system

Abstract

The system according to this embodiment aims to provide personalized educational content based on the user's interests and preferences when using a ride-hailing platform. [Solution] The system according to the embodiment comprises a reception unit, a learning unit, a generation unit, an integration unit, and a provision unit. The reception unit selects whether the user wishes to receive educational content when using the ride-hailing platform to board a vehicle. The learning unit learns the user's interests and preferences based on the information received by the reception unit. The generation unit generates educational content based on the information learned by the learning unit. The integration unit integrates the educational content generated by the generation unit into the AR headset according to the user's personality. The provision unit provides the integrated educational content in real time.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

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[0001] ​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​The system according to this embodiment comprises a reception unit, a learning unit, a generation unit, an integration unit, and a provision unit. The reception unit selects whether the user wishes to receive educational content when using the ride-hailing platform to board a vehicle. The learning unit learns the user's interests and preferences based on the information received by the reception unit. The generation unit generates educational content based on the information learned by the learning unit. The integration unit integrates the educational content generated by the generation unit into an AR headset according to the user's personality. The provision unit provides the integrated educational content in real time. [Effects of the Invention]

[0007] The system according to this embodiment can provide personalized educational content based on the user's interests and preferences when using the ride-hailing platform. [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 and an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) An embodiment of the present invention provides an educational content delivery system that works in conjunction with a ride-hailing platform to deliver personalized educational content to passengers during their ride. When a user boards a ride using the ride-hailing platform, they select whether or not they wish to receive educational content. Next, based on the user's selected topic, past learning history, interests, and preferences, the AI ​​generates appropriate educational content. For example, if a user is interested in "history," the AI ​​generates lectures and Q&A content related to history. The generated educational content is integrated into an appropriate AR headset tailored to the user's personality. This allows the user to wear the AR headset during their ride and enjoy an immersive learning experience. For example, during a history lecture, the user may experience historical places and figures appearing before their eyes through the AR headset. This system effectively utilizes the time spent in the ride and provides a valuable learning experience for the user. Furthermore, by learning the user's interests and preferences, the AI ​​can provide even more personalized content. Additionally, using an AR headset allows for a new learning experience different from conventional learning methods. Thus, the educational content delivery system effectively utilizes the time spent in the ride and provides a valuable learning experience for the user.

[0029] The educational content provision system according to this embodiment comprises a reception unit, a learning unit, a generation unit, an integration unit, and a provision unit. The reception unit allows users to select whether they wish to receive educational content when they board a ride using the ride-hailing platform. The reception unit displays, for example, a screen in which the user selects whether they wish to receive educational content through an application. The reception unit can also accept topics selected by the user and their past learning history. For example, the reception unit provides a form for the user to input topics they have previously studied and areas of interest. The learning unit uses AI to learn the user's interests and preferences based on the information received by the reception unit. The learning unit uses, for example, machine learning algorithms to analyze the user's past behavioral history and selection history. The learning unit can also analyze the user's social media data to identify their interests and preferences. The generation unit uses AI to generate educational content based on the information learned by the learning unit. The generation unit uses, for example, text generation AI (e.g., LLM) to generate lecture and Q&A content based on the user's interests and preferences. Furthermore, the generation unit can generate content such as video lectures and interactive quizzes using a multimodal generation AI. The integration unit integrates the educational content generated by the generation unit into the AR headset according to the user's personality. The integration unit selects appropriate visual effects and interactions based, for example, on the user's psychological test results and behavioral pattern analysis. The delivery unit provides the integrated educational content in real time. The delivery unit enables the user to view the educational content while riding through the AR headset. The delivery unit can also monitor the progress of the educational content in real time and update the content as needed. As a result, the educational content delivery system according to this embodiment allows the user to receive personalized educational content while riding.

[0030] The reception desk allows users to choose whether they wish to receive educational content when they board a ride using the ride-hailing platform. Specifically, a screen is displayed through the application allowing users to select whether they wish to receive educational content. This screen provides options for users to select topics of interest or areas they wish to learn about. For example, users can choose from categories such as "History," "Science," or "Business." The reception desk can also record the topics selected by the user and their past learning history. This allows the system to record what the user has learned in the past and areas of interest, which can be used as a reference for future use. For example, if a user has previously expressed interest in "Medieval European History," new relevant content can be suggested for their next ride. Furthermore, the reception desk can also propose a personalized learning plan based on the user's learning history and selection history. This allows users to receive educational content optimized for their interests and learning needs.

[0031] The learning department uses AI to learn user interests and preferences based on information received by the reception department. Specifically, it uses machine learning algorithms to analyze the user's past behavior and selection history. For example, it analyzes the history of topics the user has previously selected and content they have viewed to identify trends in the user's interests. The learning department can also analyze the user's social media data to identify interests and preferences. This allows it to understand what kind of information the user is usually interested in, enabling more accurate content suggestions. Furthermore, the learning department can collect user feedback and continuously improve its learning algorithms. For example, by having users evaluate the content they have viewed, the algorithm is adjusted based on the evaluation results to optimize future suggestions. In this way, the learning department can accurately understand user interests and preferences and build a foundation for providing personalized educational content.

[0032] The generation unit uses AI to generate educational content based on information learned by the learning unit. Specifically, it uses text generation AI (e.g., LLM) to generate lectures and Q&A content based on the user's interests and preferences. For example, if a user is interested in "medieval European history," the generation unit can generate detailed lecture content on that topic. The generation unit can also use multimodal generation AI to generate content such as video lectures and interactive quizzes. This allows users to learn not only through text, but also visually and aurally. Furthermore, the generation unit can adjust the difficulty level and content of the content based on the user's learning progress and feedback. For example, if a user has a deeper understanding of a particular topic, it can provide more advanced content to enhance the depth of learning. In this way, the generation unit can generate educational content optimized for the user's interests and learning needs, providing an effective learning experience.

[0033] The integration unit integrates the educational content generated by the generation unit into the AR headset, tailoring it to the user's personality. Specifically, it selects appropriate visual effects and interactions based on the user's psychological test results and behavioral pattern analysis. For example, if a user has a visual learning style, the integration unit provides content that emphasizes visual effects. If a user prefers interactive learning, the integration unit can provide content that includes interactive quizzes and simulations. Furthermore, the integration unit can monitor the user's real-time reactions and dynamically adjust how the content is displayed. For example, if a user shows no interest in a particular piece of content, the integration unit can try a different approach to keep the user engaged. This allows the integration unit to provide educational content optimized for the user's personality and learning style, resulting in an effective learning experience.

[0034] The delivery unit provides integrated educational content in real time, as integrated by the integration unit. Specifically, it enables users to view educational content while riding through an AR headset. The delivery unit can also monitor the progress of the educational content in real time and update it as needed. For example, if a user's understanding of a particular topic deepens, the delivery unit will instruct them to move on to the next topic. If a user has questions about the content, the delivery unit can provide answers to those questions in real time. Furthermore, the delivery unit can collect user feedback and continuously improve the quality and delivery method of the content. For example, by having users rate the content they have viewed, the content and delivery method can be adjusted based on the evaluation results. This allows the delivery unit to provide personalized educational content to users in real time, realizing an effective learning experience.

[0035] The reception unit can receive topics selected by the user and their past learning history. For example, the reception unit can provide a form for the user to input topics selected through the application. The reception unit can also retrieve the user's past learning history from a database and use it to provide educational content. For example, the reception unit can record topics, learning time, and learning outcomes previously studied by the user and provide appropriate content based on this information. This allows for the provision of educational content based on the user's selected topics and past learning history. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's selected topics and past learning history into an AI and have the AI ​​select appropriate content.

[0036] The learning unit can use algorithms to learn the user's interests and preferences. For example, the learning unit can use machine learning algorithms to analyze the user's past behavioral history and selection history. The learning unit can also use data mining techniques to analyze the user's social media data and identify their interests and preferences. For example, the learning unit can analyze the content of the user's social media posts and the number of likes to identify topics of interest. This allows the learning unit to learn the user's interests and preferences and provide more personalized educational content. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input the user's behavioral history data into an AI and have the AI ​​perform the identification of interests and preferences.

[0037] The generation unit can generate educational content in the form of lectures and Q&A sessions based on the user's interests and preferences. For example, the generation unit can use a text generation AI (e.g., LLM) to generate lectures and Q&A content based on the user's interests and preferences. The generation unit can also use a multimodal generation AI to generate content such as video lectures and interactive quizzes. For example, if the user is interested in "history," the generation unit will generate lectures and Q&A content related to history. This allows for the generation of educational content based on the user's interests and preferences. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user interest and preference data into an AI and have the AI ​​generate the educational content.

[0038] The integration unit can integrate the generated educational content into the AR headset, tailored to the user's personality. For example, the integration unit selects appropriate visual effects and interactions based on analysis of the user's psychological test results and behavioral patterns. The integration unit can also adjust the display method of the educational content and the format of interactions according to the user's personality. For example, if the user has an introverted personality, the integration unit uses quiet and calming visual effects. This allows for the integration of educational content tailored to the user's personality into the AR headset. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input user personality data into an AI and have the AI ​​select appropriate visual effects.

[0039] The service provider can deliver integrated educational content in real time. For example, the service provider can enable users to view educational content while riding through an AR headset. The service provider can also monitor the progress of the educational content in real time and update the content as needed. For example, if a user asks a question while learning, the service provider can provide an answer to that question in real time. This enables the delivery of educational content in real time. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input educational content progress data into the AI ​​and have the AI ​​perform real-time content updates.

[0040] The reception desk can analyze the user's past selection history and suggest the optimal topic selection method. For example, the reception desk can automatically suggest related topics based on the topics the user has selected in the past. The reception desk can also analyze the frequency of topics the user has selected in the past and prioritize displaying the most frequently selected topics. For example, the reception desk can predict and suggest topics selected during a specific time period based on the user's past selection history. This allows the reception desk to suggest the optimal topic selection method based on the user's past selection history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's selection history data into AI and have the AI ​​perform the task of suggesting the optimal topic selection method.

[0041] The reception desk can filter requests for educational content based on the user's current learning status and areas of interest. For example, the reception desk can suggest content at an appropriate level based on the user's current learning progress. The reception desk can also prioritize displaying relevant topics based on the user's areas of interest. For example, the reception desk can analyze the user's learning history and suggest unlearned topics. This allows for the provision of appropriate educational content based on the user's current learning status and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's learning status data into an AI and have the AI ​​perform the filtering.

[0042] When the reception desk confirms the user's request for educational content, it can prioritize presenting highly relevant topics by considering the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize displaying topics related to that region. Similarly, if the user is traveling, the reception desk can prioritize displaying topics related to their travel destination. For example, if the user is at home, the reception desk can prioritize displaying topics that can be learned at home. This allows the reception desk to provide highly relevant topics based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location data into an AI and have the AI ​​perform the task of presenting highly relevant topics.

[0043] The reception desk can analyze a user's social media activity and suggest relevant topics when confirming their desire for educational content. For example, the reception desk may prioritize displaying topics that the user frequently mentions on social media. It can also suggest topics that the user's social media followers are interested in. For example, the reception desk may analyze the user's social media posts and suggest relevant topics. This allows the reception desk to provide relevant topics based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input the user's social media data into an AI and have the AI ​​suggest relevant topics.

[0044] The learning unit can optimize the learning algorithm by referring to the user's past learning history during the learning process. For example, the learning unit can propose the optimal learning pattern based on the user's past learning history. The learning unit can also extract effective learning methods from the user's past learning history and reflect them in the algorithm. For example, the learning unit can analyze the user's past learning history and adjust the algorithm according to the learning progress. This allows the learning algorithm to be optimized based on the user's past learning history. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit can input the user's learning history data into AI and have the AI ​​perform the optimization of the learning algorithm.

[0045] The learning unit can incorporate a feedback mechanism to reflect changes in the user's interests and preferences in real time during the learning process. For example, if the user shows interest in a new topic, the learning unit will update the learning content in real time. The learning unit can also instantly adjust the learning algorithm if the user's preferences change. For example, the learning unit can optimize the learning content in real time based on user feedback. This allows changes in the user's interests and preferences to be reflected in real time. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit can input user feedback data into the AI ​​and have the AI ​​update the learning content.

[0046] The learning unit can optimize its learning algorithm during training by taking into account the user's geographical location. For example, if the user is in a specific region, the learning unit can prioritize providing data related to that region. Similarly, if the user is traveling, the learning unit can prioritize providing data related to their travel destination. For example, if the user is at home, the learning unit can prioritize providing data that can be studied at home. This allows the learning algorithm to be optimized based on the user's geographical location. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input the user's geographical location data into an AI and have the AI ​​optimize the learning algorithm.

[0047] The learning unit can analyze the user's social media activity during training and incorporate relevant data into its learning process. For example, the learning unit can reflect topics that the user frequently mentions on social media in its learning data. It can also incorporate topics that the user's social media followers are interested in into its learning data. For example, the learning unit can analyze the content of the user's social media posts and incorporate relevant data into its learning process. This allows the learning unit to incorporate relevant data based on the user's social media activity. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit can input the user's social media data into an AI and have the AI ​​perform learning on the relevant data.

[0048] The generation unit can generate optimal educational content by referring to the user's past learning history. For example, the generation unit can generate educational content that includes relevant topics based on the user's past learning history. The generation unit can also generate educational content that reflects effective learning methods based on the user's past learning history. For example, the generation unit can analyze the user's past learning history and generate educational content that corresponds to their learning progress. This allows for the generation of optimal educational content based on the user's past learning history. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can input the user's learning history data into AI and have the AI ​​generate optimal educational content.

[0049] The generation unit can adjust the level of detail of educational content based on the user's current learning status when generating educational content. For example, the generation unit can generate educational content with detailed explanations based on the user's current learning progress. Alternatively, the generation unit can generate educational content with concise explanations depending on the user's current learning status. For example, the generation unit can analyze the user's current learning status and generate educational content of appropriate difficulty. This allows for the generation of educational content with appropriate detail levels according to the user's current learning status. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user learning status data into AI and have the AI ​​perform the adjustment of content detail.

[0050] The generation unit can generate optimal educational content by considering the user's geographical location information. For example, if the user is in a specific region, the generation unit can generate educational content related to that region. Furthermore, if the user is traveling, the generation unit can generate educational content related to their travel destination. For example, if the user is at home, the generation unit can generate educational content that can be studied at home. This allows for the generation of optimal educational content based on the user's geographical location information. Some or all of the above-described processes in the generation unit may be performed using AI, or they may not. For example, the generation unit can input the user's geographical location data into an AI and have the AI ​​generate optimal educational content.

[0051] The generation unit can analyze a user's social media activity and generate relevant content when generating educational content. For example, the generation unit can reflect topics that the user frequently mentions on social media in the educational content. The generation unit can also incorporate topics that the user's social media followers are interested in into the educational content. For example, the generation unit can analyze the content of a user's social media posts and generate relevant educational content. This allows for the generation of relevant educational content based on the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's social media data into an AI and have the AI ​​generate relevant educational content.

[0052] The integration unit can select the optimal integration method by referring to the user's past learning history during integration. For example, the integration unit can select the optimal visual effect based on the user's past learning history. The integration unit can also extract and reflect effective integration methods from the user's past learning history. For example, the integration unit can analyze the user's past learning history and select an integration method according to the learning progress. This allows the integration unit to select the optimal integration method based on the user's past learning history. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input the user's learning history data into AI and have the AI ​​select the optimal integration method.

[0053] The integration unit can adjust the level of detail of the integration based on the user's current learning status during the integration process. For example, the integration unit may use detailed visual effects to integrate based on the user's current learning progress. Alternatively, the integration unit may use simplified visual effects depending on the user's current learning status. For example, the integration unit may analyze the user's current learning status and integrate using visual effects of appropriate difficulty. This allows for integration with an appropriate level of detail according to the user's current learning status. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit may input user learning status data into AI and have the AI ​​perform the adjustment of the level of detail of the integration.

[0054] The integration unit can select the optimal integration method during integration, taking into account the user's geographical location. For example, if the user is in a specific region, the integration unit will use visual effects related to that region for integration. Alternatively, if the user is traveling, the integration unit can use visual effects related to the travel destination for integration. For example, if the user is at home, the integration unit will use visual effects suitable for home learning for integration. This allows the integration unit to select the optimal integration method based on the user's geographical location. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input the user's geographical location data into an AI and have the AI ​​select the optimal integration method.

[0055] The integration unit can analyze the user's social media activity and integrate relevant content during the integration process. For example, the integration unit can reflect topics that the user frequently mentions on social media in the visual effects. It can also incorporate topics that the user's social media followers are interested in into the visual effects. For example, the integration unit can analyze the content of the user's social media posts and integrate relevant visual effects. This allows for the integration of relevant content based on the user's social media activity. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input the user's social media data into an AI and have the AI ​​perform the integration of relevant content.

[0056] The delivery unit can select the optimal delivery method by referring to the user's past learning history at the time of delivery. For example, the delivery unit can select the optimal delivery method based on the user's past learning history. The delivery unit can also extract and reflect effective delivery methods from the user's past learning history. For example, the delivery unit can analyze the user's past learning history and select a delivery method according to the learning progress. This allows the delivery unit to select the optimal delivery method based on the user's past learning history. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's learning history data into AI and have the AI ​​perform the selection of the optimal delivery method.

[0057] The delivery unit can adjust the level of detail provided based on the user's current learning status at the time of delivery. For example, the delivery unit can provide educational content with detailed explanations based on the user's current learning progress. Alternatively, the delivery unit can provide educational content with concise explanations depending on the user's current learning status. For example, the delivery unit can analyze the user's current learning status and provide educational content of appropriate difficulty. This allows the delivery unit to provide educational content with an appropriate level of detail according to the user's current learning status. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's learning status data into AI and have the AI ​​perform the adjustment of the level of detail provided.

[0058] The service provider can select the optimal delivery method at the time of delivery, taking into account the user's geographical location information. For example, if the user is in a specific region, the service provider can provide educational content related to that region. Alternatively, if the user is traveling, the service provider can provide educational content related to their travel destination. For example, if the user is at home, the service provider can provide educational content that can be studied at home. This allows the service provider to select the optimal delivery method based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's geographical location data into AI and have the AI ​​select the optimal delivery method.

[0059] The service provider can analyze the user's social media activity and provide relevant content at the time of delivery. For example, the service provider can reflect topics that the user frequently mentions on social media in the educational content. The service provider can also incorporate topics that the user's social media followers are interested in into the educational content. For example, the service provider can analyze the content of the user's social media posts and provide relevant educational content. This allows the service provider to provide relevant educational content based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media data into AI and have the AI ​​perform the task of providing relevant content.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The educational content delivery system can also include a feedback unit. The feedback unit collects user feedback on the educational content provided during learning and transmits it to the learning unit. For example, users can rate the content as "easy to understand" or "difficult." The feedback unit can also collect questions from users regarding specific content and transmit them to the generation unit to generate answers. Furthermore, the feedback unit can suggest improvements based on user feedback to be incorporated into future content delivery. This allows for real-time incorporation of user feedback and the delivery of more effective educational content.

[0062] The educational content delivery system can also include a rewards section. This section provides rewards when users achieve their learning goals. For example, it could award digital badges or points to users who complete a specific topic. The rewards section could also offer incentives to users who achieve a certain amount of study time. Furthermore, if users are competing with other users in their learning, the rewards section could display rankings or leaderboards and reward top-ranking users. This can increase user motivation and encourage continuous learning.

[0063] The educational content delivery system can also include a communication section. This section provides a platform where users can discuss learning content with each other. For example, users can ask other users questions about points they are unsure of during their studies. The communication section also allows users to share what they have learned and receive feedback from other users. Furthermore, the communication section can provide features that allow users to directly interact with experts and instructors, supporting them in gaining a deeper understanding. This promotes interaction among users and enhances learning effectiveness.

[0064] The educational content delivery system can also include a customization section. This customization section allows users to customize educational content to suit their own learning style and pace. For example, if a user desires a more detailed explanation of a particular topic, additional materials on that topic can be provided. The customization section can also provide a function to change the speed at which the content progresses, allowing users to adjust their learning pace. Furthermore, the customization section can suggest the optimal learning path based on the user's learning history and feedback. This allows users to create a learning environment that is best suited to them and to learn efficiently.

[0065] The educational content delivery system can also include a reminder function. This function provides reminders to help users continue their learning. For example, if a user has not studied for a certain period, the reminder function can send a notification encouraging them to resume learning. The reminder function can also periodically notify users of their progress based on their set learning goals. Furthermore, the reminder function can set reminders to help users develop the habit of studying at specific times of the day. This makes it easier for users to continue learning and improves learning effectiveness.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk allows users to choose whether they wish to receive educational content when they board a ride using the ride-hailing platform. For example, it can display a screen where users can choose whether they wish to receive educational content through the application. It can also accept the user's selected topics and past learning history. Step 2: The learning unit uses AI to learn the user's interests and preferences based on the information received by the reception unit. For example, it uses machine learning algorithms to analyze the user's past behavior and selection history. It can also analyze the user's social media data to identify their interests and preferences. Step 3: The generation unit uses AI to generate educational content based on the information learned by the learning unit. For example, it can use text generation AI (e.g., LLM) to generate lectures and Q&A content based on the user's interests and preferences. It can also use multimodal generation AI to generate content such as video lectures and interactive quizzes. Step 4: The integration unit integrates the educational content generated by the generation unit into the AR headset, tailored to the user's personality. For example, it selects appropriate visual effects and interactions based on the user's psychological test results and behavioral pattern analysis. Step 5: The delivery unit provides the integrated educational content in real time. For example, it allows users to view educational content while riding through an AR headset. It can also monitor the progress of the educational content in real time and update the content as needed.

[0068] (Example of form 2) An embodiment of the present invention provides an educational content delivery system that works in conjunction with a ride-hailing platform to deliver personalized educational content to passengers during their ride. When a user boards a ride using the ride-hailing platform, they select whether or not they wish to receive educational content. Next, based on the user's selected topic, past learning history, interests, and preferences, the AI ​​generates appropriate educational content. For example, if a user is interested in "history," the AI ​​generates lectures and Q&A content related to history. The generated educational content is integrated into an appropriate AR headset tailored to the user's personality. This allows the user to wear the AR headset during their ride and enjoy an immersive learning experience. For example, during a history lecture, the user may experience historical places and figures appearing before their eyes through the AR headset. This system effectively utilizes the time spent in the ride and provides a valuable learning experience for the user. Furthermore, by learning the user's interests and preferences, the AI ​​can provide even more personalized content. Additionally, using an AR headset allows for a new learning experience different from conventional learning methods. Thus, the educational content delivery system effectively utilizes the time spent in the ride and provides a valuable learning experience for the user.

[0069] The educational content provision system according to this embodiment comprises a reception unit, a learning unit, a generation unit, an integration unit, and a provision unit. The reception unit allows users to select whether they wish to receive educational content when they board a ride using the ride-hailing platform. The reception unit displays, for example, a screen in which the user selects whether they wish to receive educational content through an application. The reception unit can also accept topics selected by the user and their past learning history. For example, the reception unit provides a form for the user to input topics they have previously studied and areas of interest. The learning unit uses AI to learn the user's interests and preferences based on the information received by the reception unit. The learning unit uses, for example, machine learning algorithms to analyze the user's past behavioral history and selection history. The learning unit can also analyze the user's social media data to identify their interests and preferences. The generation unit uses AI to generate educational content based on the information learned by the learning unit. The generation unit uses, for example, text generation AI (e.g., LLM) to generate lecture and Q&A content based on the user's interests and preferences. Furthermore, the generation unit can generate content such as video lectures and interactive quizzes using a multimodal generation AI. The integration unit integrates the educational content generated by the generation unit into the AR headset according to the user's personality. The integration unit selects appropriate visual effects and interactions based, for example, on the user's psychological test results and behavioral pattern analysis. The delivery unit provides the integrated educational content in real time. The delivery unit enables the user to view the educational content while riding through the AR headset. The delivery unit can also monitor the progress of the educational content in real time and update the content as needed. As a result, the educational content delivery system according to this embodiment allows the user to receive personalized educational content while riding.

[0070] The reception desk allows users to choose whether they wish to receive educational content when they board a ride using the ride-hailing platform. Specifically, a screen is displayed through the application allowing users to select whether they wish to receive educational content. This screen provides options for users to select topics of interest or areas they wish to learn about. For example, users can choose from categories such as "History," "Science," or "Business." The reception desk can also record the topics selected by the user and their past learning history. This allows the system to record what the user has learned in the past and areas of interest, which can be used as a reference for future use. For example, if a user has previously expressed interest in "Medieval European History," new relevant content can be suggested for their next ride. Furthermore, the reception desk can also propose a personalized learning plan based on the user's learning history and selection history. This allows users to receive educational content optimized for their interests and learning needs.

[0071] The learning department uses AI to learn user interests and preferences based on information received by the reception department. Specifically, it uses machine learning algorithms to analyze the user's past behavior and selection history. For example, it analyzes the history of topics the user has previously selected and content they have viewed to identify trends in the user's interests. The learning department can also analyze the user's social media data to identify interests and preferences. This allows it to understand what kind of information the user is usually interested in, enabling more accurate content suggestions. Furthermore, the learning department can collect user feedback and continuously improve its learning algorithms. For example, by having users evaluate the content they have viewed, the algorithm is adjusted based on the evaluation results to optimize future suggestions. In this way, the learning department can accurately understand user interests and preferences and build a foundation for providing personalized educational content.

[0072] The generation unit uses AI to generate educational content based on information learned by the learning unit. Specifically, it uses text generation AI (e.g., LLM) to generate lectures and Q&A content based on the user's interests and preferences. For example, if a user is interested in "medieval European history," the generation unit can generate detailed lecture content on that topic. The generation unit can also use multimodal generation AI to generate content such as video lectures and interactive quizzes. This allows users to learn not only through text, but also visually and aurally. Furthermore, the generation unit can adjust the difficulty level and content of the content based on the user's learning progress and feedback. For example, if a user has a deeper understanding of a particular topic, it can provide more advanced content to enhance the depth of learning. In this way, the generation unit can generate educational content optimized for the user's interests and learning needs, providing an effective learning experience.

[0073] The integration unit integrates the educational content generated by the generation unit into the AR headset, tailoring it to the user's personality. Specifically, it selects appropriate visual effects and interactions based on the user's psychological test results and behavioral pattern analysis. For example, if a user has a visual learning style, the integration unit provides content that emphasizes visual effects. If a user prefers interactive learning, the integration unit can provide content that includes interactive quizzes and simulations. Furthermore, the integration unit can monitor the user's real-time reactions and dynamically adjust how the content is displayed. For example, if a user shows no interest in a particular piece of content, the integration unit can try a different approach to keep the user engaged. This allows the integration unit to provide educational content optimized for the user's personality and learning style, resulting in an effective learning experience.

[0074] The delivery unit provides integrated educational content in real time, as integrated by the integration unit. Specifically, it enables users to view educational content while riding through an AR headset. The delivery unit can also monitor the progress of the educational content in real time and update it as needed. For example, if a user's understanding of a particular topic deepens, the delivery unit will instruct them to move on to the next topic. If a user has questions about the content, the delivery unit can provide answers to those questions in real time. Furthermore, the delivery unit can collect user feedback and continuously improve the quality and delivery method of the content. For example, by having users rate the content they have viewed, the content and delivery method can be adjusted based on the evaluation results. This allows the delivery unit to provide personalized educational content to users in real time, realizing an effective learning experience.

[0075] The reception unit can receive topics selected by the user and their past learning history. For example, the reception unit can provide a form for the user to input topics selected through the application. The reception unit can also retrieve the user's past learning history from a database and use it to provide educational content. For example, the reception unit can record topics, learning time, and learning outcomes previously studied by the user and provide appropriate content based on this information. This allows for the provision of educational content based on the user's selected topics and past learning history. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's selected topics and past learning history into an AI and have the AI ​​select appropriate content.

[0076] The learning unit can use algorithms to learn the user's interests and preferences. For example, the learning unit can use machine learning algorithms to analyze the user's past behavioral history and selection history. The learning unit can also use data mining techniques to analyze the user's social media data and identify their interests and preferences. For example, the learning unit can analyze the content of the user's social media posts and the number of likes to identify topics of interest. This allows the learning unit to learn the user's interests and preferences and provide more personalized educational content. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input the user's behavioral history data into an AI and have the AI ​​perform the identification of interests and preferences.

[0077] The generation unit can generate educational content in the form of lectures and Q&A sessions based on the user's interests and preferences. For example, the generation unit can use a text generation AI (e.g., LLM) to generate lectures and Q&A content based on the user's interests and preferences. The generation unit can also use a multimodal generation AI to generate content such as video lectures and interactive quizzes. For example, if the user is interested in "history," the generation unit will generate lectures and Q&A content related to history. This allows for the generation of educational content based on the user's interests and preferences. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user interest and preference data into an AI and have the AI ​​generate the educational content.

[0078] The integration unit can integrate the generated educational content into the AR headset, tailored to the user's personality. For example, the integration unit selects appropriate visual effects and interactions based on analysis of the user's psychological test results and behavioral patterns. The integration unit can also adjust the display method of the educational content and the format of interactions according to the user's personality. For example, if the user has an introverted personality, the integration unit uses quiet and calming visual effects. This allows for the integration of educational content tailored to the user's personality into the AR headset. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input user personality data into an AI and have the AI ​​select appropriate visual effects.

[0079] The service provider can deliver integrated educational content in real time. For example, the service provider can enable users to view educational content while riding through an AR headset. The service provider can also monitor the progress of the educational content in real time and update the content as needed. For example, if a user asks a question while learning, the service provider can provide an answer to that question in real time. This enables the delivery of educational content in real time. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input educational content progress data into the AI ​​and have the AI ​​perform real-time content updates.

[0080] The reception desk can estimate the user's emotions and adjust the timing of confirming the user's desire for educational content based on the estimated emotions. For example, if the user is relaxed, the reception desk may confirm the user's desire for educational content immediately after boarding. Alternatively, if the user is stressed, the reception desk may confirm the user's desire for educational content some time after boarding. For example, if the user is in a hurry, the reception desk may confirm the user's desire for educational content before boarding. This allows the timing of confirming the user's desire for educational content to be adjusted according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into an AI and have the AI ​​adjust the timing of confirming the user's desire for educational content.

[0081] The reception desk can analyze the user's past selection history and suggest the optimal topic selection method. For example, the reception desk can automatically suggest related topics based on the topics the user has selected in the past. The reception desk can also analyze the frequency of topics the user has selected in the past and prioritize displaying the most frequently selected topics. For example, the reception desk can predict and suggest topics selected during a specific time period based on the user's past selection history. This allows the reception desk to suggest the optimal topic selection method based on the user's past selection history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's selection history data into AI and have the AI ​​perform the task of suggesting the optimal topic selection method.

[0082] The reception desk can filter requests for educational content based on the user's current learning status and areas of interest. For example, the reception desk can suggest content at an appropriate level based on the user's current learning progress. The reception desk can also prioritize displaying relevant topics based on the user's areas of interest. For example, the reception desk can analyze the user's learning history and suggest unlearned topics. This allows for the provision of appropriate educational content based on the user's current learning status and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's learning status data into an AI and have the AI ​​perform the filtering.

[0083] The reception desk can estimate the user's emotions and determine the priority of topic selection based on the estimated emotions. For example, if the user is relaxed, the reception desk will prioritize displaying topics of interest. Similarly, if the user is stressed, the reception desk can prioritize displaying topics that promote relaxation. For example, if the user is in a hurry, the reception desk will prioritize displaying topics that can be learned quickly. This allows the system to prioritize topic selection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into an AI and have the AI ​​determine the priority of topic selection.

[0084] When the reception desk confirms the user's request for educational content, it can prioritize presenting highly relevant topics by considering the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize displaying topics related to that region. Similarly, if the user is traveling, the reception desk can prioritize displaying topics related to their travel destination. For example, if the user is at home, the reception desk can prioritize displaying topics that can be learned at home. This allows the reception desk to provide highly relevant topics based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location data into an AI and have the AI ​​perform the task of presenting highly relevant topics.

[0085] The reception desk can analyze a user's social media activity and suggest relevant topics when confirming their desire for educational content. For example, the reception desk may prioritize displaying topics that the user frequently mentions on social media. It can also suggest topics that the user's social media followers are interested in. For example, the reception desk may analyze the user's social media posts and suggest relevant topics. This allows the reception desk to provide relevant topics based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input the user's social media data into an AI and have the AI ​​suggest relevant topics.

[0086] The learning unit can estimate the user's emotions and adjust the parameters of the learning algorithm based on the estimated emotions. For example, if the user is relaxed, the learning unit can set the parameters of the learning algorithm loosely. Conversely, if the user is stressed, the learning unit can also ease the parameters of the learning algorithm. For example, if the user is focused, the learning unit can set the parameters of the learning algorithm tightly. This allows the parameters of the learning algorithm to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Generative AIs include, but are not limited to, text generation AIs (e.g., LLMs) or multimodal generation AIs. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input user emotion data into an AI and have the AI ​​adjust the parameters of the learning algorithm.

[0087] The learning unit can optimize the learning algorithm by referring to the user's past learning history during the learning process. For example, the learning unit can propose the optimal learning pattern based on the user's past learning history. The learning unit can also extract effective learning methods from the user's past learning history and reflect them in the algorithm. For example, the learning unit can analyze the user's past learning history and adjust the algorithm according to the learning progress. This allows the learning algorithm to be optimized based on the user's past learning history. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit can input the user's learning history data into AI and have the AI ​​perform the optimization of the learning algorithm.

[0088] The learning unit can incorporate a feedback mechanism to reflect changes in the user's interests and preferences in real time during the learning process. For example, if the user shows interest in a new topic, the learning unit will update the learning content in real time. The learning unit can also instantly adjust the learning algorithm if the user's preferences change. For example, the learning unit can optimize the learning content in real time based on user feedback. This allows changes in the user's interests and preferences to be reflected in real time. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit can input user feedback data into the AI ​​and have the AI ​​update the learning content.

[0089] The learning unit can estimate the user's emotions and prioritize training data based on the estimated emotions. For example, if the user is relaxed, the learning unit will prioritize providing data of interest. Similarly, if the user is stressed, the learning unit can prioritize providing relaxing data. For example, if the user is focused, the learning unit will prioritize providing data of high difficulty. This allows the learning unit to prioritize training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input user emotion data into an AI and have the AI ​​determine the priority of training data.

[0090] The learning unit can optimize its learning algorithm during training by taking into account the user's geographical location. For example, if the user is in a specific region, the learning unit can prioritize providing data related to that region. Similarly, if the user is traveling, the learning unit can prioritize providing data related to their travel destination. For example, if the user is at home, the learning unit can prioritize providing data that can be studied at home. This allows the learning algorithm to be optimized based on the user's geographical location. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input the user's geographical location data into an AI and have the AI ​​optimize the learning algorithm.

[0091] The learning unit can analyze the user's social media activity during training and incorporate relevant data into its learning process. For example, the learning unit can reflect topics that the user frequently mentions on social media in its learning data. It can also incorporate topics that the user's social media followers are interested in into its learning data. For example, the learning unit can analyze the content of the user's social media posts and incorporate relevant data into its learning process. This allows the learning unit to incorporate relevant data based on the user's social media activity. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit can input the user's social media data into an AI and have the AI ​​perform learning on the relevant data.

[0092] The generation unit can estimate the user's emotions and adjust the method of generating educational content based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate educational content that progresses at a relaxed pace. Alternatively, if the user is in a hurry, the generation unit can generate educational content that can be learned in a short amount of time. For example, if the user is excited, the generation unit can generate educational content with visually stimulating effects. This allows the method of generating educational content to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into an AI and have the AI ​​adjust the method of generating educational content.

[0093] The generation unit can generate optimal educational content by referring to the user's past learning history. For example, the generation unit can generate educational content that includes relevant topics based on the user's past learning history. The generation unit can also generate educational content that reflects effective learning methods based on the user's past learning history. For example, the generation unit can analyze the user's past learning history and generate educational content that corresponds to their learning progress. This allows for the generation of optimal educational content based on the user's past learning history. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can input the user's learning history data into AI and have the AI ​​generate optimal educational content.

[0094] The generation unit can adjust the level of detail of educational content based on the user's current learning status when generating educational content. For example, the generation unit can generate educational content with detailed explanations based on the user's current learning progress. Alternatively, the generation unit can generate educational content with concise explanations depending on the user's current learning status. For example, the generation unit can analyze the user's current learning status and generate educational content of appropriate difficulty. This allows for the generation of educational content with appropriate detail levels according to the user's current learning status. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user learning status data into AI and have the AI ​​perform the adjustment of content detail.

[0095] The generation unit can estimate the user's emotions and determine the priority of content to generate based on the estimated emotions. For example, if the user is relaxed, the generation unit will prioritize generating topics of interest. Similarly, if the user is stressed, the generation unit can prioritize generating relaxing topics. For example, if the user is in a hurry, the generation unit will prioritize generating topics that can be learned quickly. This allows for the prioritization of content generation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into an AI and have the AI ​​determine the content prioritization.

[0096] The generation unit can generate optimal educational content by considering the user's geographical location information. For example, if the user is in a specific region, the generation unit can generate educational content related to that region. Furthermore, if the user is traveling, the generation unit can generate educational content related to their travel destination. For example, if the user is at home, the generation unit can generate educational content that can be studied at home. This allows for the generation of optimal educational content based on the user's geographical location information. Some or all of the above-described processes in the generation unit may be performed using AI, or they may not. For example, the generation unit can input the user's geographical location data into an AI and have the AI ​​generate optimal educational content.

[0097] The generation unit can analyze a user's social media activity and generate relevant content when generating educational content. For example, the generation unit can reflect topics that the user frequently mentions on social media in the educational content. The generation unit can also incorporate topics that the user's social media followers are interested in into the educational content. For example, the generation unit can analyze the content of a user's social media posts and generate relevant educational content. This allows for the generation of relevant educational content based on the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's social media data into an AI and have the AI ​​generate relevant educational content.

[0098] The integration unit can estimate the user's emotions and adjust the integration method to the AR headset based on the estimated user emotions. For example, if the user is relaxed, the integration unit may use visually calming effects for integration. Alternatively, if the user is stressed, the integration unit may use visually relaxing effects for integration. For example, if the user is excited, the integration unit may use visually stimulating effects for integration. This allows the integration method to be adjusted to the AR headset according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input user emotion data into an AI and have the AI ​​adjust the integration method.

[0099] The integration unit can select the optimal integration method by referring to the user's past learning history during integration. For example, the integration unit can select the optimal visual effect based on the user's past learning history. The integration unit can also extract and reflect effective integration methods from the user's past learning history. For example, the integration unit can analyze the user's past learning history and select an integration method according to the learning progress. This allows the integration unit to select the optimal integration method based on the user's past learning history. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input the user's learning history data into AI and have the AI ​​select the optimal integration method.

[0100] The integration unit can adjust the level of detail of the integration based on the user's current learning status during the integration process. For example, the integration unit may use detailed visual effects to integrate based on the user's current learning progress. Alternatively, the integration unit may use simplified visual effects depending on the user's current learning status. For example, the integration unit may analyze the user's current learning status and integrate using visual effects of appropriate difficulty. This allows for integration with an appropriate level of detail according to the user's current learning status. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit may input user learning status data into AI and have the AI ​​perform the adjustment of the level of detail of the integration.

[0101] The integration unit can estimate the user's emotions and determine the priority of content to integrate based on the estimated emotions. For example, if the user is relaxed, the integration unit will prioritize integrating content that interests them. Similarly, if the user is stressed, the integration unit can prioritize integrating relaxing content. For example, if the user is in a hurry, the integration unit will prioritize integrating content that can be learned quickly. This allows the integration unit to determine the priority of content to integrate according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input user emotion data into an AI and have the AI ​​determine the content priority.

[0102] The integration unit can select the optimal integration method during integration, taking into account the user's geographical location. For example, if the user is in a specific region, the integration unit will use visual effects related to that region for integration. Alternatively, if the user is traveling, the integration unit can use visual effects related to the travel destination for integration. For example, if the user is at home, the integration unit will use visual effects suitable for home learning for integration. This allows the integration unit to select the optimal integration method based on the user's geographical location. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input the user's geographical location data into an AI and have the AI ​​select the optimal integration method.

[0103] The integration unit can analyze the user's social media activity and integrate relevant content during the integration process. For example, the integration unit can reflect topics that the user frequently mentions on social media in the visual effects. It can also incorporate topics that the user's social media followers are interested in into the visual effects. For example, the integration unit can analyze the content of the user's social media posts and integrate relevant visual effects. This allows for the integration of relevant content based on the user's social media activity. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input the user's social media data into an AI and have the AI ​​perform the integration of relevant content.

[0104] The delivery unit can estimate the user's emotions and adjust the delivery method of educational content based on the estimated user emotions. For example, if the user is relaxed, the delivery unit can deliver educational content at a relaxed pace. If the user is in a hurry, the delivery unit can also deliver educational content that can be learned in a short amount of time. For example, if the user is excited, the delivery unit can deliver educational content with visually stimulating effects. This allows the delivery method of educational content to be adjusted 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 delivery unit may be performed using AI or not using AI. For example, the delivery unit can input user emotion data into AI and have the AI ​​perform the adjustment of the delivery method.

[0105] The delivery unit can select the optimal delivery method by referring to the user's past learning history at the time of delivery. For example, the delivery unit can select the optimal delivery method based on the user's past learning history. The delivery unit can also extract and reflect effective delivery methods from the user's past learning history. For example, the delivery unit can analyze the user's past learning history and select a delivery method according to the learning progress. This allows the delivery unit to select the optimal delivery method based on the user's past learning history. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's learning history data into AI and have the AI ​​perform the selection of the optimal delivery method.

[0106] The delivery unit can adjust the level of detail provided based on the user's current learning status at the time of delivery. For example, the delivery unit can provide educational content with detailed explanations based on the user's current learning progress. Alternatively, the delivery unit can provide educational content with concise explanations depending on the user's current learning status. For example, the delivery unit can analyze the user's current learning status and provide educational content of appropriate difficulty. This allows the delivery unit to provide educational content with an appropriate level of detail according to the user's current learning status. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's learning status data into AI and have the AI ​​perform the adjustment of the level of detail provided.

[0107] The content provider can estimate the user's emotions and prioritize the content to be delivered based on those emotions. For example, if the user is relaxed, the provider will prioritize topics of interest. Similarly, if the user is stressed, the provider can prioritize topics that promote relaxation. For example, if the user is in a hurry, the provider will prioritize topics that can be learned quickly. This allows the provider to prioritize content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the content provider may be performed using AI or not. For example, the content provider can input user emotion data into an AI and have the AI ​​determine the content prioritization.

[0108] The service provider can select the optimal delivery method at the time of delivery, taking into account the user's geographical location information. For example, if the user is in a specific region, the service provider can provide educational content related to that region. Alternatively, if the user is traveling, the service provider can provide educational content related to their travel destination. For example, if the user is at home, the service provider can provide educational content that can be studied at home. This allows the service provider to select the optimal delivery method based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's geographical location data into AI and have the AI ​​select the optimal delivery method.

[0109] The service provider can analyze the user's social media activity and provide relevant content at the time of delivery. For example, the service provider can reflect topics that the user frequently mentions on social media in the educational content. The service provider can also incorporate topics that the user's social media followers are interested in into the educational content. For example, the service provider can analyze the content of the user's social media posts and provide relevant educational content. This allows the service provider to provide relevant educational content based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media data into AI and have the AI ​​perform the task of providing relevant content.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The educational content delivery system can also include a feedback unit. The feedback unit collects user feedback on the educational content provided during learning and transmits it to the learning unit. For example, users can rate the content as "easy to understand" or "difficult." The feedback unit can also collect questions from users regarding specific content and transmit them to the generation unit to generate answers. Furthermore, the feedback unit can suggest improvements based on user feedback to be incorporated into future content delivery. This allows for real-time incorporation of user feedback and the delivery of more effective educational content.

[0112] The educational content delivery system can also include a rewards section. This section provides rewards when users achieve their learning goals. For example, it could award digital badges or points to users who complete a specific topic. The rewards section could also offer incentives to users who achieve a certain amount of study time. Furthermore, if users are competing with other users in their learning, the rewards section could display rankings or leaderboards and reward top-ranking users. This can increase user motivation and encourage continuous learning.

[0113] The educational content delivery system can also include a communication section. This section provides a platform where users can discuss learning content with each other. For example, users can ask other users questions about points they are unsure of during their studies. The communication section also allows users to share what they have learned and receive feedback from other users. Furthermore, the communication section can provide features that allow users to directly interact with experts and instructors, supporting them in gaining a deeper understanding. This promotes interaction among users and enhances learning effectiveness.

[0114] The educational content delivery system can also include a customization section. This customization section allows users to customize educational content to suit their own learning style and pace. For example, if a user desires a more detailed explanation of a particular topic, additional materials on that topic can be provided. The customization section can also provide a function to change the speed at which the content progresses, allowing users to adjust their learning pace. Furthermore, the customization section can suggest the optimal learning path based on the user's learning history and feedback. This allows users to create a learning environment that is best suited to them and to learn efficiently.

[0115] The educational content delivery system can also include a reminder function. This function provides reminders to help users continue their learning. For example, if a user has not studied for a certain period, the reminder function can send a notification encouraging them to resume learning. The reminder function can also periodically notify users of their progress based on their set learning goals. Furthermore, the reminder function can set reminders to help users develop the habit of studying at specific times of the day. This makes it easier for users to continue learning and improves learning effectiveness.

[0116] The educational content delivery system can also be equipped with an emotion analysis unit. This unit analyzes the user's emotions from their facial expressions and voice, and provides appropriate feedback according to their learning progress. For example, if the user is feeling tired during learning, the emotion analysis unit suggests a break. If the user is excited, the emotion analysis unit can also provide more challenging content. Furthermore, the emotion analysis unit can adjust the learning content based on the user's emotions to provide an optimal learning experience. This enables flexible learning support that responds to the user's emotions.

[0117] The educational content delivery system can also include a motivation component. This component estimates the user's emotions and provides content designed to enhance their motivation to learn. For example, if a user has lost motivation to learn, the motivation component provides encouraging messages and success stories. Conversely, if a user is highly motivated, the motivation component can provide challenging tasks. Furthermore, the motivation component can offer rewards based on the user's emotions to support their continued motivation. This enables motivation management tailored to the user's emotions.

[0118] The educational content delivery system can also include a relaxation section. This section estimates the user's emotions and provides relaxing content. For example, if the user is stressed, the relaxation section provides relaxing music or meditation guidance. If the user is tense, the relaxation section can also suggest deep breathing exercises. Furthermore, the relaxation section can provide a relaxing environment based on the user's emotions and support them in enhancing learning effectiveness. This enables relaxation support tailored to the user's emotions.

[0119] The educational content delivery system can also include an interactive section. This interactive section estimates the user's emotions and provides an interactive learning experience tailored to those emotions. For example, if the user is excited, the interactive section might offer a game-style quiz. If the user is relaxed, the interactive section could also offer an interactive story that progresses at a gentle pace. Furthermore, the interactive section can adjust the form of interaction based on the user's emotions to provide an optimal learning experience. This enables an interactive learning experience that responds to the user's emotions.

[0120] The educational content delivery system can also include an engagement component. This component estimates the user's emotions and provides content to enhance engagement in accordance with those emotions. For example, if a user has lost interest in learning, the engagement component provides engaging topics and activities. Conversely, if a user is highly engaged in learning, the engagement component can also provide additional resources for further exploration. Furthermore, the engagement component can provide support to maintain engagement based on the user's emotions. This enables engagement management that is tailored to the user's feelings.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception desk allows users to choose whether they wish to receive educational content when they board a ride using the ride-hailing platform. For example, it can display a screen where users can choose whether they wish to receive educational content through the application. It can also accept the user's selected topics and past learning history. Step 2: The learning unit uses AI to learn the user's interests and preferences based on the information received by the reception unit. For example, it uses machine learning algorithms to analyze the user's past behavior and selection history. It can also analyze the user's social media data to identify their interests and preferences. Step 3: The generation unit uses AI to generate educational content based on the information learned by the learning unit. For example, it can use text generation AI (e.g., LLM) to generate lectures and Q&A content based on the user's interests and preferences. It can also use multimodal generation AI to generate content such as video lectures and interactive quizzes. Step 4: The integration unit integrates the educational content generated by the generation unit into the AR headset, tailored to the user's personality. For example, it selects appropriate visual effects and interactions based on the user's psychological test results and behavioral pattern analysis. Step 5: The delivery unit provides the integrated educational content in real time. For example, it allows users to view educational content while riding through an AR headset. It can also monitor the progress of the educational content in real time and update the content as needed.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the reception unit, learning unit, generation unit, integration unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and displays a screen in which the user can select whether or not they wish to receive educational content through an application. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the user's past behavioral history and selection history using a machine learning algorithm. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates lecture and Q&A content based on the user's interests and preferences using a text generation AI. The integration unit is implemented, for example, by the control unit 46A of the smart device 14 and integrates the generated educational content into the AR headset according to the user's personality. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 and enables the user to view the educational content while riding through the AR headset. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, learning unit, generation unit, integration unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and displays a screen in which the user can select whether or not they wish to receive educational content through an application. The learning unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's past behavioral history and selection history using a machine learning algorithm. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates lecture and Q&A content based on the user's interests and preferences using a text generation AI. The integration unit is implemented by the control unit 46A of the smart glasses 214 and integrates the generated educational content into the AR headset according to the user's personality. The provision unit is implemented by the control unit 46A of the smart glasses 214 and enables the user to view the educational content while riding through the AR headset. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the reception unit, learning unit, generation unit, integration unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and displays a screen in which the user can select whether or not they wish to receive educational content through an application. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the user's past behavioral history and selection history using a machine learning algorithm. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates lecture and Q&A content based on the user's interests and preferences using a text generation AI. The integration unit is implemented, for example, by the control unit 46A of the headset terminal 314 and integrates the generated educational content into the AR headset according to the user's personality. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 and enables the user to view the educational content while riding through the AR headset. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the reception unit, learning unit, generation unit, integration unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and displays a screen in which the user can select whether or not they wish to receive educational content through an application. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the user's past behavioral history and selection history using a machine learning algorithm. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates lecture and Q&A content based on the user's interests and preferences using a text generation AI. The integration unit is implemented, for example, by the control unit 46A of the robot 414 and integrates the generated educational content into an AR headset according to the user's personality. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and enables the user to view the educational content while riding through the AR headset. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) When a user uses the ride-hailing platform to get into a ride, there is a reception area where they can choose whether or not they want to receive educational content. Based on the information received by the aforementioned reception unit, a learning unit learns the user's interests and preferences, A generation unit generates educational content based on the information learned by the aforementioned learning unit, An integration unit that integrates the educational content generated by the generation unit into an AR headset according to the user's personality, The system comprises a provisioning unit that provides the integrated educational content in real time, which is integrated by the aforementioned integration unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is Accepts user-selected topics and past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned learning unit, We use algorithms to learn the user's interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Based on user interests and preferences, educational content is generated in the form of lectures and Q&A sessions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned integration unit is Integrate the generated educational content into the AR headset, tailored to the user's personality. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We provide integrated educational content in real time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We estimate the user's emotions and adjust the timing of confirming their desire to receive educational content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past selection history and suggest the optimal topic selection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When confirming requests for educational content, 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 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of topic selection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When confirming requests for educational content, the system prioritizes presenting highly relevant topics by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When confirming a user's interest in educational content, the system analyzes their social media activity and presents relevant topics. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned learning unit, It estimates the user's emotions and adjusts the parameters of the learning algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned learning unit, During the learning process, a feedback mechanism will be introduced to reflect changes in the user's interests and preferences in real time. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned learning unit, It estimates the user's emotions and prioritizes the training data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned learning unit, During training, the learning algorithm is optimized by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned learning unit, During training, the system analyzes users' social media activity and incorporates relevant data into the learning process. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is We estimate user emotions and adjust how educational content is generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating educational content, the system references the user's past learning history to create the most suitable content. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating educational content, adjust the level of detail based on the user's current learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates user sentiment and determines the priority of content to generate based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating educational content, the system takes into account the user's geographical location to create the most suitable content. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating educational content, we analyze users' social media activity and generate relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned integration unit is It estimates the user's emotions and adjusts the integration method into the AR headset based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned integration unit is During integration, the system selects the optimal integration method by referring to the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned integration unit is During integration, adjust the level of detail of the integration based on the user's current learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned integration unit is It estimates user sentiment and determines the priority of content to integrate based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned integration unit is During integration, the optimal integration method is selected, taking into account the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned integration unit is During integration, the system analyzes users' social media activity and integrates relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, We estimate user emotions and adjust the delivery method of educational content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing the content, the system will refer to the user's past learning history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing content, adjust the level of detail based on the user's current learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the content to be delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing content, we analyze the user's social media activity and deliver relevant content. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 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. When a user uses the ride-hailing platform to get into a ride, there is a reception area where they can choose whether or not they want to receive educational content. Based on the information received by the aforementioned reception unit, a learning unit learns the user's interests and preferences, A generation unit generates educational content based on the information learned by the aforementioned learning unit, An integration unit that integrates the educational content generated by the generation unit into an AR headset according to the user's personality, The system comprises a provisioning unit that provides the integrated educational content in real time, which is integrated by the aforementioned integration unit. A system characterized by the following features.

2. The aforementioned reception unit is Accepts user-selected topics and past learning history. The system according to feature 1.

3. The aforementioned learning unit, We use algorithms to learn the user's interests and preferences. The system according to feature 1.

4. The generating unit is Based on user interests and preferences, educational content is generated in the form of lectures and Q&A sessions. The system according to feature 1.

5. The aforementioned integration unit is Integrate the generated educational content into the AR headset, tailored to the user's personality. The system according to feature 1.

6. The aforementioned supply unit is, We provide integrated educational content in real time. The system according to feature 1.

7. The aforementioned reception unit is We estimate the user's emotions and adjust the timing of confirming their desire to receive educational content based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past selection history and suggest the optimal topic selection method. The system according to feature 1.

9. The aforementioned reception unit is When confirming requests for educational content, filtering is performed based on the user's current learning status and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of topic selection based on the estimated user emotions. The system according to feature 1.

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