system

Abstract

The system according to this embodiment aims to provide customized educational content tailored to the needs of individual learners. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a provision unit, and a tracking unit. The reception unit receives user input. The generation unit generates customized educational content based on the information received by the reception unit. The provision unit provides the educational content generated by the generation unit. The tracking unit tracks learning progress based on the educational content provided by the provision unit and adjusts the learning plan as necessary.

Description

Technical Field

[0006] , , ,

[0005] , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, customized educational content that meets the needs of individual learners has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide customized educational content that meets the needs of individual learners.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a provision unit, and a tracking unit. The reception unit receives user input. The generation unit generates customized educational content based on the information received by the reception unit. The provision unit provides the educational content generated by the generation unit. The tracking unit tracks learning progress based on the educational content provided by the provision unit and adjusts the learning plan as needed. [Effects of the Invention]

[0007] The system according to this embodiment can provide customized educational content tailored to the needs of individual learners. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single 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), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An online education platform according to an embodiment of the present invention is a system that provides customized educational content tailored to the needs of individual learners. This system utilizes AI technology to enable users to efficiently and effectively learn new skills, facilitating career changes and skill upgrades. First, the user accesses the platform and inputs the skills and goals they wish to learn. For example, they may input specific goals such as "I want to learn programming" or "I want to improve my data analysis skills." This information is input into the AI. Next, the AI ​​analyzes the user's input information and generates customized educational content tailored to individual needs. The AI ​​creates an optimal learning plan considering the user's profile, past learning history, current skill level, etc. For example, content is provided according to the user's level, such as a programming course for beginners or a data analysis course for intermediate learners. The generated educational content is provided to the user, allowing them to learn at their own pace. For example, it includes various forms of content such as video lectures, interactive exercises, and real-time feedback. This enables users to acquire new skills efficiently and effectively. Furthermore, the AI ​​tracks the user's learning progress and adjusts the learning plan as needed. For example, if the user is struggling with a particular topic, the AI ​​provides additional resources and supplementary explanations. Furthermore, once a user achieves a goal, it sets a new goal as the next step, supporting further skill development. This platform targets employees within companies, particularly middle managers and above who need to keep up with technological changes. It enables efficient skill development by providing flexible learning schedules and customized educational content, in response to the need for continuous learning and skill development to keep pace with the rapid evolution of technology. For example, when company employees use this platform to acquire new technologies, the AI ​​provides an optimal learning plan tailored to each employee's needs, enabling efficient skill development. It can also be used as a solution for flexible online learning, given the prevalence of remote work.In this way, by providing customized educational content utilizing AI technology, it is possible to support users' skill development and career transitions, thereby strengthening the competitiveness of companies and supporting the career development of employees. Thus, online education platforms can support users' skill development and career transitions, thereby strengthening the competitiveness of companies and supporting the career development of employees.

[0029] The online education platform according to this embodiment comprises a reception unit, a generation unit, a provision unit, and a tracking unit. The reception unit receives user input. User input includes, for example, the skills and goals that the user wants to learn, but is not limited to such examples. The reception unit accepts, for example, the user's input of specific goals such as "I want to learn programming" or "I want to improve my data analysis skills." The generation unit generates customized educational content based on the information received by the reception unit. The generation unit generates an optimal learning plan, for example, by considering the user's profile, past learning history, and current skill level. The generation unit provides content according to the user's level, for example, a programming course for beginners or a data analysis course for intermediate users. The generation unit uses a generation AI to analyze the user's input information and generates customized educational content tailored to individual needs. The provision unit provides the educational content generated by the generation unit. The provision unit provides educational content in various forms, for example, video lectures, interactive exercises, and real-time feedback. The provision unit provides the generated educational content so that the user can learn at their own pace. The tracking unit tracks learning progress based on the educational content provided by the delivery unit and adjusts the learning plan as needed. For example, the tracking unit provides additional resources and supplementary explanations if the user is struggling with a particular topic. The tracking unit sets new goals when the user achieves their initial goal and supports further skill development. Thus, the online education platform according to the embodiment achieves efficient learning by providing customized educational content based on user input and tracking and adjusting learning progress.

[0030] The reception desk receives user input. User input includes, but is not limited to, the skills and goals that users wish to learn. The reception desk accepts specific goals, such as "I want to learn programming" or "I want to improve my data analysis skills." Specifically, the reception desk provides forms and questions that users can easily fill out through the user interface. This allows users to describe their learning goals and interests in detail. For example, if a user enters "I want to learn data analysis using Python," the reception desk accurately receives this information and sends it to the next step, the generation desk. The reception desk also collects user profile information. This includes the user's age, occupation, learning history, and current skill level. This information plays an important role in the subsequent customization process. Furthermore, the reception desk has a function to receive user feedback, allowing it to collect problems and areas for improvement that users encountered during their learning. This contributes to improving the overall quality of the platform. The reception desk securely manages user input data and implements appropriate security measures to protect privacy. For example, it implements data encryption and access control to prevent unauthorized access to users' personal information. This allows the reception department to gain the user's trust and provide a safe and secure environment for them to use.

[0031] The generation unit generates customized educational content based on information received by the reception unit. For example, the generation unit generates an optimal learning plan considering the user's profile, past learning history, and current skill level. The generation unit provides content tailored to the user's level, such as a programming course for beginners or a data analysis course for intermediate users. Using a generation AI, the generation unit analyzes the user's input and generates customized educational content that meets individual needs. Specifically, the generation AI uses natural language processing technology to analyze the user's input and select appropriate learning resources. For example, if a user inputs "I want to learn the basics of Python," the generation AI selects materials that cover the fundamental concepts and coding basics of Python. Furthermore, the generation AI can dynamically adjust the learning plan according to the user's learning style and pace. For example, if a user wants to learn intensively in a short period, the generation AI proposes an intensive learning schedule; conversely, if a user wants to learn slowly over a longer period, it provides a more relaxed schedule. In addition, the generation unit monitors the user's progress in real time and updates the learning plan as needed. For example, if a user gets stuck on a particular topic, the generative AI can provide additional resources and supplementary explanations to help the user deepen their understanding. This allows the generative unit to provide the user with an optimal learning experience and support efficient skill acquisition.

[0032] The provider unit provides educational content generated by the generator unit. The provider unit offers diverse forms of educational content, such as video lectures, interactive exercises, and real-time feedback. Specifically, the provider unit provides an easily accessible platform for users and efficiently delivers learning content. Video lectures are delivered with high-quality video and audio, allowing users to watch at their own pace. Interactive exercises allow users to practically confirm what they have learned and receive immediate feedback. For example, in programming exercises, when a user enters code, the system automatically evaluates the code and provides real-time feedback on correctness and areas for improvement. The provider unit also provides a dashboard that visualizes users' learning progress, allowing them to see their progress at a glance. This makes it easier for users to adjust their learning pace. Furthermore, the provider unit supports the learning community, providing an environment where users can interact and help each other. For example, through forums and chat functions, users can post questions and answer questions from other users. This allows the provider unit to provide users with diverse learning resources and support, enriching the learning experience.

[0033] The tracking unit tracks learning progress based on the educational content provided by the delivery unit and adjusts the learning plan as needed. For example, the tracking unit provides additional resources and supplementary explanations if the user is struggling with a particular topic. Specifically, the tracking unit monitors the user's learning data in real time and evaluates their learning progress and understanding. For example, if a user repeatedly makes mistakes on a particular exercise, the tracking unit provides resources to review the fundamental concepts related to that exercise. The tracking unit can also analyze the user's learning patterns and suggest the optimal learning method. For example, if a user often studies at night, the tracking unit suggests a learning plan tailored to that time slot. Furthermore, the tracking unit sets new goals when the user achieves their initial goals, supporting further skill development. For example, if a user completes an introductory programming course, the tracking unit suggests an intermediate course and provides guidance for moving on to the next step. The tracking unit also collects user feedback and uses it to improve the learning plan. This allows the tracking unit to continuously improve the user's learning experience and support efficient skill acquisition.

[0034] The generation unit can generate an optimal learning plan by considering the user's profile, past learning history, and current skill level. For example, the generation unit considers information included in the user's profile, such as age, occupation, and learning objectives. The generation unit considers information included in the past learning history, such as completed courses, acquired skills, and test results. The generation unit can use test results, self-assessments, and third-party assessments to evaluate the current skill level. The content of the optimal learning plan includes learning objectives, schedule, and learning materials to be used. This enables learning tailored to individual needs by providing an optimal learning plan based on the user's profile and learning history. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's profile and learning history into a generation AI and have the generation AI generate an optimal learning plan.

[0035] The service provider can offer educational content in various formats, such as video lectures, interactive exercises, and real-time feedback. For example, the service provider can offer video lectures. The service provider can also offer interactive exercises. The service provider can also offer real-time feedback. By offering educational content in various formats, effective learning tailored to the user's learning style becomes possible. Some or all of the above-described processes in the service provider may be performed using generative AI, or they may not. For example, the service provider can use generative AI to select and provide the optimal format of educational content tailored to the user's learning style.

[0036] The tracking unit can track the user's learning progress and provide additional resources or supplementary explanations if the user is struggling with a particular topic. The tracking unit tracks learning progress based on, for example, test results, study time, and completed assignments. If the user is struggling with a particular topic, the tracking unit can provide resources such as additional learning materials, video explanations, or personalized instruction. This improves learning efficiency by tracking learning progress and providing resources tailored to the user's difficulties. Some or all of the above processing in the tracking unit may be performed using generative AI, or not. For example, the tracking unit can use generative AI to analyze the user's learning progress in real time and provide resources tailored to their difficulties.

[0037] The tracking unit can set new goals when the user achieves an initial goal, supporting further skill development. For example, the tracking unit can set the next skill level as a goal when the user reaches a specific skill level. The tracking unit can also set new tasks when a specific task is completed. This supports continuous skill development by setting new goals after achieving an initial goal. Some or all of the above-described processes in the tracking unit may be performed using generative AI, or not. For example, the tracking unit can use generative AI to analyze the user's learning progress and automatically set the next goal.

[0038] The service provider offers customized features for businesses, providing flexible learning schedules and customized educational content tailored to the company's needs. For example, the service provider can provide learning schedules tailored to the company's needs. The service provider can also provide customized educational content aimed at strengthening specific skill sets. This efficiently supports the skill development of employees within a company by providing customized features tailored to the company's needs. Some or all of the processes described above in the service provider may be performed using generative AI, or not. For example, the service provider can use generative AI to generate and provide optimal learning schedules and educational content based on the company's needs.

[0039] The service provider can offer flexibility in online learning to accommodate the spread of remote work. For example, it can provide flexibility in learning time. The service provider can also increase the types of devices that can access the learning environment. This allows for learning regardless of location by providing a flexible learning environment that supports remote work. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider can use generative AI to provide an optimal learning environment tailored to the user's learning style and device.

[0040] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display skills and goals that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest skills and goals that the user will use at specific times based on their past input history. This improves input efficiency by suggesting the optimal input method based on past input history. Some or all of the above processing in the reception desk may be performed using generative AI, or not. For example, the reception desk can input the user's past input history into a generative AI and have the generative AI suggest the optimal input method.

[0041] The input field can filter input content based on the user's current learning status and areas of interest. For example, the input field can prioritize displaying input content related to the skills the user is currently learning. The input field can also suggest relevant skills and goals based on the user's areas of interest. The input field can also filter and display the skills and goals the user should learn next, according to their learning progress. This allows for more relevant input by filtering input content based on the current learning status and areas of interest. Some or all of the above processing in the input field may be performed using a generative AI, or not. For example, the input field can input the user's current learning status and areas of interest into a generative AI and have the generative AI perform optimal input filtering.

[0042] The reception desk can provide highly relevant input options by taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can suggest popular skills and goals in that region. The reception desk can also provide region-specific educational content based on the user's geographical location. If the user is traveling, the reception desk can also suggest skills and goals that would be useful at their travel destination. This allows for more appropriate input by providing highly relevant input options based on geographical location. Some or all of the above processing in the reception desk may be performed using generative AI, or not. For example, the reception desk can input the user's geographical location into the generative AI and have the generative AI suggest the most suitable input options.

[0043] The reception desk can analyze the user's social media activity and suggest relevant input content. For example, the reception desk can suggest skills and goals that the user frequently mentions on social media. The reception desk can also extract topics of interest from the user's social media activity and provide relevant input content. The reception desk can also suggest skills and goals that the user's social media followers and friends are learning. This allows for more relevant input by suggesting relevant input content based on social media activity. Some or all of the above processing in the reception desk may be performed using generative AI, or not. For example, the reception desk can input the user's social media activity into a generative AI and have the generative AI suggest the most suitable input content.

[0044] The generation unit can analyze the user's past learning history and generate an optimal learning plan. For example, the generation unit can suggest the next skills the user should learn based on the skills they have learned in the past. The generation unit can also extract effective learning methods from the user's past learning history and generate an optimal learning plan. The generation unit can also analyze the user's past learning history and provide a customized learning plan according to their learning progress. This enables efficient learning by providing an optimal learning plan based on past learning history. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's past learning history into a generation AI and have the generation AI generate an optimal learning plan.

[0045] The generation unit can apply different educational content generation algorithms depending on the user's current skill level. For example, the generation unit can apply a content generation algorithm appropriate for beginners. The generation unit can also apply a content generation algorithm appropriate for intermediate users. The generation unit can also apply a content generation algorithm appropriate for advanced users. By applying a content generation algorithm appropriate to the skill level, more appropriate educational content can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's current skill level into the generation AI and have the generation AI execute the application of the optimal educational content generation algorithm.

[0046] The generation unit can prioritize educational content based on the user's submission timing. For example, if a user submits early, the generation unit will prioritize providing educational content. If a user is approaching the submission deadline, the generation unit can also prioritize providing high-priority content. If a user has missed the submission deadline, the generation unit can also provide supplementary content. In this way, by prioritizing based on submission timing, educational content is provided at the appropriate time. Some or all of the above processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the user's submission timing into the generation AI and have the generation AI perform the task of determining the priority of educational content.

[0047] The generation unit can adjust the order of educational content based on user relevance. For example, the generation unit can provide content in the optimal order based on the user's current learning progress. The generation unit can also prioritize providing highly relevant content based on the user's areas of interest. The generation unit can also provide content in an effective order based on the user's past learning history. This allows for more effective learning by adjusting the order of content based on relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input user relevance into a generation AI and have the generation AI perform the adjustment of the order of educational content.

[0048] The delivery unit can select the optimal delivery method by referring to the user's past learning history. For example, the delivery unit can select the optimal delivery method based on delivery methods that were effective for the user in the past. The delivery unit can also extract effective delivery methods from the user's past learning history and select the optimal delivery method. The delivery unit can also analyze the user's past learning history and select the optimal delivery method according to the user's learning progress. This enables efficient learning by selecting the optimal delivery method based on past learning history. Some or all of the above processing in the delivery unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the delivery unit can input the user's past learning history into a generative AI and have the generative AI select the optimal delivery method.

[0049] The delivery unit can customize the means of delivering educational content based on the user's current learning status. For example, the delivery unit can customize the means of delivery related to the skills the user is currently learning. The delivery unit can also customize the optimal means of delivery according to the user's learning progress. The delivery unit can also customize the most effective means of delivery based on the user's current learning status. This makes more effective learning possible by customizing the means of delivery based on the current learning status. Some or all of the above processing in the delivery unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the delivery unit can input the user's current learning status into a generative AI and have the generative AI perform the customization of the optimal means of delivery.

[0050] The service provider can provide optimal educational content by taking into account the user's geographical location. For example, if the user is in a specific region, the service provider can provide educational content that is popular in that region. The service provider can also provide region-specific educational content based on the user's geographical location. If the user is traveling, the service provider can also provide educational content that is useful at their travel destination. This enables more effective learning by providing optimal educational content based on geographical location. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input the user's geographical location information into a generative AI and have the generative AI perform the task of providing optimal educational content.

[0051] The service provider can analyze a user's social media activity and provide relevant educational content. For example, it can provide educational content related to skills and goals that the user frequently mentions on social media. It can also provide educational content related to topics of interest based on the user's social media activity. It can also provide educational content related to skills and goals that the user's social media followers and friends are learning. This enables more effective learning by providing relevant educational content based on social media activity. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider can input the user's social media activity into a generative AI and have the generative AI provide the most suitable educational content.

[0052] The tracking unit can analyze the user's past learning progress and select the optimal tracking method. For example, the tracking unit can select the optimal tracking method based on tracking methods that were effective for the user in the past. The tracking unit can also extract effective tracking methods from the user's past learning progress and select the optimal tracking method. The tracking unit can also analyze the user's past learning progress and select the optimal tracking method according to the learning progress. This enables efficient management of learning progress by selecting the optimal tracking method based on past learning progress. Some or all of the above processing in the tracking unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the tracking unit can input the user's past learning progress into a generative AI and have the generative AI select the optimal tracking method.

[0053] The tracking unit can customize the means of tracking learning progress based on the user's current learning status. For example, the tracking unit can customize tracking means related to the skills the user is currently learning. The tracking unit can also customize the optimal tracking means according to the user's learning progress. The tracking unit can also customize effective tracking means based on the user's current learning status. This makes it possible to manage learning progress more effectively by customizing tracking means based on the current learning status. Some or all of the above processing in the tracking unit may be performed using generative AI or not. For example, the tracking unit can input the user's current learning status into the generative AI and have the generative AI perform the customization of the optimal tracking means.

[0054] The tracking unit can provide an optimal method for tracking learning progress, taking into account the user's geographical location. For example, if the user is in a specific region, the tracking unit can provide a method for tracking learning progress in that region. The tracking unit can also provide a method for tracking region-specific learning progress based on the user's geographical location. If the user is traveling, the tracking unit can also provide a method for tracking learning progress at their travel destination. This enables more effective management of learning progress by providing an optimal tracking method based on geographical location. Some or all of the above processing in the tracking unit may be performed using a generative AI, or not. For example, the tracking unit can input the user's geographical location information into a generative AI and have the generative AI perform the task of providing an optimal method for tracking learning progress.

[0055] The tracking unit can analyze a user's social media activity and provide means to track relevant learning progress. For example, the tracking unit can track learning progress related to skills and goals that the user frequently mentions on social media. The tracking unit can also track learning progress related to topics of interest from the user's social media activity. The tracking unit can also track learning progress related to skills and goals that the user's social media followers and friends are learning. This enables more effective management of learning progress by providing means to track relevant learning progress based on social media activity. Some or all of the above processing in the tracking unit may be performed using generative AI or not. For example, the tracking unit can input the user's social media activity into a generative AI and have the generative AI perform the task of providing the optimal means to track learning progress.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The reception desk can dynamically adjust learning priorities based on user input. For example, if a user inputs that they want to learn multiple skills, the reception desk can suggest the most effective learning sequence based on the user's current skill level and learning objectives. Furthermore, if a user shows a strong interest in a particular skill, the system can adjust the learning priorities to prioritize that skill. Additionally, if a user is in a hurry, the system can prioritize suggesting skills that can be learned effectively in a short period. This provides a flexible learning plan tailored to the user's needs, enabling efficient learning.

[0058] The generation unit can create optimal educational content based on the user's learning style. For example, users who prefer visual learning can be provided with content that makes extensive use of videos and infographics. Users who prefer practical learning can be provided with interactive exercises and simulations. Furthermore, users who prefer reading can be provided with text-based learning materials and ebooks. This ensures that the optimal educational content is provided to match the user's learning style, improving learning effectiveness.

[0059] The system can provide real-time feedback based on the user's learning progress. For example, it can provide immediate feedback as soon as the user completes a specific task. It can also provide detailed explanations and additional practice problems for questions the user answered incorrectly. Furthermore, when a user acquires a specific skill, it can suggest the next steps related to that skill. This allows users to learn while receiving real-time feedback, improving learning effectiveness.

[0060] The tracking unit can provide a dashboard to visualize the user's learning progress. For example, it can display completed tasks and acquired skills using graphs and charts. It can also display the user's learning pace and progress in real time. Furthermore, it can visually display the degree of achievement against the goals set by the user. This allows users to grasp their learning progress at a glance and maintain motivation as they continue learning.

[0061] The tracking unit can dynamically adjust the learning plan based on the user's learning progress. For example, if a user is struggling with a particular topic, it can provide additional resources and supplementary explanations related to that topic. Furthermore, if the user achieves a goal, it can set a new goal and suggest the next step. It can also adjust the learning plan schedule according to the user's learning pace. This provides a flexible learning plan tailored to the user's progress, enabling efficient learning.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk receives user input. User input includes, for example, the skills and goals the user wants to learn. Specifically, it accepts user input such as "I want to learn programming" or "I want to improve my data analysis skills." Step 2: The generation unit generates customized educational content based on the information received by the reception unit. The generation unit generates an optimal learning plan considering the user's profile, past learning history, and current skill level. For example, it provides content tailored to the user's level, such as a programming course for beginners or a data analysis course for intermediate users. The generation unit uses a generation AI to analyze the user's input information and generate customized educational content that meets individual needs. Step 3: The provider unit provides the educational content generated by the generator unit. The provider unit provides educational content in various forms, such as video lectures, interactive exercises, and real-time feedback. The generated educational content is provided so that users can learn at their own pace. Step 4: The tracking unit tracks learning progress based on the educational content provided by the delivery unit and adjusts the learning plan as needed. The tracking unit provides additional resources and supplementary explanations if the user is struggling with a particular topic. It sets new goals when the user achieves their initial goals and supports further skill development.

[0064] (Example of form 2) An online education platform according to an embodiment of the present invention is a system that provides customized educational content tailored to the needs of individual learners. This system utilizes AI technology to enable users to efficiently and effectively learn new skills, facilitating career changes and skill upgrades. First, the user accesses the platform and inputs the skills and goals they wish to learn. For example, they may input specific goals such as "I want to learn programming" or "I want to improve my data analysis skills." This information is input into the AI. Next, the AI ​​analyzes the user's input information and generates customized educational content tailored to individual needs. The AI ​​creates an optimal learning plan considering the user's profile, past learning history, current skill level, etc. For example, content is provided according to the user's level, such as a programming course for beginners or a data analysis course for intermediate learners. The generated educational content is provided to the user, allowing them to learn at their own pace. For example, it includes various forms of content such as video lectures, interactive exercises, and real-time feedback. This enables users to acquire new skills efficiently and effectively. Furthermore, the AI ​​tracks the user's learning progress and adjusts the learning plan as needed. For example, if the user is struggling with a particular topic, the AI ​​provides additional resources and supplementary explanations. Furthermore, once a user achieves a goal, it sets a new goal as the next step, supporting further skill development. This platform targets employees within companies, particularly middle managers and above who need to keep up with technological changes. It enables efficient skill development by providing flexible learning schedules and customized educational content, in response to the need for continuous learning and skill development to keep pace with the rapid evolution of technology. For example, when company employees use this platform to acquire new technologies, the AI ​​provides an optimal learning plan tailored to each employee's needs, enabling efficient skill development. It can also be used as a solution for flexible online learning, given the prevalence of remote work.In this way, by providing customized educational content utilizing AI technology, it is possible to support users' skill development and career transitions, thereby strengthening the competitiveness of companies and supporting the career development of employees. Thus, online education platforms can support users' skill development and career transitions, thereby strengthening the competitiveness of companies and supporting the career development of employees.

[0065] The online education platform according to this embodiment comprises a reception unit, a generation unit, a provision unit, and a tracking unit. The reception unit receives user input. User input includes, for example, the skills and goals that the user wants to learn, but is not limited to such examples. The reception unit accepts, for example, the user's input of specific goals such as "I want to learn programming" or "I want to improve my data analysis skills." The generation unit generates customized educational content based on the information received by the reception unit. The generation unit generates an optimal learning plan, for example, by considering the user's profile, past learning history, and current skill level. The generation unit provides content according to the user's level, for example, a programming course for beginners or a data analysis course for intermediate users. The generation unit uses a generation AI to analyze the user's input information and generates customized educational content tailored to individual needs. The provision unit provides the educational content generated by the generation unit. The provision unit provides educational content in various forms, for example, video lectures, interactive exercises, and real-time feedback. The provision unit provides the generated educational content so that the user can learn at their own pace. The tracking unit tracks learning progress based on the educational content provided by the delivery unit and adjusts the learning plan as needed. For example, the tracking unit provides additional resources and supplementary explanations if the user is struggling with a particular topic. The tracking unit sets new goals when the user achieves their initial goal and supports further skill development. Thus, the online education platform according to the embodiment achieves efficient learning by providing customized educational content based on user input and tracking and adjusting learning progress.

[0066] The reception desk receives user input. User input includes, but is not limited to, the skills and goals that users wish to learn. The reception desk accepts specific goals, such as "I want to learn programming" or "I want to improve my data analysis skills." Specifically, the reception desk provides forms and questions that users can easily fill out through the user interface. This allows users to describe their learning goals and interests in detail. For example, if a user enters "I want to learn data analysis using Python," the reception desk accurately receives this information and sends it to the next step, the generation desk. The reception desk also collects user profile information. This includes the user's age, occupation, learning history, and current skill level. This information plays an important role in the subsequent customization process. Furthermore, the reception desk has a function to receive user feedback, allowing it to collect problems and areas for improvement that users encountered during their learning. This contributes to improving the overall quality of the platform. The reception desk securely manages user input data and implements appropriate security measures to protect privacy. For example, it implements data encryption and access control to prevent unauthorized access to users' personal information. This allows the reception department to gain the user's trust and provide a safe and secure environment for them to use.

[0067] The generation unit generates customized educational content based on information received by the reception unit. For example, the generation unit generates an optimal learning plan considering the user's profile, past learning history, and current skill level. The generation unit provides content tailored to the user's level, such as a programming course for beginners or a data analysis course for intermediate users. Using a generation AI, the generation unit analyzes the user's input and generates customized educational content that meets individual needs. Specifically, the generation AI uses natural language processing technology to analyze the user's input and select appropriate learning resources. For example, if a user inputs "I want to learn the basics of Python," the generation AI selects materials that cover the fundamental concepts and coding basics of Python. Furthermore, the generation AI can dynamically adjust the learning plan according to the user's learning style and pace. For example, if a user wants to learn intensively in a short period, the generation AI proposes an intensive learning schedule; conversely, if a user wants to learn slowly over a longer period, it provides a more relaxed schedule. In addition, the generation unit monitors the user's progress in real time and updates the learning plan as needed. For example, if a user gets stuck on a particular topic, the generative AI can provide additional resources and supplementary explanations to help the user deepen their understanding. This allows the generative unit to provide the user with an optimal learning experience and support efficient skill acquisition.

[0068] The provider unit provides educational content generated by the generator unit. The provider unit offers diverse forms of educational content, such as video lectures, interactive exercises, and real-time feedback. Specifically, the provider unit provides an easily accessible platform for users and efficiently delivers learning content. Video lectures are delivered with high-quality video and audio, allowing users to watch at their own pace. Interactive exercises allow users to practically confirm what they have learned and receive immediate feedback. For example, in programming exercises, when a user enters code, the system automatically evaluates the code and provides real-time feedback on correctness and areas for improvement. The provider unit also provides a dashboard that visualizes users' learning progress, allowing them to see their progress at a glance. This makes it easier for users to adjust their learning pace. Furthermore, the provider unit supports the learning community, providing an environment where users can interact and help each other. For example, through forums and chat functions, users can post questions and answer questions from other users. This allows the provider unit to provide users with diverse learning resources and support, enriching the learning experience.

[0069] The tracking unit tracks learning progress based on the educational content provided by the delivery unit and adjusts the learning plan as needed. For example, the tracking unit provides additional resources and supplementary explanations if the user is struggling with a particular topic. Specifically, the tracking unit monitors the user's learning data in real time and evaluates their learning progress and understanding. For example, if a user repeatedly makes mistakes on a particular exercise, the tracking unit provides resources to review the fundamental concepts related to that exercise. The tracking unit can also analyze the user's learning patterns and suggest the optimal learning method. For example, if a user often studies at night, the tracking unit suggests a learning plan tailored to that time slot. Furthermore, the tracking unit sets new goals when the user achieves their initial goals, supporting further skill development. For example, if a user completes an introductory programming course, the tracking unit suggests an intermediate course and provides guidance for moving on to the next step. The tracking unit also collects user feedback and uses it to improve the learning plan. This allows the tracking unit to continuously improve the user's learning experience and support efficient skill acquisition.

[0070] The generation unit can generate an optimal learning plan by considering the user's profile, past learning history, and current skill level. For example, the generation unit considers information included in the user's profile, such as age, occupation, and learning objectives. The generation unit considers information included in the past learning history, such as completed courses, acquired skills, and test results. The generation unit can use test results, self-assessments, and third-party assessments to evaluate the current skill level. The content of the optimal learning plan includes learning objectives, schedule, and learning materials to be used. This enables learning tailored to individual needs by providing an optimal learning plan based on the user's profile and learning history. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's profile and learning history into a generation AI and have the generation AI generate an optimal learning plan.

[0071] The service provider can offer educational content in various formats, such as video lectures, interactive exercises, and real-time feedback. For example, the service provider can offer video lectures. The service provider can also offer interactive exercises. The service provider can also offer real-time feedback. By offering educational content in various formats, effective learning tailored to the user's learning style becomes possible. Some or all of the above-described processes in the service provider may be performed using generative AI, or they may not. For example, the service provider can use generative AI to select and provide the optimal format of educational content tailored to the user's learning style.

[0072] The tracking unit can track the user's learning progress and provide additional resources or supplementary explanations if the user is struggling with a particular topic. The tracking unit tracks learning progress based on, for example, test results, study time, and completed assignments. If the user is struggling with a particular topic, the tracking unit can provide resources such as additional learning materials, video explanations, or personalized instruction. This improves learning efficiency by tracking learning progress and providing resources tailored to the user's difficulties. Some or all of the above processing in the tracking unit may be performed using generative AI, or not. For example, the tracking unit can use generative AI to analyze the user's learning progress in real time and provide resources tailored to their difficulties.

[0073] The tracking unit can set new goals when the user achieves an initial goal, supporting further skill development. For example, the tracking unit can set the next skill level as a goal when the user reaches a specific skill level. The tracking unit can also set new tasks when a specific task is completed. This supports continuous skill development by setting new goals after achieving an initial goal. Some or all of the above-described processes in the tracking unit may be performed using generative AI, or not. For example, the tracking unit can use generative AI to analyze the user's learning progress and automatically set the next goal.

[0074] The service provider offers customized features for businesses, providing flexible learning schedules and customized educational content tailored to the company's needs. For example, the service provider can provide learning schedules tailored to the company's needs. The service provider can also provide customized educational content aimed at strengthening specific skill sets. This efficiently supports the skill development of employees within a company by providing customized features tailored to the company's needs. Some or all of the processes described above in the service provider may be performed using generative AI, or not. For example, the service provider can use generative AI to generate and provide optimal learning schedules and educational content based on the company's needs.

[0075] The service provider can offer flexibility in online learning to accommodate the spread of remote work. For example, it can provide flexibility in learning time. The service provider can also increase the types of devices that can access the learning environment. This allows for learning regardless of location by providing a flexible learning environment that supports remote work. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider can use generative AI to provide an optimal learning environment tailored to the user's learning style and device.

[0076] The reception desk can estimate the user's emotions and adjust the priority of input content based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk can prioritize voice input, allowing them to quickly input skills or goals they want to learn. This allows for more appropriate input by adjusting the priority of input content according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using generative AI or not. For example, the reception desk can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display skills and goals that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest skills and goals that the user will use at specific times based on their past input history. This improves input efficiency by suggesting the optimal input method based on past input history. Some or all of the above processing in the reception desk may be performed using generative AI, or not. For example, the reception desk can input the user's past input history into a generative AI and have the generative AI suggest the optimal input method.

[0078] The input field can filter input content based on the user's current learning status and areas of interest. For example, the input field can prioritize displaying input content related to the skills the user is currently learning. The input field can also suggest relevant skills and goals based on the user's areas of interest. The input field can also filter and display the skills and goals the user should learn next, according to their learning progress. This allows for more relevant input by filtering input content based on the current learning status and areas of interest. Some or all of the above processing in the input field may be performed using a generative AI, or not. For example, the input field can input the user's current learning status and areas of interest into a generative AI and have the generative AI perform optimal input filtering.

[0079] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, if the user is tense, the reception unit can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, the reception unit can provide an interface with bright colors to make the input process more enjoyable. If the user is tired, the reception unit can provide a simple and highly visible interface to facilitate the input process. In this way, by adjusting the design of the input interface according to the user's emotions, a more comfortable input environment is provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using generative AI or not. For example, the reception unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0080] The reception desk can provide highly relevant input options by taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can suggest popular skills and goals in that region. The reception desk can also provide region-specific educational content based on the user's geographical location. If the user is traveling, the reception desk can also suggest skills and goals that would be useful at their travel destination. This allows for more appropriate input by providing highly relevant input options based on geographical location. Some or all of the above processing in the reception desk may be performed using generative AI, or not. For example, the reception desk can input the user's geographical location into the generative AI and have the generative AI suggest the most suitable input options.

[0081] The reception desk can analyze the user's social media activity and suggest relevant input content. For example, the reception desk can suggest skills and goals that the user frequently mentions on social media. The reception desk can also extract topics of interest from the user's social media activity and provide relevant input content. The reception desk can also suggest skills and goals that the user's social media followers and friends are learning. This allows for more relevant input by suggesting relevant input content based on social media activity. Some or all of the above processing in the reception desk may be performed using generative AI, or not. For example, the reception desk can input the user's social media activity into a generative AI and have the generative AI suggest the most suitable input content.

[0082] The generation unit can estimate the user's emotions and adjust the difficulty level of the educational content based on the estimated emotions. For example, if the user is relaxed, the generation unit can provide slightly more difficult content. If the user is stressed, the generation unit can also provide easier content. If the user is excited, the generation unit can also provide challenging content. By adjusting the difficulty level of the educational content according to the user's emotions, more appropriate learning becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generative AI or not. For example, the generation unit can input user emotion data into the generative AI and have the generative AI adjust the difficulty level of the educational content.

[0083] The generation unit can analyze the user's past learning history and generate an optimal learning plan. For example, the generation unit can suggest the next skills the user should learn based on the skills they have learned in the past. The generation unit can also extract effective learning methods from the user's past learning history and generate an optimal learning plan. The generation unit can also analyze the user's past learning history and provide a customized learning plan according to their learning progress. This enables efficient learning by providing an optimal learning plan based on past learning history. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's past learning history into a generation AI and have the generation AI generate an optimal learning plan.

[0084] The generation unit can apply different educational content generation algorithms depending on the user's current skill level. For example, the generation unit can apply a content generation algorithm appropriate for beginners. The generation unit can also apply a content generation algorithm appropriate for intermediate users. The generation unit can also apply a content generation algorithm appropriate for advanced users. By applying a content generation algorithm appropriate to the skill level, more appropriate educational content can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's current skill level into the generation AI and have the generation AI execute the application of the optimal educational content generation algorithm.

[0085] The generation unit can estimate the user's emotions and adjust the format of the educational content based on the estimated emotions. For example, if the user is relaxed, the generation unit can provide content in the form of a video lecture. If the user is stressed, the generation unit can also provide content in the form of interactive exercises. If the user is excited, the generation unit can also provide content in the form of a game. This allows for more effective learning by adjusting the format of the educational content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without a generative AI. For example, the generation unit can input user emotion data into a generative AI and have the generative AI adjust the format of the educational content.

[0086] The generation unit can prioritize educational content based on the user's submission timing. For example, if a user submits early, the generation unit will prioritize providing educational content. If a user is approaching the submission deadline, the generation unit can also prioritize providing high-priority content. If a user has missed the submission deadline, the generation unit can also provide supplementary content. In this way, by prioritizing based on submission timing, educational content is provided at the appropriate time. Some or all of the above processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the user's submission timing into the generation AI and have the generation AI perform the task of determining the priority of educational content.

[0087] The generation unit can adjust the order of educational content based on user relevance. For example, the generation unit can provide content in the optimal order based on the user's current learning progress. The generation unit can also prioritize providing highly relevant content based on the user's areas of interest. The generation unit can also provide content in an effective order based on the user's past learning history. This allows for more effective learning by adjusting the order of content based on relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input user relevance into a generation AI and have the generation AI perform the adjustment of the order of educational content.

[0088] The delivery unit can estimate the user's emotions and adjust the method of delivering educational content based on the estimated emotions. For example, if the user is relaxed, the delivery unit can deliver content in the form of a video lecture. If the user is stressed, the delivery unit can also deliver content in the form of an interactive exercise. If the user is excited, the delivery unit can also deliver content in the form of a game. By adjusting the delivery method according to the user's emotions, more effective learning becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using generative AI or not. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI adjust the method of delivering educational content.

[0089] The delivery unit can select the optimal delivery method by referring to the user's past learning history. For example, the delivery unit can select the optimal delivery method based on delivery methods that were effective for the user in the past. The delivery unit can also extract effective delivery methods from the user's past learning history and select the optimal delivery method. The delivery unit can also analyze the user's past learning history and select the optimal delivery method according to the user's learning progress. This enables efficient learning by selecting the optimal delivery method based on past learning history. Some or all of the above processing in the delivery unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the delivery unit can input the user's past learning history into a generative AI and have the generative AI select the optimal delivery method.

[0090] The delivery unit can customize the means of delivering educational content based on the user's current learning status. For example, the delivery unit can customize the means of delivery related to the skills the user is currently learning. The delivery unit can also customize the optimal means of delivery according to the user's learning progress. The delivery unit can also customize the most effective means of delivery based on the user's current learning status. This makes more effective learning possible by customizing the means of delivery based on the current learning status. Some or all of the above processing in the delivery unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the delivery unit can input the user's current learning status into a generative AI and have the generative AI perform the customization of the optimal means of delivery.

[0091] The content delivery unit can estimate the user's emotions and adjust the order in which educational content is delivered based on the estimated emotions. For example, if the user is relaxed, the delivery unit can prioritize providing more difficult content. If the user is stressed, the delivery unit can also prioritize providing less difficult content. If the user is excited, the delivery unit can also prioritize providing challenging content. By adjusting the delivery order according to the user's emotions, more effective learning becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using generative AI or not. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI adjust the order in which educational content is delivered.

[0092] The service provider can provide optimal educational content by taking into account the user's geographical location. For example, if the user is in a specific region, the service provider can provide educational content that is popular in that region. The service provider can also provide region-specific educational content based on the user's geographical location. If the user is traveling, the service provider can also provide educational content that is useful at their travel destination. This enables more effective learning by providing optimal educational content based on geographical location. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input the user's geographical location information into a generative AI and have the generative AI perform the task of providing optimal educational content.

[0093] The service provider can analyze a user's social media activity and provide relevant educational content. For example, it can provide educational content related to skills and goals that the user frequently mentions on social media. It can also provide educational content related to topics of interest based on the user's social media activity. It can also provide educational content related to skills and goals that the user's social media followers and friends are learning. This enables more effective learning by providing relevant educational content based on social media activity. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider can input the user's social media activity into a generative AI and have the generative AI provide the most suitable educational content.

[0094] The tracking unit can estimate the user's emotions and adjust the learning progress tracking method based on the estimated user emotions. For example, if the user is relaxed, the tracking unit can provide a detailed progress report. If the user is stressed, the tracking unit can also provide a concise progress report. If the user is excited, the tracking unit can also provide a visually stimulating progress report. This allows for more effective management of learning progress by adjusting the tracking method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using or without a generative AI. For example, the tracking unit can input user emotion data into a generative AI and have the generative AI adjust the learning progress tracking method.

[0095] The tracking unit can analyze the user's past learning progress and select the optimal tracking method. For example, the tracking unit can select the optimal tracking method based on tracking methods that were effective for the user in the past. The tracking unit can also extract effective tracking methods from the user's past learning progress and select the optimal tracking method. The tracking unit can also analyze the user's past learning progress and select the optimal tracking method according to the learning progress. This enables efficient management of learning progress by selecting the optimal tracking method based on past learning progress. Some or all of the above processing in the tracking unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the tracking unit can input the user's past learning progress into a generative AI and have the generative AI select the optimal tracking method.

[0096] The tracking unit can customize the means of tracking learning progress based on the user's current learning status. For example, the tracking unit can customize tracking means related to the skills the user is currently learning. The tracking unit can also customize the optimal tracking means according to the user's learning progress. The tracking unit can also customize effective tracking means based on the user's current learning status. This makes it possible to manage learning progress more effectively by customizing tracking means based on the current learning status. Some or all of the above processing in the tracking unit may be performed using generative AI or not. For example, the tracking unit can input the user's current learning status into the generative AI and have the generative AI perform the customization of the optimal tracking means.

[0097] The tracking unit can estimate the user's emotions and adjust the display method of learning progress based on the estimated user emotions. For example, if the user is relaxed, the tracking unit can provide a detailed progress display. If the user is stressed, the tracking unit can also provide a concise progress display. If the user is excited, the tracking unit can also provide a visually stimulating progress display. This allows for more effective management of learning progress by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using the generative AI or not. For example, the tracking unit can input user emotion data into the generative AI and have the generative AI adjust the display method of learning progress.

[0098] The tracking unit can provide an optimal method for tracking learning progress, taking into account the user's geographical location. For example, if the user is in a specific region, the tracking unit can provide a method for tracking learning progress in that region. The tracking unit can also provide a method for tracking region-specific learning progress based on the user's geographical location. If the user is traveling, the tracking unit can also provide a method for tracking learning progress at their travel destination. This enables more effective management of learning progress by providing an optimal tracking method based on geographical location. Some or all of the above processing in the tracking unit may be performed using a generative AI, or not. For example, the tracking unit can input the user's geographical location information into a generative AI and have the generative AI perform the task of providing an optimal method for tracking learning progress.

[0099] The tracking unit can analyze a user's social media activity and provide means to track relevant learning progress. For example, the tracking unit can track learning progress related to skills and goals that the user frequently mentions on social media. The tracking unit can also track learning progress related to topics of interest from the user's social media activity. The tracking unit can also track learning progress related to skills and goals that the user's social media followers and friends are learning. This enables more effective management of learning progress by providing means to track relevant learning progress based on social media activity. Some or all of the above processing in the tracking unit may be performed using generative AI or not. For example, the tracking unit can input the user's social media activity into a generative AI and have the generative AI perform the task of providing the optimal means to track learning progress.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The reception desk can dynamically adjust learning priorities based on user input. For example, if a user inputs that they want to learn multiple skills, the reception desk can suggest the most effective learning sequence based on the user's current skill level and learning objectives. Furthermore, if a user shows a strong interest in a particular skill, the system can adjust the learning priorities to prioritize that skill. Additionally, if a user is in a hurry, the system can prioritize suggesting skills that can be learned effectively in a short period. This provides a flexible learning plan tailored to the user's needs, enabling efficient learning.

[0102] The generation unit can create optimal educational content based on the user's learning style. For example, users who prefer visual learning can be provided with content that makes extensive use of videos and infographics. Users who prefer practical learning can be provided with interactive exercises and simulations. Furthermore, users who prefer reading can be provided with text-based learning materials and ebooks. This ensures that the optimal educational content is provided to match the user's learning style, improving learning effectiveness.

[0103] The system can provide real-time feedback based on the user's learning progress. For example, it can provide immediate feedback as soon as the user completes a specific task. It can also provide detailed explanations and additional practice problems for questions the user answered incorrectly. Furthermore, when a user acquires a specific skill, it can suggest the next steps related to that skill. This allows users to learn while receiving real-time feedback, improving learning effectiveness.

[0104] The tracking unit can provide a dashboard to visualize the user's learning progress. For example, it can display completed tasks and acquired skills using graphs and charts. It can also display the user's learning pace and progress in real time. Furthermore, it can visually display the degree of achievement against the goals set by the user. This allows users to grasp their learning progress at a glance and maintain motivation as they continue learning.

[0105] The tracking unit can dynamically adjust the learning plan based on the user's learning progress. For example, if a user is struggling with a particular topic, it can provide additional resources and supplementary explanations related to that topic. Furthermore, if the user achieves a goal, it can set a new goal and suggest the next step. It can also adjust the learning plan schedule according to the user's learning pace. This provides a flexible learning plan tailored to the user's progress, enabling efficient learning.

[0106] The reception system can estimate the user's emotions and adjust the priority of input based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. If the user is in a hurry, it can prioritize voice input, allowing them to quickly input the skills or goals they want to learn. This allows for more appropriate input by adjusting the priority of input according to the user's emotions.

[0107] The generation unit can estimate the user's emotions and adjust the difficulty level of the educational content based on those emotions. For example, if the user is relaxed, it can provide slightly more difficult content. If the user is stressed, it can provide easier content. If the user is excited, it can provide challenging content. By adjusting the difficulty level of the educational content according to the user's emotions, more appropriate learning becomes possible.

[0108] The delivery unit can estimate the user's emotions and adjust the delivery method of educational content based on those estimates. For example, if the user is relaxed, the content can be delivered in the form of a video lecture. If the user is stressed, the content can be delivered in the form of an interactive exercise. If the user is excited, the content can be delivered in the form of a game. By adjusting the delivery method according to the user's emotions, more effective learning becomes possible.

[0109] The tracking unit can estimate the user's emotions and adjust the learning progress tracking method based on the estimated emotions. For example, if the user is relaxed, it can provide a detailed progress report. If the user is stressed, it can provide a concise progress report. If the user is excited, it can provide a visually stimulating progress report. This allows for more effective management of learning progress by adjusting the tracking method according to the user's emotions.

[0110] The content delivery system can estimate the user's emotions and adjust the order in which educational content is delivered based on those estimates. For example, if the user is relaxed, it can prioritize providing more challenging content. If the user is stressed, it can prioritize providing less challenging content. If the user is excited, it can prioritize providing challenging content. By adjusting the order of content delivery according to the user's emotions, more effective learning becomes possible.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The reception desk receives user input. User input includes, for example, the skills and goals the user wants to learn. Specifically, it accepts user input such as "I want to learn programming" or "I want to improve my data analysis skills." Step 2: The generation unit generates customized educational content based on the information received by the reception unit. The generation unit generates an optimal learning plan considering the user's profile, past learning history, and current skill level. For example, it provides content tailored to the user's level, such as a programming course for beginners or a data analysis course for intermediate users. The generation unit uses a generation AI to analyze the user's input information and generate customized educational content that meets individual needs. Step 3: The provider unit provides the educational content generated by the generator unit. The provider unit provides educational content in various forms, such as video lectures, interactive exercises, and real-time feedback. The generated educational content is provided so that users can learn at their own pace. Step 4: The tracking unit tracks learning progress based on the educational content provided by the delivery unit and adjusts the learning plan as needed. The tracking unit provides additional resources and supplementary explanations if the user is struggling with a particular topic. It sets new goals when the user achieves their initial goals and supports further skill development.

[0113] 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.

[0114] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and tracking unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives user input. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates customized educational content. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated educational content. The tracking unit is implemented by the specific processing unit 290 of the data processing unit 12 and tracks learning progress and adjusts the learning plan as needed. The reception unit can estimate the user's emotions and adjust the priority of input content based on the estimated emotions. Emotion estimation is implemented by the specific processing unit 290 of the data processing unit 12, for example. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0120] 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.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0122] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] 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.

[0124] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and tracking unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives user input. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates customized educational content. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the generated educational content. The tracking unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and tracks learning progress and adjusts the learning plan as needed. The reception unit can estimate the user's emotions and adjust the priority of input content based on the estimated emotions. Emotion estimation is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0136] 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.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0138] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] 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.

[0140] 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.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and tracking unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives user input. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates customized educational content. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated educational content. The tracking unit is implemented by the specific processing unit 290 of the data processing unit 12 and tracks learning progress and adjusts the learning plan as needed. The reception unit can estimate the user's emotions and adjust the priority of input content based on the estimated emotions. Emotion estimation is implemented by the specific processing unit 290 of the data processing unit 12, for example. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0152] 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.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0154] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] 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.

[0156] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] 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.

[0158] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] 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.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and tracking unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives user input. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates customized educational content. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the generated educational content. The tracking unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and tracks learning progress and adjusts the learning plan as needed. The reception unit can estimate the user's emotions and adjust the priority of input content based on the estimated emotions. Emotion estimation is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0166] 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.

[0167] Figure 9 shows the 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.

[0168] 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.

[0169] 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.

[0170] 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, and motorcycles, 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 based, for example, 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.

[0171] 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."

[0172] 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.

[0173] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] 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 other things 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.

[0183] 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.

[0184] (Note 1) A reception area that receives user input, A generation unit that generates customized educational content based on the information received by the reception unit, A providing unit that provides educational content generated by the generation unit, The system includes a tracking unit that tracks learning progress based on the educational content provided by the aforementioned provisioning unit and adjusts the learning plan as necessary. A system characterized by the following features. (Note 2) The generating unit is It generates an optimal learning plan considering the user's profile, past learning history, and current skill level. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, We offer educational content in various formats, including video lectures, interactive exercises, and real-time feedback. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned tracking unit is Track the user's learning progress and provide additional resources and supplementary explanations if they get stuck on a particular topic. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned tracking unit is When a user achieves a goal, new goals are set to support further skill development. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We offer customized features for businesses, providing flexible learning schedules and customized educational content tailored to the needs of each company. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Providing online learning flexibility to accommodate the spread of remote work. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and adjusts the priority of input content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is Filter input based on the user's current learning status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is Provide relevant input options by taking the user's geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is Analyzes users' social media activity and suggests relevant inputs. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and adjusts the difficulty level of educational content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It analyzes the user's past learning history and generates the optimal learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is Apply different educational content generation algorithms depending on the user's current skill level. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is It estimates user sentiment and adjusts the format of educational content based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is Prioritize educational content based on when users submit their content. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is The order of educational content is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, We estimate user emotions and adjust the delivery method of educational content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, The system selects the optimal delivery method by referring to the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, Customize the delivery method of educational content based on the user's current learning status. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, It estimates the user's emotions and adjusts the order in which educational content is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, We provide optimal educational content by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, Analyze users' social media activity and provide relevant educational content. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned tracking unit is It estimates the user's emotions and adjusts how learning progress is tracked based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned tracking unit is Analyze the user's past learning progress and select the optimal tracking method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned tracking unit is Customize the method of tracking learning progress based on the user's current learning status. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned tracking unit is It estimates the user's emotions and adjusts how learning progress is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned tracking unit is It provides an optimal method for tracking learning progress, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned tracking unit is It analyzes users' social media activity and provides a means to track relevant learning progress. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that receives user input, A generation unit that generates customized educational content based on the information received by the reception unit, A providing unit that provides educational content generated by the generation unit, The system includes a tracking unit that tracks learning progress based on the educational content provided by the aforementioned provisioning unit and adjusts the learning plan as necessary. A system characterized by the following features.

2. The generating unit is It generates an optimal learning plan considering the user's profile, past learning history, and current skill level. The system according to feature 1.

3. The aforementioned supply unit is, We offer educational content in various formats, including video lectures, interactive exercises, and real-time feedback. The system according to feature 1.

4. The aforementioned tracking unit is Track the user's learning progress and provide additional resources and supplementary explanations if they get stuck on a particular topic. The system according to feature 1.

5. The aforementioned tracking unit is When a user achieves a goal, new goals are set to support further skill development. The system according to feature 1.

6. The aforementioned supply unit is, We offer customized features for businesses, providing flexible learning schedules and customized educational content tailored to the needs of each company. The system according to feature 1.

7. The aforementioned supply unit is, Providing online learning flexibility to accommodate the spread of remote work. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and adjusts the priority of input content based on the estimated user emotions. The system according to feature 1.

9. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

10. The aforementioned reception unit is Filter input based on the user's current learning status and areas of interest. The system according to feature 1.

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