0737] The following describes the processing flow.

[0738] Step 1:

[0739] Users log in using their devices and fill out questionnaires about their profile information and learning goals. Simultaneously, user emotion data is collected through sensors such as the device's camera and microphone.

[0740] Step 2:

[0741] The device sends the collected user profile information and emotional data to the server. This includes emotional information derived from the user's facial expressions and tone of voice.

[0742] Step 3:

[0743] The server uses a generative AI model to analyze the received data. This analyzes the user's cognitive abilities, interests, learning style, potential talents, and current emotional state, generating a detailed profile.

[0744] Step 4:

[0745] Based on profile results and sentiment data, the server selects the most suitable learning content for the user and creates a dynamic curriculum. This curriculum optimizes the order and format of the content, taking the user's emotions into consideration.

[0746] Step 5:

[0747] The server sends the generated curriculum to the terminal, which then prepares to provide learning content to the learner in a VR / AR environment based on this curriculum.

[0748] Step 6:

[0749] Users engage in personalized learning using content provided through their devices. During learning, the emotion engine continuously monitors the user's emotions and sends emotion data to the server in real time as needed.

[0750] Step 7:

[0751] The server evaluates the user's learning progress based on sentiment data and generates real-time feedback. The feedback is designed to maintain or improve the user's motivation and adjusts the learning content as needed.

[0752] Step 8:

[0753] The server provides the adjusted curriculum to the terminal, and the terminal continues to present it to the user, thereby facilitating a smooth learning experience.

[0754] Step 9:

[0755] Users can use the feedback provided by the server to advance their learning and improve their abilities and acquire new skills.

[0756] (Example 2)

[0757] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0758] Traditional education systems face the challenge of providing individualized learning experiences that take into account each learner's cognitive characteristics and emotional state. As a result, many learners are unable to maximize their learning efficiency within a standardized curriculum-based learning environment and tend to lose interest in learning. Furthermore, the lack of sufficient real-time feedback and adjustments to learning content based on emotional responses limits the ability to improve learner motivation and unlock their potential.

[0759] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0760] In this invention, the server includes means for collecting and analyzing the learner's cognitive abilities, interests, learning style, potential talents, and emotional state; means for generating a dynamic curriculum optimized for the learner based on the analysis results using a generative AI model; and means for providing an immersive learning environment based on the generated curriculum using VR / AR technology. This enables the provision of a personalized learning experience in real time that is tailored to the learner's individual characteristics and emotional state, thereby improving learning efficiency and maintaining motivation.

[0761] A "learner" is an individual who acquires knowledge and skills through the use of an educational system.

[0762] "Cognitive ability" refers to intellectual functions such as a learner's comprehension, memory, and thinking skills.

[0763] "Interest" refers to the degree to which a learner shows interest in a particular topic or activity.

[0764] "Learning style" refers to the tendencies and methods by which learners most effectively understand and process information.

[0765] "Latent talent" refers to untapped abilities or qualities that a learner may possess in a particular field or activity.

[0766] "Emotional state" refers to the learner's emotional situation or changes in mood.

[0767] A "generative AI model" refers to an artificial intelligence algorithm that performs predictions and generation based on large amounts of data.

[0768] A "dynamic curriculum" is a learning plan and material structure that is adjusted in real time according to the characteristics and needs of the learners.

[0769] "VR / AR technology" is a technology that uses virtual reality and augmented reality to provide information visually.

[0770] An "immersive learning environment" is a highly interactive learning space designed to allow learners to concentrate and participate in learning activities.

[0771] "Feedback" refers to evaluations and advice provided based on a learner's learning activities and emotional state.

[0772] This invention is an educational system designed to provide learners with personalized learning experiences. The central elements of the invention are a server integrating a generative AI model and an emotion engine, and terminals that receive learning content from this server. The entire system is designed to provide an interactive and immersive learning environment.

[0773] The server collects data submitted by learners and analyzes their cognitive abilities, interests, learning styles, potential talents, and emotional states. This process utilizes a generative AI model, and data analysis tools such as Python and R are used for data analysis. This generates a detailed profile based on the learner's characteristics. For example, the server inputs a prompt such as "Suggest the optimal teaching method based on the user profile" into the generative AI model, generating a curriculum suited to that profile.

[0774] The server then builds a customized dynamic curriculum based on the generated profile and sends it to the terminal. This curriculum generation process utilizes VR / AR technology as interactive learning materials to create an immersive learning environment where learners can connect theory and practice.

[0775] The terminal provides learners with educational content based on a dynamic curriculum provided by the server. The terminal is equipped with sensors that can track learners' movements and gaze in real time, allowing for monitoring of emotional responses during learning. This emotional data is continuously transmitted to the server and used to adjust the overall system.

[0776] Users progress through their learning using a curriculum delivered via their device. As users learn, the content is dynamically adjusted according to their emotional state, ensuring a consistently engaging learning experience. For example, if a user is feeling discouraged about learning, the device can identify this emotion through an emotion engine, and the server can adjust the content to improve motivation.

[0777] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0778] Step 1:

[0779] Users log in to the system via their device and enter basic information to begin learning. This includes information about their current learning subject, objectives, and mood. This input data is sent from the device to the server.

[0780] Step 2:

[0781] The server uses a generative AI model to analyze data based on basic information, cognitive data, and emotional data received from the user. Input data includes the user's past learning history and real-time emotional data. This data is analyzed to generate a profile tailored to the user's learning characteristics and emotional state. The output is a detailed profile that optimally reflects the user's characteristics.

[0782] Step 3:

[0783] The server generates a dynamic curriculum based on the generated profile. This curriculum is created by inputting the prompt "Suggest the optimal learning method based on the user profile" into the generating AI model. The input data is profile information, and the output is a detailed curriculum that outlines what learning content the user is interested in and how they should proceed with their learning.

[0784] Step 4:

[0785] The device provides users with learning content using VR / AR technology based on a customized curriculum received from the server. The input data is curriculum information from the server, and the device provides learning materials to the user according to that information. As output, the user can obtain an immersive learning experience.

[0786] Step 5:

[0787] The device monitors the user's emotional state in real time during the learning process and feeds this information back to the server. The input data consists of the user's biometric information and emotional responses during the learning process, which are sent to the server via the emotion engine. The output is the user's emotional data during the learning process.

[0788] Step 6:

[0789] The server adjusts the curriculum as needed based on emotional data received from the device. The input data is user emotional information processed through an emotion engine. Based on this information, a generative AI model creates optimal feedback and updates content to maintain the user's interest and motivation. As output, a new learning strategy and an adjusted curriculum are generated and sent to the device.

[0790] (Application Example 2)

[0791] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0792] In modern industry, workers are required to quickly and effectively acquire skills in complex machinery and technology. However, traditional methods often fail to adequately consider the individual cognitive characteristics and emotional states of learners, resulting in inefficient learning and increased stress. To address these challenges, there is a need for a system that provides an individualized learning environment and real-time feedback.

[0793] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0794] In this invention, the server includes means for collecting and analyzing the learner's cognitive functions, interests, learning style, and potential abilities; means for generating an adaptive curriculum optimized for the learner based on the analysis results; and means for sensing emotional states and displaying interactive content that takes the learner's psychological state into consideration in real time. This provides an immersive learning experience tailored to the individual needs and emotions of the learner, enabling efficient skill acquisition and stress reduction.

[0795] A "learner" is an individual who utilizes an educational system to acquire specific knowledge or skills.

[0796] "Cognitive function" refers to the brain's processes necessary to understand, analyze, and remember information.

[0797] "Interest" refers to the degree of interest or curiosity a learner has towards a particular topic or activity.

[0798] "Learning format" refers to a category of methods and approaches that enable learners to absorb information most effectively.

[0799] "Latent abilities" refer to skills and talents that learners have not yet manifested but have the potential to develop.

[0800] An "adaptive curriculum" is an educational plan that is dynamically adjusted based on the individual characteristics and emotional state of each learner.

[0801] "Emotional state" refers to the emotions and psychological responses that learners experience in a particular situation.

[0802] "Interactive content" refers to educational materials that are interactive, allowing learners to directly manipulate them and receive feedback.

[0803] An "immersive learning experience" is a learning environment designed to allow learners to become so deeply engrossed in learning activities that they forget their surroundings.

[0804] "Skill acquisition" is the process of acquiring the techniques and knowledge necessary to perform a specific job or task professionally.

[0805] The system that realizes this invention mainly consists of a server, a terminal, and a user. The server collects and analyzes the learner's cognitive functions, interests, learning style, and potential abilities. This uses an advanced data processing technique called a generative AI model. The generative AI model is built using software such as Python or TensorFlow and is capable of precisely identifying the learner's profile.

[0806] Based on the analysis, the server generates an adaptive curriculum optimized for the learner and sends it to the terminal. The terminal then uses VR / AR technology to provide the learner with an immersive learning environment based on the received curriculum. Furthermore, the terminal monitors the user's emotional state using an emotion engine and sends data back to the server in real time based on predetermined indicators. Building a VR / AR environment using Unity is recommended for the implementation of this terminal.

[0807] As a concrete example, consider a scenario where a user is learning to operate a new welding machine. The server analyzes the acquired emotional data in real time, and if the learner feels anxious, it sends interactive content tailored to that emotion to the user's device. The user receives visual guidelines through the device, enabling them to efficiently improve their skills.

[0808] An example of a prompt message is as follows: "Analyze real-time data from workers who want to improve their welding machine operation skills and provide an optimal training simulation."

[0809] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0810] Step 1:

[0811] The server receives cognitive and emotional state data submitted by the user. This input data includes the learner's cognitive functions, interests, learning style, and potential abilities. The server collects the data, performs initial analysis, and then sends it as input to a generative AI model after data cleansing and normalization.

[0812] Step 2:

[0813] The server uses a generative AI model to analyze the input data in detail and build learner profiles. This process identifies the characteristics of each learner through data calculations and outputs profile data. Specifically, it performs weighting based on each characteristic and organizes the analysis results.

[0814] Step 3:

[0815] The server generates an adaptive curriculum optimized for the learner based on profile data. This curriculum is dynamic, taking into account the user's current situation, and is intended for use in a VR / AR environment. The generated curriculum is sent to the device, enabling execution on the device.

[0816] Step 4:

[0817] The device builds an immersive learning environment using VR / AR technology based on an adaptive curriculum received from the server. This environment is designed using Unity and other tools, and is interactive. The device monitors the learner's emotional state in real time and sends this data to an emotion engine to obtain feedback.

[0818] Step 5:

[0819] Users immerse themselves in the learning environment provided through the device and perform actions. The device detects the user's responses and emotional state and sends data to the server as needed. The emotion engine returns the results of the user's psychological state analysis to the server, providing feedback for further curriculum adjustments.

[0820] Step 6:

[0821] The server receives feedback from the emotion engine and adjusts the curriculum accordingly. This allows it to provide relaxing content when the user is stressed, or adjust the difficulty level when the user is highly motivated.

[0822] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0823] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0824] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0825] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0826] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0827] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0828] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0829] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0830] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0831] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0832] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0833] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0834] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0835] 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.

[0836] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0837] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0838] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0839] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0840] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0841] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0842] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0843] The following is further disclosed regarding the embodiments described above.

[0844] (Claim 1)

[0845] A means of collecting and analyzing learners' cognitive abilities, interests, learning styles, and potential talents,

[0846] A means for generating a dynamic curriculum optimized for learners based on the analysis results,

[0847] A means of providing an immersive learning environment based on the generated curriculum,

[0848] A means of tracking learners' progress in real time and generating feedback,

[0849] Means for adjusting learning content in feedback,

[0850] A system that includes this.

[0851] (Claim 2)

[0852] The system according to claim 1, which uses a generative AI model to analyze collected data and identify learner characteristics.

[0853] (Claim 3)

[0854] The system according to claim 1, further comprising means for evaluating the learner's performance after they have completed their learning and for suggesting a new learning plan.

[0855] "Example 1"

[0856] (Claim 1)

[0857] A device for collecting and analyzing learners' cognitive abilities, interests, learning methods, and potential abilities,

[0858] A device that generates a dynamic instruction plan optimized for learners based on the analysis results,

[0859] Based on the generated instructional plan, a device that provides an experiential learning environment,

[0860] A device that tracks the learner's progress in real time and generates responses,

[0861] A device that adjusts learning content based on responses,

[0862] An information processing system that includes this.

[0863] (Claim 2)

[0864] The information processing system according to claim 1, which uses a machine learning model to identify learner characteristics based on the analyzed data.

[0865] (Claim 3)

[0866] The information processing system according to claim 1, further comprising a device that evaluates the learner's performance after the learner has completed the learning and proposes a new instructional plan.

[0867] "Application Example 1"

[0868] (Claim 1)

[0869] Means for collecting and analyzing consumer interests, purchase history, and style,

[0870] A means for generating a list of dynamic product suggestions optimized for consumers based on the analysis results,

[0871] A means of providing a virtual shopping environment based on the generated product suggestions,

[0872] A means of tracking consumer choices in real time and generating suggestions,

[0873] A means of adjusting the product list in the proposal,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, which uses a generative AI model to analyze collected data and identify consumer characteristics.

[0877] (Claim 3)

[0878] The system according to claim 1, further comprising means for evaluating consumer satisfaction after the consumer has finished shopping and for making new product suggestions.

[0879] "Example 2 of combining an emotion engine"

[0880] (Claim 1)

[0881] Means for collecting and analyzing learners' cognitive abilities, interests, learning styles, potential talents, and emotional states,

[0882] A means for generating a dynamic curriculum optimized for learners based on analysis results using a generative AI model,

[0883] A means of providing an immersive learning environment based on a generated curriculum using VR / AR technology,

[0884] A means of tracking learners' progress and emotional responses in real time via a device and generating feedback,

[0885] A means by which the generative AI model adjusts its learning content based on feedback,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, wherein the generative AI model used identifies learner characteristics and uses prompt sentences that integrate an emotion engine to realize a personalized learning experience.

[0889] (Claim 3)

[0890] A system that, after a learner has completed their learning, uses a generative AI model to evaluate their performance, proposes a new learning plan, and takes their emotional state into consideration, the system according to claim 1.

[0891] "Application example 2 when combining with an emotional engine"

[0892] (Claim 1)

[0893] Means for collecting and analyzing learners' cognitive functions, interests, learning styles, and potential abilities,

[0894] A means for generating an adaptive curriculum optimized for learners based on the analysis results,

[0895] A means of providing an immersive learning environment based on the generated curriculum,

[0896] A means of tracking learners' progress sequentially and generating feedback,

[0897] A means of adjusting educational content based on feedback,

[0898] A means of sensing emotional states and displaying interactive content that takes the learner's psychological state into consideration in real time,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, which uses a generative AI model to analyze collected data and identify learner characteristics.

[0902] (Claim 3)

[0903] The system according to claim 1, further comprising means for evaluating the learner's performance after they have completed their learning and for suggesting a new learning plan. [Explanation of Symbols]

[0904] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting and analyzing learners' cognitive abilities, interests, learning styles, and potential talents, A means for generating a dynamic curriculum optimized for learners based on the analysis results, A means of providing an immersive learning environment based on the generated curriculum, A means of tracking learners' progress in real time and generating feedback, Means for adjusting learning content in feedback, A system that includes this.

2. The system according to claim 1, which uses a generative AI model to analyze collected data and identify the characteristics of learners.

3. The system according to claim 1, further comprising means for evaluating the learner's performance after they have completed their learning and for suggesting a new learning plan.

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