system

Abstract

The system according to this embodiment aims to provide customized educational content that meets the individual needs of learners. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, a feedback unit, and a content generation unit. The analysis unit analyzes the user's learning history and progress. The generation unit generates a customized learning plan based on the data analyzed by the analysis unit. The feedback unit provides real-time feedback based on the learning plan generated by the generation unit. The content generation unit dynamically generates learning content from the learner's behavior and performance data.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to provide customized educational content according to the individual needs of learners.

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

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a generation unit, a feedback unit, and a content generation unit. The analysis unit analyzes the user's learning history and progress. The generation unit generates a customized learning plan based on the data analyzed by the analysis unit. The feedback unit provides real-time feedback based on the learning plan generated by the generation unit. The content generation unit dynamically generates learning content from the learner's behavior and performance data. [Effects of the Invention]

[0007] The system according to this embodiment can provide customized educational content that meets the individual needs of 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, etc. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 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 �2. 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 aims to enable users to efficiently and effectively learn new skills, facilitating career changes and skill development. The online education platform uses AI to analyze the user's learning history and progress to understand each learner's needs. Next, the AI ​​generates a customized learning plan and provides it to the user. This learning plan is optimized based on the user's learning history and progress, allowing for efficient learning. Furthermore, the AI ​​provides real-time feedback and adaptive learning support. This allows users to receive optimal support according to their learning progress. For example, the online education platform caters to the needs of a wide range of users, including students seeking efficient learning methods, professionals considering career changes, and individuals aiming for self-improvement. It can also be used by organizations such as educational institutions, corporate HR departments, and vocational training centers. For instance, the online education platform is expected to reduce learning time, improve learning outcomes, and increase the success rate of career transitions. Furthermore, it enables AI to understand and respond to individual learning needs and promotes a data-driven approach in the field of education. For example, online education platforms utilize generative AI to dynamically generate learning content from learner behavior and performance data. This provides each learner with an optimal learning experience, achieving personalized education. As a result, online education platforms can provide customized educational content tailored to learners' needs, supporting efficient and effective learning.

[0029] The online education platform according to this embodiment comprises an analysis unit, a generation unit, a feedback unit, and a content generation unit. The analysis unit analyzes the user's learning history and progress. For example, the analysis unit can collect data such as past learning content, learning time, and learning outcomes to identify the user's learning patterns. The analysis unit can also monitor the user's learning progress and achievements in real time to understand the progress of learning. Furthermore, based on the user's learning history, the analysis unit can identify learning trends and challenges and use this to optimize the learning plan. The generation unit generates a customized learning plan based on the data analyzed by the analysis unit. For example, the generation unit can create an optimal learning plan according to the user's learning goals and learning style. The generation unit can also dynamically adjust the learning plan according to the user's learning pace and progress. Furthermore, the generation unit can provide a plan that meets individual learning needs, taking into account the user's learning history. The feedback unit provides real-time feedback based on the learning plan generated by the generation unit. For example, the feedback unit can provide immediate evaluations and advice according to the user's learning progress. Furthermore, the feedback unit can provide correction instructions and suggestions for improvement regarding the user's learning content. In addition, the feedback unit can evaluate the user's learning outcomes and suggest the next learning steps. The content generation unit dynamically generates learning content from the learner's behavior and outcome data. For example, the content generation unit can automatically create optimal learning content based on the user's learning history and progress. The content generation unit can also update content in real time according to the user's learning needs. Furthermore, the content generation unit can use generation AI to generate content that provides the learner with the optimal learning experience. As a result, the online education platform according to this embodiment can efficiently and effectively advance learning by analyzing the user's learning history and progress, generating customized learning plans, providing real-time feedback, and dynamically generating learning content.

[0030] The analytics department analyzes users' learning history and progress. For example, it can collect data such as past learning content, learning time, and learning outcomes to identify users' learning patterns. Specifically, it analyzes in detail which subjects or topics users spend the most time on, what problems they struggle with, and what learning methods are effective. This allows for a clear understanding of users' strengths and weaknesses, enabling the department to address individual learning needs. The analytics department can also monitor users' learning progress and achievements in real time to understand their learning status. For example, it can determine how far along a user is in achieving their set learning goals and what kind of support they need to reach those goals. Furthermore, based on users' learning history, the analytics department can identify learning trends and challenges, which can be used to optimize learning plans. For example, it can use past data to evaluate the effectiveness of learning at specific times or situations and reflect this in future learning plans. This allows the analytics department to maximize users' learning efficiency and provide effective learning support.

[0031] The generation unit generates customized learning plans based on data analyzed by the analysis unit. For example, the generation unit can create an optimal learning plan according to the user's learning goals and learning style. Specifically, it proposes daily learning schedules and learning content based on the user's short-term goals and long-term learning plans. The generation unit can also dynamically adjust the learning plan according to the user's learning pace and progress. For example, if the user is progressing faster than planned, it will provide additional tasks to move on to the next step, and conversely, if they are falling behind, it will suggest content for review and supplementary learning. Furthermore, the generation unit can provide plans that meet individual learning needs by considering the user's learning history. For example, for a user who has difficulty in a particular area, it will create a learning plan specialized for that area to support them in overcoming their difficulties efficiently. In this way, the generation unit can provide learning plans optimized for each individual user and support effective learning.

[0032] The feedback unit provides real-time feedback based on the learning plan generated by the generation unit. For example, the feedback unit can provide immediate evaluations and advice according to the user's learning progress. Specifically, it determines whether the user's answers are correct or incorrect, providing advice on how to proceed to the next step if the answer is correct, and showing the cause of the error and the correct way to answer if the answer is incorrect. The feedback unit can also provide correction instructions and suggestions for improvement regarding the user's learning. For example, if the user does not understand a particular concept, it can provide additional explanations and examples to support deeper understanding. Furthermore, the feedback unit can evaluate the user's learning outcomes and suggest the next learning steps. For example, it can introduce the next level of challenges or new topics to users who have achieved certain learning goals, promoting continuous learning. In this way, the feedback unit can support the user's learning in real time and provide an effective learning experience.

[0033] The content generation unit dynamically generates learning content from learner behavior and performance data. For example, the content generation unit can automatically create optimal learning content based on the user's learning history and progress. Specifically, it selects the next topics and problems to be studied based on what the user has learned in the past and their current level of understanding, providing content that meets individual learning needs. The content generation unit can also update content in real time according to the user's learning needs. For example, if a user has difficulty in a particular area, it provides additional learning materials and practice problems related to that area to support deeper understanding. Furthermore, the content generation unit can use generation AI to generate content that provides learners with the optimal learning experience. For example, the generation AI automatically creates learning materials and problems that meet individual learning needs based on the user's learning history and progress data, and provides them to the user. In this way, the content generation unit can provide learning content optimized for each individual user, supporting effective learning.

[0034] The analysis unit can analyze the user's past learning history and select the optimal analysis algorithm. For example, the analysis unit can select the optimal analysis algorithm based on the learning methods the user has used in the past. Furthermore, the analysis unit can extract specific patterns from the user's past learning history and select an analysis algorithm based on those patterns. In addition, the analysis unit can cluster the user's past learning history and select the optimal analysis algorithm for each cluster. This improves the accuracy of the analysis by analyzing the user's past learning history and selecting the optimal analysis algorithm. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past learning history data into a generating AI and have the generating AI select the optimal analysis algorithm.

[0035] The analysis unit can filter learning history based on the user's current learning status and areas of interest. For example, the analysis unit can acquire the user's current learning status in real time and analyze only the relevant learning history. The analysis unit can also prioritize the analysis of relevant learning history based on the user's areas of interest. Furthermore, the analysis unit can combine the user's current learning status and areas of interest to filter for the most relevant learning history. This allows for more relevant analysis by filtering based on the user's current learning status and areas of interest. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's current learning status data into a generating AI and have the generating AI perform the filtering.

[0036] The analysis unit can prioritize the analysis of highly relevant history by considering the user's geographical location information when analyzing learning history. For example, the analysis unit can prioritize the analysis of relevant learning history based on the user's current location. Furthermore, the analysis unit can also prioritize the analysis of highly relevant learning history by considering the user's past travel history. In addition, the analysis unit can combine the user's geographical location information and learning history to perform optimal analysis. This allows for more appropriate analysis by prioritizing the analysis of highly relevant history by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information data into a generating AI and have the generating AI perform a priority analysis of highly relevant history.

[0037] The analysis unit can analyze a user's social media activity and retrieve relevant history when analyzing learning history. For example, the analysis unit can extract relevant learning history from a user's social media activity. The analysis unit can also analyze a user's areas of interest on social media and prioritize the analysis of relevant learning history. Furthermore, the analysis unit can combine the user's social media activity and learning history to perform optimal analysis. This allows for more appropriate analysis by analyzing the user's social media activity and retrieving relevant history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI and have the generating AI retrieve relevant history.

[0038] The generation unit can adjust the level of detail of a learning plan based on the importance of the learning content when generating the learning plan. For example, the generation unit can generate a detailed learning plan for important learning content. It can also generate a simple learning plan for less important learning content. Furthermore, the generation unit can adjust the level of detail of the plan in stages according to the importance of the learning content. By adjusting the level of detail of the plan based on the importance of the learning content, a more appropriate learning plan is generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input learning content importance data into a generation AI and have the generation AI perform the level of detail adjustment.

[0039] The generation unit can apply different generation algorithms depending on the category of learning content when generating a learning plan. For example, the generation unit can apply a specific generation algorithm to science-related learning content. It can also apply a different generation algorithm to humanities-related learning content. Furthermore, the generation unit can select the optimal generation algorithm depending on the category of learning content. By applying different generation algorithms depending on the category of learning content, a more appropriate learning plan can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input learning content category data into a generation AI and have the generation AI execute the application of a generation algorithm.

[0040] The generation unit can determine the priority of learning plans based on the submission deadlines for the learning content when generating learning plans. For example, the generation unit can prioritize incorporating learning content with approaching submission deadlines into the plan. It can also postpone learning content with ample time for submission. Furthermore, the generation unit can adjust the priority of the learning plans in stages based on the submission deadlines. This allows for the generation of more appropriate learning plans by determining the priority of the plans based on the submission deadlines of the learning content. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input learning content submission deadline data into a generation AI and have the generation AI perform the priority determination.

[0041] The generation unit can adjust the order of learning plans based on the relevance of the learning content when generating a learning plan. For example, the generation unit can incorporate highly relevant learning content into the plan consecutively. It can also incorporate less relevant learning content in a distributed manner. Furthermore, the generation unit can adjust the order of the plans step by step based on the relevance of the learning content. By adjusting the order of the plans based on the relevance of the learning content, a more appropriate learning plan is generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the relevance of the learning content into a generation AI and have the generation AI perform the order adjustment.

[0042] The feedback unit can adjust the level of detail of the feedback based on the importance of the learning content when providing feedback. For example, the feedback unit can provide detailed feedback for important learning content. It can also provide simple feedback for less important learning content. Furthermore, the feedback unit can adjust the level of detail of the feedback in stages according to the importance of the learning content. This allows for more appropriate feedback to be provided by adjusting the level of detail of the feedback based on the importance of the learning content. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input learning content importance data into a generating AI and have the generating AI perform the adjustment of the level of detail.

[0043] The feedback unit can apply different feedback algorithms depending on the category of the learning content when providing feedback. For example, the feedback unit can apply a specific feedback algorithm to science-related learning content. It can also apply a different feedback algorithm to humanities-related learning content. Furthermore, the feedback unit can select the optimal feedback algorithm depending on the category of the learning content. By applying different feedback algorithms depending on the category of the learning content, more appropriate feedback is provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input learning content category data into a generating AI and have the generating AI execute the application of the feedback algorithm.

[0044] The feedback unit can determine the priority of feedback based on the submission date of the learning content when providing feedback. For example, the feedback unit can prioritize providing feedback for learning content with an approaching submission deadline. Conversely, the feedback unit can also postpone providing feedback for learning content with ample time before the submission deadline. Furthermore, the feedback unit can adjust the priority of feedback in stages based on the submission date. This allows for the provision of more appropriate feedback by determining the priority of feedback based on the submission date of the learning content. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input learning content submission date data into a generating AI and have the generating AI perform the priority determination.

[0045] The feedback unit can adjust the order of feedback based on the relevance of the learning content when providing feedback. For example, the feedback unit can provide feedback sequentially for highly relevant learning content. It can also provide feedback in a distributed manner for less relevant learning content. Furthermore, the feedback unit can adjust the order of feedback step by step based on the relevance of the learning content. By adjusting the order of feedback based on the relevance of the learning content, more appropriate feedback can be provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input data on the relevance of the learning content into a generating AI and have the generating AI perform the order adjustment.

[0046] The content generation unit can improve the accuracy of content generation by considering the interrelationships of learning content. For example, the content generation unit can analyze the interrelationships of learning content and consistently generate related content. The content generation unit can also optimize the content generation order by considering the interrelationships of learning content. Furthermore, the content generation unit can generate related content sequentially based on the interrelationships of learning content. By improving the accuracy of generation by considering the interrelationships of learning content, more appropriate learning content is generated. Some or all of the above-described processes in the content generation unit may be performed using AI, for example, or without AI. For example, the content generation unit can input data on the interrelationships of learning content into a generation AI and have the generation AI perform the task of improving the generation accuracy.

[0047] The content generation unit can generate learning content while considering the learner's attribute information. For example, the content generation unit can generate content of appropriate difficulty level according to the learner's age. It can also generate content that reinforces relevant skills according to the learner's occupation. Furthermore, the content generation unit can generate content in the optimal format according to the learner's learning style. By considering the learner's attribute information during generation, more appropriate learning content is generated. Some or all of the above processing in the content generation unit may be performed using AI, for example, or without AI. For example, the content generation unit can input learner attribute information data into a generation AI and have the generation AI perform the generation.

[0048] The content generation unit can generate learning content while considering the geographical distribution of the learning content. For example, the content generation unit can analyze the geographical distribution of the learning content and generate region-specific content. Furthermore, the content generation unit can generate content tailored to the characteristics of each region, taking into account the geographical distribution of the learning content. In addition, the content generation unit can generate content that meets the needs of each region based on the geographical distribution of the learning content. This allows for the generation of more appropriate learning content by considering the geographical distribution of the learning content. Some or all of the above-described processes in the content generation unit may be performed using AI, for example, or without AI. For example, the content generation unit can input geographical distribution data of the learning content into a generation AI and have the generation AI perform the generation.

[0049] The content generation unit can improve the accuracy of content generation by referring to relevant literature on the learning content during the generation of learning content. For example, the content generation unit can refer to literature related to the learning content and generate accurate content. Furthermore, the content generation unit can generate detailed content based on relevant literature on the learning content. In addition, the content generation unit can refer to relevant literature on the learning content and generate content that includes the latest information. This improves the accuracy of content generation by referring to relevant literature on the learning content, resulting in the generation of more appropriate learning content. Some or all of the above-described processes in the content generation unit may be performed using AI, for example, or without AI. For example, the content generation unit can input data on relevant literature on the learning content into a generation AI and have the generation AI perform the task of improving the accuracy of the generation.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The analytics department can consider the user's learning style when analyzing their learning history and progress. For example, it can provide analysis results using graphs and charts for visual learners and audio feedback for auditory learners. Furthermore, the analytics department can suggest the optimal learning method based on the user's learning style. It can also adjust the content and format of the learning plan according to the user's learning style. This enables analysis tailored to the user's learning style, improving the effectiveness of learning.

[0052] The feedback unit can analyze a user's learning history and evaluate the effectiveness of past feedback. For example, it can analyze how previously provided feedback affected learning outcomes and identify effective feedback patterns. Furthermore, the feedback unit can adjust the content and method of future feedback based on the effectiveness of past feedback. In addition, the feedback unit can provide feedback tailored to individual feedback needs based on the user's learning history. This allows for improved learning effectiveness by evaluating the effectiveness of past feedback and optimizing future feedback.

[0053] The analytics department can evaluate the reliability of user learning histories when analyzing them. For example, it can evaluate the reliability of user-reported learning histories and prioritize the analysis of reliable data. The analytics department can also adjust the accuracy of the analysis results based on the reliability of the learning histories. Furthermore, the analytics department can utilize other data sources to supplement less reliable data. This improves the accuracy of the analysis by evaluating the reliability of learning histories and conducting analysis based on reliable data.

[0054] The feedback unit can analyze the user's learning history and assess their learning motivation. For example, it can identify changes in the user's motivation from past learning history and provide encouraging feedback if their motivation is low. Conversely, if their motivation is high, it can provide feedback that encourages further challenges. Furthermore, the feedback unit can adjust the content and method of feedback according to the user's motivation. This allows for improved learning effectiveness by assessing the user's motivation and providing appropriate feedback.

[0055] The analytics department can evaluate the consistency of a user's learning history when analyzing it. For example, it can assess whether a user's learning history is consistent and prioritize the analysis of consistent data. The analytics department can also utilize other data sources to supplement inconsistent data. Furthermore, the analytics department can adjust the reliability of the analysis results based on the consistency of the learning history. This improves the accuracy of the analysis by evaluating the consistency of the learning history and conducting analysis based on consistent data.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The analytics department analyzes the user's learning history and progress. For example, the analytics department can collect data such as past learning content, learning time, and learning outcomes to identify the user's learning patterns. The analytics department can also monitor the user's learning progress and achievements in real time to understand their learning status. Furthermore, based on the user's learning history, the analytics department can identify learning trends and challenges, which can be used to optimize the learning plan. Step 2: The generation unit generates a customized learning plan based on the data analyzed by the analysis unit. For example, the generation unit can create an optimal learning plan according to the user's learning goals and learning style. The generation unit can also dynamically adjust the learning plan according to the user's learning pace and progress. Furthermore, the generation unit can provide a plan that meets individual learning needs by taking into account the user's learning history. Step 3: The feedback unit provides real-time feedback based on the learning plan generated by the generation unit. For example, the feedback unit can provide immediate evaluations and advice according to the user's learning progress. The feedback unit can also suggest corrections and improvements to the user's learning content. Furthermore, the feedback unit can evaluate the user's learning outcomes and suggest the next learning steps. Step 4: The content generation unit dynamically generates learning content from learner behavior and performance data. For example, the content generation unit can automatically create optimal learning content based on the user's learning history and progress. It can also update content in real time according to the user's learning needs. Furthermore, the content generation unit can use generation AI to generate content that provides learners with the optimal learning experience.

[0058] (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 aims to enable users to efficiently and effectively learn new skills, facilitating career changes and skill development. The online education platform uses AI to analyze the user's learning history and progress to understand each learner's needs. Next, the AI ​​generates a customized learning plan and provides it to the user. This learning plan is optimized based on the user's learning history and progress, allowing for efficient learning. Furthermore, the AI ​​provides real-time feedback and adaptive learning support. This allows users to receive optimal support according to their learning progress. For example, the online education platform caters to the needs of a wide range of users, including students seeking efficient learning methods, professionals considering career changes, and individuals aiming for self-improvement. It can also be used by organizations such as educational institutions, corporate HR departments, and vocational training centers. For instance, the online education platform is expected to reduce learning time, improve learning outcomes, and increase the success rate of career transitions. Furthermore, it enables AI to understand and respond to individual learning needs and promotes a data-driven approach in the field of education. For example, online education platforms utilize generative AI to dynamically generate learning content from learner behavior and performance data. This provides each learner with an optimal learning experience, achieving personalized education. As a result, online education platforms can provide customized educational content tailored to learners' needs, supporting efficient and effective learning.

[0059] The online education platform according to this embodiment comprises an analysis unit, a generation unit, a feedback unit, and a content generation unit. The analysis unit analyzes the user's learning history and progress. For example, the analysis unit can collect data such as past learning content, learning time, and learning outcomes to identify the user's learning patterns. The analysis unit can also monitor the user's learning progress and achievements in real time to understand the progress of learning. Furthermore, based on the user's learning history, the analysis unit can identify learning trends and challenges and use this to optimize the learning plan. The generation unit generates a customized learning plan based on the data analyzed by the analysis unit. For example, the generation unit can create an optimal learning plan according to the user's learning goals and learning style. The generation unit can also dynamically adjust the learning plan according to the user's learning pace and progress. Furthermore, the generation unit can provide a plan that meets individual learning needs, taking into account the user's learning history. The feedback unit provides real-time feedback based on the learning plan generated by the generation unit. For example, the feedback unit can provide immediate evaluations and advice according to the user's learning progress. Furthermore, the feedback unit can provide correction instructions and suggestions for improvement regarding the user's learning content. In addition, the feedback unit can evaluate the user's learning outcomes and suggest the next learning steps. The content generation unit dynamically generates learning content from the learner's behavior and outcome data. For example, the content generation unit can automatically create optimal learning content based on the user's learning history and progress. The content generation unit can also update content in real time according to the user's learning needs. Furthermore, the content generation unit can use generation AI to generate content that provides the learner with the optimal learning experience. As a result, the online education platform according to this embodiment can efficiently and effectively advance learning by analyzing the user's learning history and progress, generating customized learning plans, providing real-time feedback, and dynamically generating learning content.

[0060] The analytics department analyzes users' learning history and progress. For example, it can collect data such as past learning content, learning time, and learning outcomes to identify users' learning patterns. Specifically, it analyzes in detail which subjects or topics users spend the most time on, what problems they struggle with, and what learning methods are effective. This allows for a clear understanding of users' strengths and weaknesses, enabling the department to address individual learning needs. The analytics department can also monitor users' learning progress and achievements in real time to understand their learning status. For example, it can determine how far along a user is in achieving their set learning goals and what kind of support they need to reach those goals. Furthermore, based on users' learning history, the analytics department can identify learning trends and challenges, which can be used to optimize learning plans. For example, it can use past data to evaluate the effectiveness of learning at specific times or situations and reflect this in future learning plans. This allows the analytics department to maximize users' learning efficiency and provide effective learning support.

[0061] The generation unit generates customized learning plans based on data analyzed by the analysis unit. For example, the generation unit can create an optimal learning plan according to the user's learning goals and learning style. Specifically, it proposes daily learning schedules and learning content based on the user's short-term goals and long-term learning plans. The generation unit can also dynamically adjust the learning plan according to the user's learning pace and progress. For example, if the user is progressing faster than planned, it will provide additional tasks to move on to the next step, and conversely, if they are falling behind, it will suggest content for review and supplementary learning. Furthermore, the generation unit can provide plans that meet individual learning needs by considering the user's learning history. For example, for a user who has difficulty in a particular area, it will create a learning plan specialized for that area to support them in overcoming their difficulties efficiently. In this way, the generation unit can provide learning plans optimized for each individual user and support effective learning.

[0062] The feedback unit provides real-time feedback based on the learning plan generated by the generation unit. For example, the feedback unit can provide immediate evaluations and advice according to the user's learning progress. Specifically, it determines whether the user's answers are correct or incorrect, providing advice on how to proceed to the next step if the answer is correct, and showing the cause of the error and the correct way to answer if the answer is incorrect. The feedback unit can also provide correction instructions and suggestions for improvement regarding the user's learning. For example, if the user does not understand a particular concept, it can provide additional explanations and examples to support deeper understanding. Furthermore, the feedback unit can evaluate the user's learning outcomes and suggest the next learning steps. For example, it can introduce the next level of challenges or new topics to users who have achieved certain learning goals, promoting continuous learning. In this way, the feedback unit can support the user's learning in real time and provide an effective learning experience.

[0063] The content generation unit dynamically generates learning content from learner behavior and performance data. For example, the content generation unit can automatically create optimal learning content based on the user's learning history and progress. Specifically, it selects the next topics and problems to be studied based on what the user has learned in the past and their current level of understanding, providing content that meets individual learning needs. The content generation unit can also update content in real time according to the user's learning needs. For example, if a user has difficulty in a particular area, it provides additional learning materials and practice problems related to that area to support deeper understanding. Furthermore, the content generation unit can use generation AI to generate content that provides learners with the optimal learning experience. For example, the generation AI automatically creates learning materials and problems that meet individual learning needs based on the user's learning history and progress data, and provides them to the user. In this way, the content generation unit can provide learning content optimized for each individual user, supporting effective learning.

[0064] The analysis unit can estimate the user's emotions and adjust the analysis method of the learning history based on the estimated user emotions. For example, if the user is stressed, the analysis unit can apply a simplified analysis method to reduce the burden. If the user is relaxed, the analysis unit can also apply a detailed analysis method to provide deeper insights. Furthermore, if the user is focused, the analysis unit can perform a detailed analysis in real time and provide immediate feedback. This allows for more appropriate analysis by adjusting the analysis method of the learning history according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0065] The analysis unit can analyze the user's past learning history and select the optimal analysis algorithm. For example, the analysis unit can select the optimal analysis algorithm based on the learning methods the user has used in the past. Furthermore, the analysis unit can extract specific patterns from the user's past learning history and select an analysis algorithm based on those patterns. In addition, the analysis unit can cluster the user's past learning history and select the optimal analysis algorithm for each cluster. This improves the accuracy of the analysis by analyzing the user's past learning history and selecting the optimal analysis algorithm. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past learning history data into a generating AI and have the generating AI select the optimal analysis algorithm.

[0066] The analysis unit can filter learning history based on the user's current learning status and areas of interest. For example, the analysis unit can acquire the user's current learning status in real time and analyze only the relevant learning history. The analysis unit can also prioritize the analysis of relevant learning history based on the user's areas of interest. Furthermore, the analysis unit can combine the user's current learning status and areas of interest to filter for the most relevant learning history. This allows for more relevant analysis by filtering based on the user's current learning status and areas of interest. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's current learning status data into a generating AI and have the generating AI perform the filtering.

[0067] The analysis unit can estimate the user's emotions and determine the priority of the learning history to analyze based on the estimated user emotions. For example, if the user is stressed, the analysis unit can prioritize the analysis of simple learning history. If the user is relaxed, the analysis unit can also prioritize the analysis of detailed learning history. Furthermore, if the user is focused, the analysis unit can also prioritize the analysis of important learning history. This allows for more appropriate analysis by prioritizing the learning history according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0068] The analysis unit can prioritize the analysis of highly relevant history by considering the user's geographical location information when analyzing learning history. For example, the analysis unit can prioritize the analysis of relevant learning history based on the user's current location. Furthermore, the analysis unit can also prioritize the analysis of highly relevant learning history by considering the user's past travel history. In addition, the analysis unit can combine the user's geographical location information and learning history to perform optimal analysis. This allows for more appropriate analysis by prioritizing the analysis of highly relevant history by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information data into a generating AI and have the generating AI perform a priority analysis of highly relevant history.

[0069] The analysis unit can analyze a user's social media activity and retrieve relevant history when analyzing learning history. For example, the analysis unit can extract relevant learning history from a user's social media activity. The analysis unit can also analyze a user's areas of interest on social media and prioritize the analysis of relevant learning history. Furthermore, the analysis unit can combine the user's social media activity and learning history to perform optimal analysis. This allows for more appropriate analysis by analyzing the user's social media activity and retrieving relevant history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI and have the generating AI retrieve relevant history.

[0070] The generation unit can estimate the user's emotions and adjust the method of generating the learning plan based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can generate a simple learning plan. If the user is relaxed, the generation unit can also generate a detailed learning plan. Furthermore, if the user is focused, the generation unit can generate a challenging learning plan. By adjusting the method of generating the learning plan according to the user's emotions, a more appropriate learning plan is generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI generate an emotion-based learning plan.

[0071] The generation unit can adjust the level of detail of a learning plan based on the importance of the learning content when generating the learning plan. For example, the generation unit can generate a detailed learning plan for important learning content. It can also generate a simple learning plan for less important learning content. Furthermore, the generation unit can adjust the level of detail of the plan in stages according to the importance of the learning content. By adjusting the level of detail of the plan based on the importance of the learning content, a more appropriate learning plan is generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input learning content importance data into a generation AI and have the generation AI perform the level of detail adjustment.

[0072] The generation unit can apply different generation algorithms depending on the category of learning content when generating a learning plan. For example, the generation unit can apply a specific generation algorithm to science-related learning content. It can also apply a different generation algorithm to humanities-related learning content. Furthermore, the generation unit can select the optimal generation algorithm depending on the category of learning content. By applying different generation algorithms depending on the category of learning content, a more appropriate learning plan can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input learning content category data into a generation AI and have the generation AI execute the application of a generation algorithm.

[0073] The generation unit can estimate the user's emotions and adjust the length of the learning plan based on the estimated emotions. For example, if the user is stressed, the generation unit can generate a shorter learning plan. If the user is relaxed, the generation unit can generate a longer learning plan. Furthermore, if the user is focused, the generation unit can generate a learning plan of appropriate length. By adjusting the length of the learning plan according to the user's emotions, a more appropriate learning plan is generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 AI, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the learning plan based on the emotions.

[0074] The generation unit can determine the priority of learning plans based on the submission deadlines for the learning content when generating learning plans. For example, the generation unit can prioritize incorporating learning content with approaching submission deadlines into the plan. It can also postpone learning content with ample time for submission. Furthermore, the generation unit can adjust the priority of the learning plans in stages based on the submission deadlines. This allows for the generation of more appropriate learning plans by determining the priority of the plans based on the submission deadlines of the learning content. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input learning content submission deadline data into a generation AI and have the generation AI perform the priority determination.

[0075] The generation unit can adjust the order of learning plans based on the relevance of the learning content when generating a learning plan. For example, the generation unit can incorporate highly relevant learning content into the plan consecutively. It can also incorporate less relevant learning content in a distributed manner. Furthermore, the generation unit can adjust the order of the plans step by step based on the relevance of the learning content. By adjusting the order of the plans based on the relevance of the learning content, a more appropriate learning plan is generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the relevance of the learning content into a generation AI and have the generation AI perform the order adjustment.

[0076] The feedback unit can estimate the user's emotions and adjust the way feedback is expressed based on the estimated emotions. For example, if the user is stressed, the feedback unit can provide feedback in gentle language. If the user is relaxed, the feedback unit can provide detailed feedback. Furthermore, if the user is focused, the feedback unit can provide feedback that includes specific areas for improvement. By adjusting the way feedback is expressed according to the user's emotions, more appropriate feedback is provided. Emotion estimation is achieved using an emotion estimation function, for example, using 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 feedback unit may be performed using AI or not using AI. For example, the feedback unit can input user emotion data into the generative AI and have the generative AI adjust the way feedback is expressed based on the emotions.

[0077] The feedback unit can adjust the level of detail of the feedback based on the importance of the learning content when providing feedback. For example, the feedback unit can provide detailed feedback for important learning content. It can also provide simple feedback for less important learning content. Furthermore, the feedback unit can adjust the level of detail of the feedback in stages according to the importance of the learning content. This allows for more appropriate feedback to be provided by adjusting the level of detail of the feedback based on the importance of the learning content. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input learning content importance data into a generating AI and have the generating AI perform the adjustment of the level of detail.

[0078] The feedback unit can apply different feedback algorithms depending on the category of the learning content when providing feedback. For example, the feedback unit can apply a specific feedback algorithm to science-related learning content. It can also apply a different feedback algorithm to humanities-related learning content. Furthermore, the feedback unit can select the optimal feedback algorithm depending on the category of the learning content. By applying different feedback algorithms depending on the category of the learning content, more appropriate feedback is provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input learning content category data into a generating AI and have the generating AI execute the application of the feedback algorithm.

[0079] The feedback unit can estimate the user's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the user is stressed, the feedback unit can provide short feedback. If the user is relaxed, the feedback unit can provide longer feedback. Furthermore, if the user is focused, the feedback unit can provide feedback of an appropriate length. By adjusting the length of the feedback according to the user's emotions, more appropriate feedback is provided. Emotion estimation is achieved using an emotion estimation function, for example, using 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 feedback unit may be performed using AI, or not using AI. For example, the feedback unit can input user emotion data into the generative AI and have the generative AI adjust the length of the feedback based on the emotion.

[0080] The feedback unit can determine the priority of feedback based on the submission date of the learning content when providing feedback. For example, the feedback unit can prioritize providing feedback for learning content with an approaching submission deadline. Conversely, the feedback unit can also postpone providing feedback for learning content with ample time before the submission deadline. Furthermore, the feedback unit can adjust the priority of feedback in stages based on the submission date. This allows for the provision of more appropriate feedback by determining the priority of feedback based on the submission date of the learning content. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input learning content submission date data into a generating AI and have the generating AI perform the priority determination.

[0081] The feedback unit can adjust the order of feedback based on the relevance of the learning content when providing feedback. For example, the feedback unit can provide feedback sequentially for highly relevant learning content. It can also provide feedback in a distributed manner for less relevant learning content. Furthermore, the feedback unit can adjust the order of feedback step by step based on the relevance of the learning content. By adjusting the order of feedback based on the relevance of the learning content, more appropriate feedback can be provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input data on the relevance of the learning content into a generating AI and have the generating AI perform the order adjustment.

[0082] The content generation unit can estimate the user's emotions and determine the priority of the learning content to be generated based on the estimated user emotions. For example, if the user is feeling stressed, the content generation unit can prioritize generating simple learning content. Similarly, if the user is relaxed, the content generation unit can prioritize generating detailed learning content. Furthermore, if the user is focused, the content generation unit can prioritize generating challenging learning content. This allows for the generation of more appropriate learning content by prioritizing learning 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 may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the content generation unit may be performed using AI, or not. For example, the content generation unit can input user emotion data into the generative AI and have the generative AI determine the priority of learning content based on emotions.

[0083] The content generation unit can improve the accuracy of content generation by considering the interrelationships of learning content. For example, the content generation unit can analyze the interrelationships of learning content and consistently generate related content. The content generation unit can also optimize the content generation order by considering the interrelationships of learning content. Furthermore, the content generation unit can generate related content sequentially based on the interrelationships of learning content. By improving the accuracy of generation by considering the interrelationships of learning content, more appropriate learning content is generated. Some or all of the above-described processes in the content generation unit may be performed using AI, for example, or without AI. For example, the content generation unit can input data on the interrelationships of learning content into a generation AI and have the generation AI perform the task of improving the generation accuracy.

[0084] The content generation unit can generate learning content while considering the learner's attribute information. For example, the content generation unit can generate content of appropriate difficulty level according to the learner's age. It can also generate content that reinforces relevant skills according to the learner's occupation. Furthermore, the content generation unit can generate content in the optimal format according to the learner's learning style. By considering the learner's attribute information during generation, more appropriate learning content is generated. Some or all of the above processing in the content generation unit may be performed using AI, for example, or without AI. For example, the content generation unit can input learner attribute information data into a generation AI and have the generation AI perform the generation.

[0085] The content generation unit can estimate the user's emotions and adjust the display method of the generated learning content based on the estimated user emotions. For example, if the user is stressed, the content generation unit can provide a simple display method. If the user is relaxed, the content generation unit can also provide a detailed display method. Furthermore, if the user is focused, the content generation unit can provide a visually stimulating display method. By adjusting the display method of learning content according to the user's emotions, more appropriate learning content is provided. Emotion estimation is achieved using an emotion estimation function, for example, using 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 content generation unit may be performed using AI, for example, or without AI. For example, the content generation unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method based on the emotions.

[0086] The content generation unit can generate learning content while considering the geographical distribution of the learning content. For example, the content generation unit can analyze the geographical distribution of the learning content and generate region-specific content. Furthermore, the content generation unit can generate content tailored to the characteristics of each region, taking into account the geographical distribution of the learning content. In addition, the content generation unit can generate content that meets the needs of each region based on the geographical distribution of the learning content. This allows for the generation of more appropriate learning content by considering the geographical distribution of the learning content. Some or all of the above-described processes in the content generation unit may be performed using AI, for example, or without AI. For example, the content generation unit can input geographical distribution data of the learning content into a generation AI and have the generation AI perform the generation.

[0087] The content generation unit can improve the accuracy of content generation by referring to relevant literature on the learning content during the generation of learning content. For example, the content generation unit can refer to literature related to the learning content and generate accurate content. Furthermore, the content generation unit can generate detailed content based on relevant literature on the learning content. In addition, the content generation unit can refer to relevant literature on the learning content and generate content that includes the latest information. This improves the accuracy of content generation by referring to relevant literature on the learning content, resulting in the generation of more appropriate learning content. Some or all of the above-described processes in the content generation unit may be performed using AI, for example, or without AI. For example, the content generation unit can input data on relevant literature on the learning content into a generation AI and have the generation AI perform the task of improving the accuracy of the generation.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The analytics department can consider the user's learning style when analyzing their learning history and progress. For example, it can provide analysis results using graphs and charts for visual learners and audio feedback for auditory learners. Furthermore, the analytics department can suggest the optimal learning method based on the user's learning style. It can also adjust the content and format of the learning plan according to the user's learning style. This enables analysis tailored to the user's learning style, improving the effectiveness of learning.

[0090] The generation unit can estimate the user's emotions and adjust the difficulty level of the learning plan based on those emotions. For example, if the user is feeling stressed, it can generate a learning plan centered on easy tasks. If the user is relaxed, it can generate a learning plan that includes slightly more difficult tasks. Furthermore, if the user is focused, it can generate a learning plan that includes many challenging tasks. By adjusting the difficulty level of the learning plan according to the user's emotions, a more appropriate learning plan can be generated.

[0091] The feedback unit can analyze a user's learning history and evaluate the effectiveness of past feedback. For example, it can analyze how previously provided feedback affected learning outcomes and identify effective feedback patterns. Furthermore, the feedback unit can adjust the content and method of future feedback based on the effectiveness of past feedback. In addition, the feedback unit can provide feedback tailored to individual feedback needs based on the user's learning history. This allows for improved learning effectiveness by evaluating the effectiveness of past feedback and optimizing future feedback.

[0092] The content generation unit can estimate the user's emotions and adjust the format of the learning content based on those emotions. For example, if the user is stressed, it can provide learning content in a simple and intuitive format. If the user is relaxed, it can provide learning content that includes detailed explanations and supplementary materials. Furthermore, if the user is focused, it can provide learning content that includes many interactive elements. By adjusting the format of the learning content according to the user's emotions, a more appropriate learning experience can be provided.

[0093] The analytics department can evaluate the reliability of user learning histories when analyzing them. For example, it can evaluate the reliability of user-reported learning histories and prioritize the analysis of reliable data. The analytics department can also adjust the accuracy of the analysis results based on the reliability of the learning histories. Furthermore, the analytics department can utilize other data sources to supplement less reliable data. This improves the accuracy of the analysis by evaluating the reliability of learning histories and conducting analysis based on reliable data.

[0094] The generation unit can estimate the user's emotions and adjust the pace of the learning plan based on those emotions. For example, if the user is stressed, it can generate a learning plan with a slow pace. If the user is relaxed, it can generate a learning plan with a normal pace. Furthermore, if the user is focused, it can generate a learning plan with a fast pace. By adjusting the pace of the learning plan according to the user's emotions, a more appropriate learning plan can be generated.

[0095] The feedback unit can analyze the user's learning history and assess their learning motivation. For example, it can identify changes in the user's motivation from past learning history and provide encouraging feedback if their motivation is low. Conversely, if their motivation is high, it can provide feedback that encourages further challenges. Furthermore, the feedback unit can adjust the content and method of feedback according to the user's motivation. This allows for improved learning effectiveness by assessing the user's motivation and providing appropriate feedback.

[0096] The content generation unit can estimate the user's emotions and adjust the difficulty level of the learning content based on those emotions. For example, if the user is stressed, it can provide easy learning content. If the user is relaxed, it can provide learning content of normal difficulty. Furthermore, if the user is focused, it can provide learning content of higher difficulty. By adjusting the difficulty level of the learning content according to the user's emotions, a more appropriate learning experience can be provided.

[0097] The analytics department can evaluate the consistency of a user's learning history when analyzing it. For example, it can assess whether a user's learning history is consistent and prioritize the analysis of consistent data. The analytics department can also utilize other data sources to supplement inconsistent data. Furthermore, the analytics department can adjust the reliability of the analysis results based on the consistency of the learning history. This improves the accuracy of the analysis by evaluating the consistency of the learning history and conducting analysis based on consistent data.

[0098] The generation unit can estimate the user's emotions and adjust the content of the learning plan based on those emotions. For example, if the user is feeling stressed, it can generate a learning plan that includes relaxing content. If the user is relaxed, it can generate a learning plan that includes normal content. Furthermore, if the user is focused, it can generate a learning plan that includes challenging content. By adjusting the content of the learning plan according to the user's emotions, a more appropriate learning plan can be generated.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The analytics department analyzes the user's learning history and progress. For example, the analytics department can collect data such as past learning content, learning time, and learning outcomes to identify the user's learning patterns. The analytics department can also monitor the user's learning progress and achievements in real time to understand their learning status. Furthermore, based on the user's learning history, the analytics department can identify learning trends and challenges, which can be used to optimize the learning plan. Step 2: The generation unit generates a customized learning plan based on the data analyzed by the analysis unit. For example, the generation unit can create an optimal learning plan according to the user's learning goals and learning style. The generation unit can also dynamically adjust the learning plan according to the user's learning pace and progress. Furthermore, the generation unit can provide a plan that meets individual learning needs by taking into account the user's learning history. Step 3: The feedback unit provides real-time feedback based on the learning plan generated by the generation unit. For example, the feedback unit can provide immediate evaluations and advice according to the user's learning progress. The feedback unit can also suggest corrections and improvements to the user's learning content. Furthermore, the feedback unit can evaluate the user's learning outcomes and suggest the next learning steps. Step 4: The content generation unit dynamically generates learning content from learner behavior and performance data. For example, the content generation unit can automatically create optimal learning content based on the user's learning history and progress. It can also update content in real time according to the user's learning needs. Furthermore, the content generation unit can use generation AI to generate content that provides learners with the optimal learning experience.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] Each of the multiple elements described above, including the analysis unit, generation unit, feedback unit, and content generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's learning history and progress. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a customized learning plan. The feedback unit is implemented by the control unit 46A of the smart device 14 and provides real-time feedback. The content generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and dynamically generates learning content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the analysis unit, generation unit, feedback unit, and content generation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's learning history and progress. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a customized learning plan. The feedback unit is implemented by the control unit 46A of the smart glasses 214 and provides real-time feedback. The content generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and dynamically generates learning content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the analysis unit, generation unit, feedback unit, and content generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's learning history and progress. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a customized learning plan. The feedback unit is implemented by the control unit 46A of the headset terminal 314 and provides real-time feedback. The content generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and dynamically generates learning content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements described above, including the analysis unit, generation unit, feedback unit, and content generation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's learning history and progress. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a customized learning plan. The feedback unit is implemented by the control unit 46A of the robot 414 and provides real-time feedback. The content generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and dynamically generates learning content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] (Note 1) The analytics department analyzes the user's learning history and progress, A generation unit generates a customized learning plan based on the data analyzed by the analysis unit, A feedback unit provides real-time feedback based on the learning plan generated by the generation unit, It comprises a content generation unit that dynamically generates learning content from learner behavior and outcome data. A system characterized by the following features. (Note 2) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis method of the learning history based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The system analyzes the user's past learning history and selects the optimal analysis algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is When analyzing learning history, filtering is performed 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 5) The aforementioned analysis unit is It estimates the user's emotions and prioritizes the learning history for analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is When analyzing learning history, the system prioritizes analyzing highly relevant history by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is When analyzing learning history, the system analyzes the user's social media activity and retrieves relevant history. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is It estimates the user's emotions and adjusts how the learning plan is generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is When generating a learning plan, adjust the level of detail in the plan based on the importance of the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is When generating a learning plan, different generation algorithms are applied depending on the category of the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is It estimates the user's emotions and adjusts the length of the learning plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating a learning plan, prioritize the plan based on the submission deadlines for the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating a learning plan, the order of the plan is adjusted based on the relevance of the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned feedback unit is When providing feedback, adjust the level of detail based on the importance of the learned content. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned feedback unit is When providing feedback, different feedback algorithms are applied depending on the category of the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned feedback unit is It estimates the user's emotions and adjusts the length of the feedback based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned feedback unit is When providing feedback, we prioritize feedback based on when the learning content was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned feedback unit is When providing feedback, adjust the order of feedback based on the relevance of the learned content. The system described in Appendix 1, characterized by the features described herein. (Note 20) The content generation unit, It estimates the user's emotions and determines the priority of the learning content to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The content generation unit, When generating learning content, we improve the accuracy of the generation by considering the interrelationships between the learning elements. The system described in Appendix 1, characterized by the features described herein. (Note 22) The content generation unit, When generating learning content, the system takes learner attribute information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The content generation unit, It estimates the user's emotions and adjusts how the learning content is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The content generation unit, When generating learning content, the geographical distribution of the learning material is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The content generation unit, When generating learning content, we improve the accuracy of the generation by referring to relevant literature on the learning material. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 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. The analytics department analyzes the user's learning history and progress, A generation unit generates a customized learning plan based on the data analyzed by the analysis unit, A feedback unit provides real-time feedback based on the learning plan generated by the generation unit, It comprises a content generation unit that dynamically generates learning content from learner behavior and outcome data. A system characterized by the following features.

2. The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis method of the learning history based on the estimated user emotions. The system according to feature 1.

3. The aforementioned analysis unit is The system analyzes the user's past learning history and selects the optimal analysis algorithm. The system according to feature 1.

4. The aforementioned analysis unit is When analyzing learning history, filtering is performed based on the user's current learning status and areas of interest. The system according to feature 1.

5. The aforementioned analysis unit is It estimates the user's emotions and prioritizes the learning history for analysis based on the estimated user emotions. The system according to feature 1.

6. The aforementioned analysis unit is When analyzing learning history, the system prioritizes analyzing highly relevant history by considering the user's geographical location. The system according to feature 1.

7. The aforementioned analysis unit is When analyzing learning history, the system analyzes the user's social media activity and retrieves relevant history. The system according to feature 1.

8. The generating unit is It estimates the user's emotions and adjusts how the learning plan is generated based on those estimated emotions. The system according to feature 1.

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