system

Abstract

The system according to the embodiment aims to manage an individual's learning trajectory and provide appropriate rewards. [Solution] The system according to the embodiment includes a management unit, a recommendation unit, a content management unit, a reward unit, and an education management unit. The management unit manages learning trajectories. The recommendation unit recommends appropriate information and learning steps to individuals based on the learning trajectory managed by the management unit. The content management unit manages content based on the information and learning steps recommended by the recommendation unit. The reward unit awards rewards based on the content managed by the content management unit. The education management unit provides education and management for companies and local governments based on the rewards awarded by the reward unit.

Description

[Technical Field]

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

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

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

[0004] Existing technologies do not adequately provide a system that effectively manages an individual's learning trajectory and provides appropriate rewards, and there is room for improvement.

[0005] The system according to the embodiment aims to manage an individual's learning trajectory and provide appropriate rewards. [Means for solving the problem]

[0006] The system according to the embodiment includes a management unit, a recommendation unit, a content management unit, a reward unit, and an education management unit. The management unit manages learning trajectories. The recommendation unit recommends appropriate information and learning steps to individuals based on the learning trajectories managed by the management unit. The content management unit manages content based on the information and learning steps recommended by the recommendation unit. The reward unit awards rewards based on the content managed by the content management unit. The education management unit provides education and management for companies and local governments based on rewards awarded by the reward unit. [Effects of the Invention]

[0007] The system according to the embodiment can manage an individual's learning trajectory and provide appropriate rewards. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The Study to Earn platform according to an embodiment of the present invention is a system that uses generative AI to provide appropriate compensation to individuals who provide useful know-how. The more experience and skills an individual acquires, the more rewards (tokens) they receive. This system manages learning trajectories on a blockchain and incorporates AI that recommends optimal information and learning steps for each individual. AI also manages content. Furthermore, the platform aims to monetize as a human resources training and management platform for companies and local governments. For example, a user registers on the platform and sets the field of study and goals they wish to study. Next, the AI ​​recommends optimal learning steps and information based on the user's settings. For example, a user who wants to learn programming is provided with a sequential guide from basic to advanced level, and the next step is recommended based on progress. Furthermore, the user's learning content and results are recorded on the blockchain, preventing tampering. Furthermore, a system allows users to earn rewards (tokens) by sharing what they have learned with other users. For example, if a user provides their learning know-how as an article or video and other users find it useful, they are awarded tokens. These tokens can be used for services within the platform or converted into cash. For companies and local governments, the platform can be provided as a platform for streamlining the education and management of employees and citizens. For example, companies can use the platform to improve their employees' skills and manage their learning progress and results. Local governments can also provide educational programs for their citizens and manage their learning results. In this way, the present invention provides a "Study to Earn" platform that promotes individual learning and earns rewards, and can also be monetized as a human resource education and management platform for companies and local governments. As a result, the Study to Earn platform promotes individual learning and earns rewards. It can also be monetized as a human resource education and management platform for companies and local governments.

[0029] The Study to Earn platform according to the embodiment includes a management unit, a recommendation unit, a content management unit, a reward unit, and an education management unit. The management unit manages a learning trajectory. The learning trajectory includes, but is not limited to, a learning history, learning outcomes, and learning time. For example, the management unit stores the learning history in a database and evaluates the learning outcomes. The management unit can also record learning time and manage learning progress. For example, the management unit displays the learning history in chronological order to visualize learning progress. The management unit can also evaluate learning outcomes and measure learning effectiveness. The recommendation unit recommends appropriate information and learning steps to individuals based on the learning trajectory managed by the management unit. For example, the recommendation unit recommends optimal learning steps based on the user's learning history and goals. The recommendation unit can also provide appropriate information based on the user's learning style and interests. For example, the recommendation unit analyzes the user's learning history and recommends the next learning step. The recommendation unit can also provide related information based on the user's interests. The content management unit manages content based on the information and learning steps recommended by the recommendation unit. For example, the content management unit classifies learning content and organizes it into appropriate categories. The content management unit can also evaluate the quality of the learning content and improve it as necessary. For example, the content management unit regularly updates the learning content to provide the latest information. The content management unit can also improve the learning content based on user feedback. The reward unit grants rewards based on the content managed by the content management unit. For example, the reward unit grants rewards when a user shares what they have learned with other users. The reward unit can also grant rewards based on the user's learning outcomes. For example, the reward unit provides what a user has learned as an article or video and grants tokens if it is evaluated as useful by other users. The reward unit can also grant points or badges based on the user's learning outcomes. The education management unit manages education and training for companies and local governments based on the rewards granted by the reward unit.For example, the education management department allows companies to use the platform to improve the skills of their employees and manage the learning progress and results of employees. The education management department can also allow local governments to provide educational programs for their citizens and manage their learning results. For example, the education management department provides customized educational plans for companies and local governments and performs efficient education and management. This allows the Study to Earn platform according to the embodiment to promote individual learning and earn rewards. It can also be monetized as a human resource training and management platform for companies and local governments.

[0030] The management unit can manage the learning trajectory on a blockchain. Examples of blockchains include, but are not limited to, public blockchains, private blockchains, and consortium blockchains. The management unit can manage the learning trajectory using, for example, a public blockchain. A public blockchain is a decentralized network that anyone can participate in, making data tampering difficult. The management unit can also manage the learning trajectory using a private blockchain. A private blockchain is a decentralized network used only within a specific organization or group, allowing for access control. The management unit can also manage the learning trajectory using a consortium blockchain. A consortium blockchain is a decentralized network jointly operated by multiple organizations, allowing for both data sharing and security. By managing the learning trajectory on a blockchain, tampering is prevented. Some or all of the above-described processing in the management unit can be performed using, for example, AI, or without AI. For example, when recording learning trajectory data on the blockchain, the management unit can use AI to verify the integrity of the data and detect tampering.

[0031] The recommendation unit can recommend appropriate learning steps and information based on the user's settings. The recommendation unit recommends optimal learning steps based on, for example, the user's learning goals and areas of interest. For example, if a user wants to learn programming, the recommendation unit can provide sequential steps from basic to advanced and recommend the next step based on the user's progress. The recommendation unit can also provide appropriate information based on the user's learning style. For example, the recommendation unit can recommend video content to a user who prefers visual learning, and text content to a user who prefers reading comprehension. The recommendation unit can also analyze the user's learning history and recommend the next step to learn. For example, the recommendation unit can recommend the next step to learn based on the user's past learning history. The recommendation unit can also provide related information based on the user's interests. For example, the recommendation unit can provide information related to topics in which the user has shown interest. This enables personalized learning by recommending optimal learning steps and information based on the user's settings. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's learning history and interests into the AI, which can then recommend the most appropriate learning steps and information.

[0032] The content management unit can manage content based on recommended information and learning steps. The content management unit, for example, classifies learning content and organizes it into appropriate categories. For example, the content management unit can categorize learning content by topic, difficulty level, and format to enable easy user access. The content management unit can also evaluate the quality of the learning content and improve it as necessary. For example, the content management unit can evaluate the quality of the learning content based on user feedback and identify areas for improvement. The content management unit can also periodically update the learning content to provide the latest information. For example, the content management unit can adjust the update frequency of the learning content according to the user's learning progress. This improves the quality of learning by managing content based on recommended information and learning steps. Some or all of the above-described processing in the content management unit may be performed using, or without, AI. For example, the content management unit can input the classification and quality evaluation of the learning content into AI, which can then organize the content into the most appropriate category and evaluate its quality.

[0033] The reward unit can reward a user for sharing what they have learned with other users. For example, if a user provides their learning know-how as an article or video and it is evaluated as useful by other users, the reward unit can award tokens. The reward unit can also award rewards based on the user's learning outcomes. For example, the reward unit can award points or badges when a user achieves a specific learning goal. The reward unit can also award rewards when a user shares what they have learned with other users. For example, the reward unit can award tokens when a user shares their learning know-how and it is evaluated as useful by other users. This promotes the sharing of learning by rewarding users for sharing what they have learned with other users. Some or all of the above-described processing in the reward unit can be performed, for example, using AI, or can be performed without AI. For example, the reward unit can input the user's learning outcomes and shared content into AI, which can then award rewards.

[0034] The education management unit can provide education and management for employees and citizens for companies and local governments. For example, companies can use the platform to improve their employees' skills and manage their learning progress and results. The education management unit can also provide educational programs for citizens and manage their learning results for local governments. For example, the education management unit can provide customized educational plans for companies and local governments and provide efficient education and management. This enables efficient education and management by providing education and management for employees and citizens for companies and local governments. Some or all of the above-mentioned processing in the education management unit can be performed using, for example, AI, or without AI. For example, the education management unit can input the learning progress and results of employees and citizens into AI, which can then provide and manage optimal education plans.

[0035] The management unit can update the learning trajectory in real time and dynamically manage it according to the user's progress. For example, the management unit updates the learning trajectory in real time each time the user completes a new learning step. The management unit can also display the latest learning trajectory when the user checks their progress. Furthermore, if the user interrupts learning, the management unit can update the learning trajectory based on the latest progress information when the user resumes learning. In this way, by updating the learning trajectory in real time and dynamically managing it according to the user's progress, the latest information is always reflected. Real-time updates are performed taking into account, for example, the frequency of data updates, delay time, synchronization method, etc. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without AI. For example, the management unit can input the user's learning progress data into a generation AI, which updates and manages the data in real time.

[0036] The management unit can compare the learning trajectory with the user's past learning history and evaluate the progress. For example, the management unit can compare the user's past learning history with the user's current learning trajectory and evaluate the progress. The management unit can also compare the user's current progress with learning goals previously achieved and evaluate the progress. Furthermore, the management unit can evaluate the user's current progress based on the user's past learning history and provide feedback. In this way, the learning trajectory is compared with the user's past learning history and the progress is evaluated, thereby visualizing the user's growth. Past learning history includes, for example, study time, learning outcomes, learning content, etc., but is not limited to these examples. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without AI. For example, the management unit can input the user's past learning history data into a generation AI, which can evaluate the progress and provide feedback.

[0037] The management unit can share the learning trajectory with other users to promote collaborative learning. For example, the management unit allows a user to share their learning trajectory with other users and check the progress of the collaborative learning. The management unit can also receive feedback from other users when a user shares their learning trajectory. Furthermore, the management unit can allow a user to join a collaborative learning group, share their learning trajectory, and compare progress. This promotes collaborative learning by sharing the learning trajectory with other users. Collaborative learning includes, but is not limited to, the collaborative learning format, the roles of participants, and the learning process. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without AI. For example, the management unit can input learning trajectory data into a generation AI, which then shares the data with other users and manages the progress of the collaborative learning.

[0038] The management unit can synchronize the learning trajectory across different devices, allowing the user to continue learning anywhere. For example, the management unit allows a user to start learning on a smartphone and continue the learning trajectory by synchronizing it on a tablet. The management unit can also allow a user to study on a desktop and then synchronize the learning trajectory to a laptop while on the go. The management unit can also synchronize the learning trajectory of a user across different devices in real time, allowing the user to continue learning without interruption. This allows the user to continue learning anywhere by synchronizing the learning trajectory across different devices. Synchronization across different devices includes, but is not limited to, the timing of synchronization, data consistency, and the technology used. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without AI. For example, the management unit can input learning trajectory data into a generation AI, which can then synchronize data across different devices to continue learning.

[0039] The recommendation unit can analyze the user's learning history and recommend optimal learning steps based on past successful experiences. For example, the recommendation unit recommends the next step based on the user's past successful learning steps. The recommendation unit can also prioritize recommending areas in the user's learning history where the user has had many successful experiences. Furthermore, the recommendation unit can analyze the user's past learning history and recommend the most effective learning steps. This improves the effectiveness of learning by analyzing the user's learning history and recommending optimal learning steps based on the user's past successful experiences. Past successful experiences include, but are not limited to, successful learning steps, achieved goals, factors behind success, etc. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without AI. For example, the recommendation unit can input the user's past learning history data into a generation AI, which can analyze the user's successful experiences and recommend optimal learning steps.

[0040] The recommendation unit can recommend different types of learning content depending on the user's learning style. For example, if the user prefers visual learning, the recommendation unit can recommend video content. Furthermore, if the user prefers reading comprehension, the recommendation unit can also recommend text content. Furthermore, if the user prefers interactive learning, the recommendation unit can also recommend interactive content. By recommending different types of learning content depending on the user's learning style, the effectiveness of learning is improved. Examples of different types of learning content include, but are not limited to, video, text, interactive content, etc. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input the user's learning style data into a generation AI, which can then recommend the optimal type of learning content.

[0041] The recommendation unit can recommend region-specific learning content based on the user's geographical location information. For example, if the user is in a specific region, the recommendation unit can recommend learning content related to that region. Furthermore, if the user is traveling, the recommendation unit can recommend learning content related to the region the user is visiting. Furthermore, if the user is interested in a specific region, the recommendation unit can recommend learning content specific to that region. This improves learning effectiveness by recommending region-specific learning content taking the user's geographical location information into consideration. Examples of geographical location information include, but are not limited to, GPS data, location-based services, and types of region-specific content. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input the user's geographical location information data into a generation AI, which can then recommend region-specific learning content.

[0042] The recommendation unit can analyze the user's social media activity and recommend relevant learning content. For example, the recommendation unit can recommend learning content related to topics in which the user has shown interest on social media. The recommendation unit can also recommend learning content based on the content posted by accounts the user follows. Furthermore, the recommendation unit can recommend learning content related to content shared by the user on social media. This improves the effectiveness of learning by analyzing the user's social media activity and recommending relevant learning content. Social media activity includes, but is not limited to, the content of posts, the number of likes, the number of followers, etc. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input the user's social media activity data into a generation AI, which then recommends relevant learning content.

[0043] The content management unit can evaluate the quality of the content and make improvements based on user feedback. For example, the content management unit can evaluate the quality of the content based on user feedback and identify areas for improvement. The content management unit can also aggregate user evaluations and propose specific improvements to improve the quality of the content. Furthermore, the content management unit can analyze user feedback and continuously improve the quality of the content. As a result, the quality of the content is improved by evaluating the quality of the content and making improvements based on user feedback. Content quality includes, but is not limited to, user feedback, content accuracy, and comprehensiveness of information. Some or all of the above-mentioned processing in the content management unit can be performed using, for example, AI, or without AI. For example, the content management unit can input user feedback data into a generation AI, which can evaluate the quality and propose improvements.

[0044] The content management unit can adjust the content update frequency according to the user's learning progress. For example, if the user's learning progress is rapid, the content management unit can increase the content update frequency. Furthermore, if the user's learning progress is slow, the content management unit can also decrease the content update frequency. Furthermore, the content management unit can update the content at the optimal timing according to the user's learning progress. By adjusting the content update frequency according to the user's learning progress, the effectiveness of learning is improved. The content update frequency includes, but is not limited to, for example, the timing of updates and criteria for selecting update content. Some or all of the above-described processing in the content management unit may be performed using, or without, AI. For example, the content management unit can input the user's learning progress data into a generation AI, which can adjust the update frequency and update the content at the optimal timing.

[0045] The content management unit can classify content into different categories and customize it according to the user's interests. For example, the content management unit can display related content by category based on the user's interests. Furthermore, if the user shows interest in a particular category, the content management unit can prioritize displaying content in that category. Furthermore, the content management unit can customize the display order of content according to the user's interests. This improves learning effectiveness by classifying content into different categories and customizing it according to the user's interests. Examples of different categories include, but are not limited to, by topic, difficulty level, format, etc. Some or all of the above-described processing in the content management unit may be performed using, or without, AI. For example, the content management unit can input user interest data into a generation AI, which then classifies the content and customizes the display order.

[0046] The content management unit can analyze the relevance of content and provide the user with an optimal learning path. For example, the content management unit can analyze related content based on the user's learning history and provide the optimal learning path. The content management unit can also recommend other content related to the content the user is currently learning and provide the learning path. Furthermore, the content management unit can customize the optimal learning path based on the user's interests and goals. This improves learning effectiveness by analyzing the relevance of content and providing the user with the optimal learning path. Examples of content relevance include, but are not limited to, commonalities in topics, learning flow, and related knowledge. Some or all of the above-described processing in the content management unit may be performed using, or without, AI. For example, the content management unit can input the user's learning history data into a generation AI, which then analyzes the relevance and provides the optimal learning path.

[0047] The reward unit can dynamically change the reward awarding criteria according to the user's learning achievement. For example, if the user's learning achievement is high, the reward unit can relax the reward awarding criteria and award a larger reward. Furthermore, if the user's learning achievement is low, the reward unit can tighten the reward awarding criteria and limit the awarding of rewards. Furthermore, the reward unit can dynamically adjust the reward awarding criteria according to the user's learning achievement to provide an optimal reward. By dynamically changing the reward awarding criteria according to the user's learning achievement, an appropriate reward can be provided. The reward awarding criteria include, but are not limited to, for example, learning achievement evaluation criteria, reward type, and awarding timing. Some or all of the above-described processing in the reward unit may be performed using, for example, AI, or may be performed without AI. For example, the reward unit can input the user's learning achievement data into a generation AI, which can dynamically adjust the awarding criteria and provide an optimal reward.

[0048] The reward unit can analyze the reward history and provide an optimal reward plan to maintain the user's motivation. For example, the reward unit can analyze the user's past reward history and propose an optimal reward plan. The reward unit can also adjust the frequency and amount of rewards to maintain the user's motivation. Furthermore, the reward unit can provide an individually customized reward plan based on the user's reward history. This promotes continued learning by analyzing the reward history and providing an optimal reward plan to maintain the user's motivation. The reward history includes, but is not limited to, past reward types, timing of granting, and reward usage status. Some or all of the above-described processing in the reward unit may be performed using, for example, AI, or may be performed without AI. For example, the reward unit can input the user's reward history data into a generation AI, which analyzes the history and provides an optimal reward plan.

[0049] The reward unit can provide rewards in different formats to increase user choices. For example, the reward unit can allow the user to select a token-type reward. The reward unit can also allow the user to select a point-type reward. Furthermore, the reward unit can allow the user to select a badge-type reward. This increases user choices and improves motivation by providing rewards in different formats. Examples of different types of rewards include, but are not limited to, tokens, points, badges, etc. Some or all of the above-described processing in the reward unit can be performed using AI, or can be performed without AI. For example, the reward unit can input the user's reward selection data into a generation AI, which can then provide the optimal type of reward.

[0050] The reward unit can analyze the reward usage history and suggest the optimal way to use the reward to the user. For example, the reward unit can analyze the user's past reward usage history and suggest the optimal way to use the reward. The reward unit can also analyze how the user uses the reward and suggest the optimal way to use the reward. Furthermore, the reward unit can suggest an individually customized way to use the reward based on the user's reward usage history. This maximizes the effectiveness of the reward by analyzing the reward usage history and suggesting the optimal way to use the reward to the user. The reward usage history includes, for example, the reward usage status, frequency of use, and purpose of use, but is not limited to such examples. Some or all of the above-mentioned processing in the reward unit may be performed using, for example, AI, or may be performed without using AI. For example, the reward unit can input the user's reward usage history data into a generation AI, which analyzes the history and suggests the optimal way to use the reward.

[0051] The education management unit can monitor the progress of education management in real time and intervene as necessary. For example, the education management unit can monitor the user's progress in education management in real time and intervene if a problem occurs. The education management unit can also intervene appropriately to promote progress if the user's progress is slow. Furthermore, the education management unit can provide real-time feedback to encourage improvement if the user's progress is not going well. This improves the effectiveness of education by monitoring the progress of education management in real time and intervening as necessary. Examples of real-time monitoring include, but are not limited to, the frequency of data updates, delay times, and synchronization methods. Some or all of the above-described processing in the education management unit may be performed using AI, or may be performed without AI. For example, the education management unit can input the user's progress data into a generation AI, which can monitor the data in real time and intervene as necessary.

[0052] The education management unit can analyze education management history and provide an optimal education plan. For example, the education management unit can analyze a user's past education management history and propose an optimal education plan. The education management unit can also identify and provide an effective education plan from the user's education management history. Furthermore, the education management unit can provide an individually customized education plan based on the user's education management history. This improves the effectiveness of education by analyzing education management history and providing an optimal education plan. The education management history includes, for example, past education plans, progress, and evaluation results, but is not limited to these examples. Some or all of the above-described processing in the education management unit may be performed using, or without, AI. For example, the education management unit can input the user's education management history data into a generation AI, which then analyzes the history and provides an optimal education plan.

[0053] The education management department can link the education management data with other systems to provide comprehensive educational support. For example, the education management department can link the education management data with other learning management systems to provide comprehensive educational support. The education management department can also link the education management data with a company's human resources system to provide employee educational support. Furthermore, the education management department can link the education management data with a local government's education system to provide educational support for citizens. This enables comprehensive educational support by linking the education management data with other systems. Linking with other systems includes, for example, a data sharing method, the type of system to be linked, and the purpose of the linkage, but is not limited to these examples. Some or all of the above-mentioned processing in the education management department may be performed using, or without, AI. For example, the education management department can input the education management data into a generation AI, which then links with other systems to provide comprehensive educational support.

[0054] The education management unit can visualize the results of education management and provide feedback to the user. For example, the education management unit can visualize the results of education management using graphs and charts and provide feedback to the user. The education management unit can also provide the results of education management in report format and provide detailed feedback to the user. Furthermore, the education management unit can display the results of education management in real time and provide immediate feedback to the user. This improves the effectiveness of education by visualizing the results of education management and providing feedback to the user. Examples of the results of education management include, but are not limited to, progress, evaluation results, and feedback formats. Some or all of the above-mentioned processing in the education management unit may be performed using, or without, AI. For example, the education management unit can input education management result data into a generation AI, which can visualize the results and provide feedback.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The management unit can analyze the user's learning history and propose an optimal learning schedule to maximize the effectiveness of learning. For example, the management unit can identify the time periods in which the user was most effective in studying in the past and recommend studying during those times. The management unit can also propose appropriate break times based on the user's learning pace. Furthermore, the management unit can predict the user's learning progress based on the user's learning history and provide a schedule for achieving goals. This maximizes the effectiveness of the user's learning.

[0057] The recommendation unit can provide incentives to maintain the user's motivation to study based on the user's learning history and current learning situation. For example, the recommendation unit can provide a special reward when the user achieves a certain learning goal. The recommendation unit can also suggest challenges and competitions to encourage the user to continue studying. Furthermore, the recommendation unit can provide gradual rewards according to the user's learning progress based on the user's learning history. This helps maintain the user's motivation to study.

[0058] The content management unit can personalize learning content based on the user's learning history and feedback. For example, the content management unit can analyze the characteristics of content that the user has previously rated highly and recommend similar content. The content management unit can also adjust the format of content according to the user's learning style. Furthermore, the content management unit can identify areas for improvement in content and improve its quality based on user feedback. This allows the system to provide users with the most suitable learning content.

[0059] The reward unit can optimize the timing of reward provision based on the user's learning history and current learning situation. For example, the reward unit provides rewards at appropriate times to maintain the user's motivation to learn. The reward unit can also provide rewards in stages according to the user's learning progress. Furthermore, the reward unit can adjust the timing of reward provision based on the user's learning history to maximize the effectiveness of learning. This maintains the user's motivation to learn.

[0060] The education management unit can provide an individually customized education plan based on the user's learning history and current learning situation. For example, the education management unit can analyze the user's effective learning methods in the past and propose an education plan that incorporates those methods. The education management unit can also provide optimal learning steps based on the user's learning goals. Furthermore, the education management unit can predict the user's learning progress based on the user's learning history and provide a plan for achieving the goals. This maximizes the user's learning effectiveness.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The management department manages the learning trajectory. The learning trajectory includes learning history, learning outcomes, and learning time. The management department saves the learning history in a database and evaluates learning outcomes. It can also record learning time and manage learning progress. For example, it can display learning history in chronological order to visualize learning progress. It can also evaluate learning outcomes and measure the effectiveness of learning. Step 2: The recommendation unit recommends appropriate information and learning steps to individuals based on the learning trajectory managed by the management unit. For example, it recommends optimal learning steps based on the user's learning history and goals. It can also provide appropriate information based on the user's learning style and interests. It analyzes the user's learning history and recommends the next learning step. It can also provide related information based on the user's interests. Step 3: The content management unit manages the content based on the information and learning steps recommended by the recommendation unit. For example, it classifies the learning content and organizes it into appropriate categories. It can also evaluate the quality of the learning content and improve it if necessary. It regularly updates the learning content to provide the latest information. It can also improve the learning content based on user feedback. Step 4: The reward unit awards rewards based on the content managed by the content management unit. For example, a user can be rewarded by sharing what they have learned with other users. Rewards can also be awarded based on the user's learning outcomes. If a user provides what they have learned as an article or video and it is evaluated as useful by other users, tokens are awarded. Points and badges can also be awarded based on the user's learning outcomes. Step 5: The Education Management Department provides education and management services for companies and local governments based on the rewards awarded by the Reward Department. For example, companies can use the platform to improve their employees' skills and manage their learning progress and results. Local governments can also provide educational programs for their citizens and manage their learning outcomes. The platform provides customized educational plans for companies and local governments, enabling efficient education and management.

[0063] (Example 2) The Study to Earn platform according to an embodiment of the present invention is a system that uses generative AI to provide appropriate compensation to individuals who provide useful know-how. The more experience and skills an individual acquires, the more rewards (tokens) they receive. This system manages learning trajectories on a blockchain and incorporates AI that recommends optimal information and learning steps for each individual. AI also manages content. Furthermore, the platform aims to monetize as a human resources training and management platform for companies and local governments. For example, a user registers on the platform and sets the field of study and goals they wish to study. Next, the AI ​​recommends optimal learning steps and information based on the user's settings. For example, a user who wants to learn programming is provided with a sequential guide from basic to advanced level, and the next step is recommended based on progress. Furthermore, the user's learning content and results are recorded on the blockchain, preventing tampering. Furthermore, a system allows users to earn rewards (tokens) by sharing what they have learned with other users. For example, if a user provides their learning know-how as an article or video and other users find it useful, they are awarded tokens. These tokens can be used for services within the platform or converted into cash. For companies and local governments, the platform can be provided as a platform for streamlining the education and management of employees and citizens. For example, companies can use the platform to improve their employees' skills and manage their learning progress and results. Local governments can also provide educational programs for their citizens and manage their learning results. In this way, the present invention provides a "Study to Earn" platform that promotes individual learning and earns rewards, and can also be monetized as a human resource education and management platform for companies and local governments. As a result, the Study to Earn platform promotes individual learning and earns rewards. It can also be monetized as a human resource education and management platform for companies and local governments.

[0064] The Study to Earn platform according to the embodiment includes a management unit, a recommendation unit, a content management unit, a reward unit, and an education management unit. The management unit manages a learning trajectory. The learning trajectory includes, but is not limited to, a learning history, learning outcomes, and learning time. For example, the management unit stores the learning history in a database and evaluates the learning outcomes. The management unit can also record learning time and manage learning progress. For example, the management unit displays the learning history in chronological order to visualize learning progress. The management unit can also evaluate learning outcomes and measure learning effectiveness. The recommendation unit recommends appropriate information and learning steps to individuals based on the learning trajectory managed by the management unit. For example, the recommendation unit recommends optimal learning steps based on the user's learning history and goals. The recommendation unit can also provide appropriate information based on the user's learning style and interests. For example, the recommendation unit analyzes the user's learning history and recommends the next learning step. The recommendation unit can also provide related information based on the user's interests. The content management unit manages content based on the information and learning steps recommended by the recommendation unit. For example, the content management unit classifies learning content and organizes it into appropriate categories. The content management unit can also evaluate the quality of the learning content and improve it as necessary. For example, the content management unit regularly updates the learning content to provide the latest information. The content management unit can also improve the learning content based on user feedback. The reward unit grants rewards based on the content managed by the content management unit. For example, the reward unit grants rewards when a user shares what they have learned with other users. The reward unit can also grant rewards based on the user's learning outcomes. For example, the reward unit provides what a user has learned as an article or video and grants tokens if it is evaluated as useful by other users. The reward unit can also grant points or badges based on the user's learning outcomes. The education management unit manages education and training for companies and local governments based on the rewards granted by the reward unit.For example, the education management department allows companies to use the platform to improve the skills of their employees and manage the learning progress and results of employees. The education management department can also allow local governments to provide educational programs for their citizens and manage their learning results. For example, the education management department provides customized educational plans for companies and local governments and performs efficient education and management. This allows the Study to Earn platform according to the embodiment to promote individual learning and earn rewards. It can also be monetized as a human resource training and management platform for companies and local governments.

[0065] The management unit can manage the learning trajectory on a blockchain. Examples of blockchains include, but are not limited to, public blockchains, private blockchains, and consortium blockchains. The management unit can manage the learning trajectory using, for example, a public blockchain. A public blockchain is a decentralized network that anyone can participate in, making data tampering difficult. The management unit can also manage the learning trajectory using a private blockchain. A private blockchain is a decentralized network used only within a specific organization or group, allowing for access control. The management unit can also manage the learning trajectory using a consortium blockchain. A consortium blockchain is a decentralized network jointly operated by multiple organizations, allowing for both data sharing and security. By managing the learning trajectory on a blockchain, tampering is prevented. Some or all of the above-described processing in the management unit can be performed using, for example, AI, or without AI. For example, when recording learning trajectory data on the blockchain, the management unit can use AI to verify the integrity of the data and detect tampering.

[0066] The recommendation unit can recommend appropriate learning steps and information based on the user's settings. The recommendation unit recommends optimal learning steps based on, for example, the user's learning goals and areas of interest. For example, if a user wants to learn programming, the recommendation unit can provide sequential steps from basic to advanced and recommend the next step based on the user's progress. The recommendation unit can also provide appropriate information based on the user's learning style. For example, the recommendation unit can recommend video content to a user who prefers visual learning, and text content to a user who prefers reading comprehension. The recommendation unit can also analyze the user's learning history and recommend the next step to learn. For example, the recommendation unit can recommend the next step to learn based on the user's past learning history. The recommendation unit can also provide related information based on the user's interests. For example, the recommendation unit can provide information related to topics in which the user has shown interest. This enables personalized learning by recommending optimal learning steps and information based on the user's settings. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's learning history and interests into the AI, which can then recommend the most appropriate learning steps and information.

[0067] The content management unit can manage content based on recommended information and learning steps. The content management unit, for example, classifies learning content and organizes it into appropriate categories. For example, the content management unit can categorize learning content by topic, difficulty level, and format to enable easy user access. The content management unit can also evaluate the quality of the learning content and improve it as necessary. For example, the content management unit can evaluate the quality of the learning content based on user feedback and identify areas for improvement. The content management unit can also periodically update the learning content to provide the latest information. For example, the content management unit can adjust the update frequency of the learning content according to the user's learning progress. This improves the quality of learning by managing content based on recommended information and learning steps. Some or all of the above-described processing in the content management unit may be performed using, or without, AI. For example, the content management unit can input the classification and quality evaluation of the learning content into AI, which can then organize the content into the most appropriate category and evaluate its quality.

[0068] The reward unit can reward a user for sharing what they have learned with other users. For example, if a user provides their learning know-how as an article or video and it is evaluated as useful by other users, the reward unit can award tokens. The reward unit can also award rewards based on the user's learning outcomes. For example, the reward unit can award points or badges when a user achieves a specific learning goal. The reward unit can also award rewards when a user shares what they have learned with other users. For example, the reward unit can award tokens when a user shares their learning know-how and it is evaluated as useful by other users. This promotes the sharing of learning by rewarding users for sharing what they have learned with other users. Some or all of the above-described processing in the reward unit can be performed, for example, using AI, or can be performed without AI. For example, the reward unit can input the user's learning outcomes and shared content into AI, which can then award rewards.

[0069] The education management unit can provide education and management for employees and citizens for companies and local governments. For example, companies can use the platform to improve their employees' skills and manage their learning progress and results. The education management unit can also provide educational programs for citizens and manage their learning results for local governments. For example, the education management unit can provide customized educational plans for companies and local governments and provide efficient education and management. This enables efficient education and management by providing education and management for employees and citizens for companies and local governments. Some or all of the above-mentioned processing in the education management unit can be performed using, for example, AI, or without AI. For example, the education management unit can input the learning progress and results of employees and citizens into AI, which can then provide and manage optimal education plans.

[0070] The management unit can analyze the user's emotions and adjust the learning trajectory recording method based on the analyzed user's emotions. For example, if the user is feeling stressed, the management unit can simplify the recording method and minimize input steps. Furthermore, if the user is relaxed, the management unit can provide detailed recording options and suggest customizable recording methods. Furthermore, if the user is in a hurry, the management unit can prioritize voice input to quickly record the learning trajectory. This reduces the user's burden by adjusting the learning trajectory recording method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the management unit can be performed using AI, or without AI. For example, the management unit can input the user's emotion data into the generation AI, which can analyze the emotion and adjust the recording method.

[0071] The management unit can update the learning trajectory in real time and dynamically manage it according to the user's progress. For example, the management unit updates the learning trajectory in real time each time the user completes a new learning step. The management unit can also display the latest learning trajectory when the user checks their progress. Furthermore, if the user interrupts learning, the management unit can update the learning trajectory based on the latest progress information when the user resumes learning. In this way, by updating the learning trajectory in real time and dynamically managing it according to the user's progress, the latest information is always reflected. Real-time updates are performed taking into account, for example, the frequency of data updates, delay time, synchronization method, etc. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without AI. For example, the management unit can input the user's learning progress data into a generation AI, which updates and manages the data in real time.

[0072] The management unit can compare the learning trajectory with the user's past learning history and evaluate the progress. For example, the management unit can compare the user's past learning history with the user's current learning trajectory and evaluate the progress. The management unit can also compare the user's current progress with learning goals previously achieved and evaluate the progress. Furthermore, the management unit can evaluate the user's current progress based on the user's past learning history and provide feedback. In this way, the learning trajectory is compared with the user's past learning history and the progress is evaluated, thereby visualizing the user's growth. Past learning history includes, for example, study time, learning outcomes, learning content, etc., but is not limited to these examples. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without AI. For example, the management unit can input the user's past learning history data into a generation AI, which can evaluate the progress and provide feedback.

[0073] The management unit can analyze the user's emotions and adjust the display method of the learning trajectory based on the analyzed user's emotions. For example, if the user is nervous, the management unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the management unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the management unit can provide a display method that focuses on the main points. This helps the user understand by adjusting the display method of the learning trajectory according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the management unit can be performed using AI, or without AI. For example, the management unit can input the user's emotion data into the generation AI, which can analyze the emotion and adjust the display method.

[0074] The management unit can share the learning trajectory with other users to promote collaborative learning. For example, the management unit allows a user to share their learning trajectory with other users and check the progress of the collaborative learning. The management unit can also receive feedback from other users when a user shares their learning trajectory. Furthermore, the management unit can allow a user to join a collaborative learning group, share their learning trajectory, and compare progress. This promotes collaborative learning by sharing the learning trajectory with other users. Collaborative learning includes, but is not limited to, the collaborative learning format, the roles of participants, and the learning process. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without AI. For example, the management unit can input learning trajectory data into a generation AI, which then shares the data with other users and manages the progress of the collaborative learning.

[0075] The management unit can synchronize the learning trajectory across different devices, allowing the user to continue learning anywhere. For example, the management unit allows a user to start learning on a smartphone and continue the learning trajectory by synchronizing it on a tablet. The management unit can also allow a user to study on a desktop and then synchronize the learning trajectory to a laptop while on the go. The management unit can also synchronize the learning trajectory of a user across different devices in real time, allowing the user to continue learning without interruption. This allows the user to continue learning anywhere by synchronizing the learning trajectory across different devices. Synchronization across different devices includes, but is not limited to, the timing of synchronization, data consistency, and the technology used. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without AI. For example, the management unit can input learning trajectory data into a generation AI, which can then synchronize data across different devices to continue learning.

[0076] The recommendation unit can analyze the user's emotions and adjust the difficulty of the recommended learning steps based on the analyzed user emotions. For example, if the user is feeling stressed, the recommendation unit can recommend learning steps with low difficulty. Furthermore, if the user is relaxed, the recommendation unit can also recommend learning steps that can be completed in a short time if the user is in a hurry. This improves the effectiveness of learning by adjusting the difficulty of the learning steps according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the recommendation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the recommendation unit can input the user's emotion data into the generation AI, which can analyze the emotion and adjust the difficulty of the learning steps.

[0077] The recommendation unit can analyze the user's learning history and recommend optimal learning steps based on past successful experiences. For example, the recommendation unit recommends the next step based on the user's past successful learning steps. The recommendation unit can also prioritize recommending areas in the user's learning history where the user has had many successful experiences. Furthermore, the recommendation unit can analyze the user's past learning history and recommend the most effective learning steps. This improves the effectiveness of learning by analyzing the user's learning history and recommending optimal learning steps based on the user's past successful experiences. Past successful experiences include, but are not limited to, successful learning steps, achieved goals, factors behind success, etc. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without AI. For example, the recommendation unit can input the user's past learning history data into a generation AI, which can analyze the user's successful experiences and recommend optimal learning steps.

[0078] The recommendation unit can recommend different types of learning content depending on the user's learning style. For example, if the user prefers visual learning, the recommendation unit can recommend video content. Furthermore, if the user prefers reading comprehension, the recommendation unit can also recommend text content. Furthermore, if the user prefers interactive learning, the recommendation unit can also recommend interactive content. By recommending different types of learning content depending on the user's learning style, the effectiveness of learning is improved. Examples of different types of learning content include, but are not limited to, video, text, interactive content, etc. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input the user's learning style data into a generation AI, which can then recommend the optimal type of learning content.

[0079] The recommendation unit can analyze the user's emotions and adjust the order of the recommended learning steps based on the analyzed user's emotions. For example, if the user is feeling stressed, the recommendation unit can recommend an order that starts with easier steps. Furthermore, if the user is relaxed, the recommendation unit can also recommend an order that prioritizes steps that can be completed in a short time if the user is in a hurry. This improves the effectiveness of learning by adjusting the order of learning steps according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the recommendation unit can be performed using, for example, AI, or without AI. For example, the recommendation unit can input the user's emotion data into the generation AI, which can analyze the emotion and adjust the order of the learning steps.

[0080] The recommendation unit can recommend region-specific learning content based on the user's geographical location information. For example, if the user is in a specific region, the recommendation unit can recommend learning content related to that region. Furthermore, if the user is traveling, the recommendation unit can recommend learning content related to the region the user is visiting. Furthermore, if the user is interested in a specific region, the recommendation unit can recommend learning content specific to that region. This improves learning effectiveness by recommending region-specific learning content taking the user's geographical location information into consideration. Examples of geographical location information include, but are not limited to, GPS data, location-based services, and types of region-specific content. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input the user's geographical location information data into a generation AI, which can then recommend region-specific learning content.

[0081] The recommendation unit can analyze the user's social media activity and recommend relevant learning content. For example, the recommendation unit can recommend learning content related to topics in which the user has shown interest on social media. The recommendation unit can also recommend learning content based on the content posted by accounts the user follows. Furthermore, the recommendation unit can recommend learning content related to content shared by the user on social media. This improves the effectiveness of learning by analyzing the user's social media activity and recommending relevant learning content. Social media activity includes, but is not limited to, the content of posts, the number of likes, the number of followers, etc. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input the user's social media activity data into a generation AI, which then recommends relevant learning content.

[0082] The content management unit can analyze the user's emotions and adjust the content display method based on the analyzed user's emotions. For example, if the user is nervous, the content management unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the content management unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the content management unit can provide a display method that focuses on the main points. This helps the user understand by adjusting the content display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 processing in the content management unit can be performed using, for example, AI, or without AI. For example, the content management unit can input the user's emotion data into the generation AI, which can analyze the emotion and adjust the display method.

[0083] The content management unit can evaluate the quality of the content and make improvements based on user feedback. For example, the content management unit can evaluate the quality of the content based on user feedback and identify areas for improvement. The content management unit can also aggregate user evaluations and propose specific improvements to improve the quality of the content. Furthermore, the content management unit can analyze user feedback and continuously improve the quality of the content. As a result, the quality of the content is improved by evaluating the quality of the content and making improvements based on user feedback. Content quality includes, but is not limited to, user feedback, content accuracy, and comprehensiveness of information. Some or all of the above-mentioned processing in the content management unit can be performed using, for example, AI, or without AI. For example, the content management unit can input user feedback data into a generation AI, which can evaluate the quality and propose improvements.

[0084] The content management unit can adjust the content update frequency according to the user's learning progress. For example, if the user's learning progress is rapid, the content management unit can increase the content update frequency. Furthermore, if the user's learning progress is slow, the content management unit can also decrease the content update frequency. Furthermore, the content management unit can update the content at the optimal timing according to the user's learning progress. By adjusting the content update frequency according to the user's learning progress, the effectiveness of learning is improved. The content update frequency includes, but is not limited to, for example, the timing of updates and criteria for selecting update content. Some or all of the above-described processing in the content management unit may be performed using, or without, AI. For example, the content management unit can input the user's learning progress data into a generation AI, which can adjust the update frequency and update the content at the optimal timing.

[0085] The content management unit can analyze the user's emotions and prioritize content based on the analyzed user's emotions. For example, if the user is feeling stressed, the content management unit can prioritize displaying relaxing content. Furthermore, if the user is feeling relaxed, the content management unit can also prioritize displaying difficult content. Furthermore, if the user is in a hurry, the content management unit can also prioritize displaying content that can be completed in a short time. This improves learning effectiveness by prioritizing content based on 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 can 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 processing in the content management unit can be performed using, for example, AI, or without AI. For example, the content management unit can input the user's emotion data into the generation AI, which then analyzes the emotions and determines the priority.

[0086] The content management unit can classify content into different categories and customize it according to the user's interests. For example, the content management unit can display related content by category based on the user's interests. Furthermore, if the user shows interest in a particular category, the content management unit can prioritize displaying content in that category. Furthermore, the content management unit can customize the display order of content according to the user's interests. This improves learning effectiveness by classifying content into different categories and customizing it according to the user's interests. Examples of different categories include, but are not limited to, by topic, difficulty level, format, etc. Some or all of the above-described processing in the content management unit may be performed using, or without, AI. For example, the content management unit can input user interest data into a generation AI, which then classifies the content and customizes the display order.

[0087] The content management unit can analyze the relevance of content and provide the user with an optimal learning path. For example, the content management unit can analyze related content based on the user's learning history and provide the optimal learning path. The content management unit can also recommend other content related to the content the user is currently learning and provide the learning path. Furthermore, the content management unit can customize the optimal learning path based on the user's interests and goals. This improves learning effectiveness by analyzing the relevance of content and providing the user with the optimal learning path. Examples of content relevance include, but are not limited to, commonalities in topics, learning flow, and related knowledge. Some or all of the above-described processing in the content management unit may be performed using, or without, AI. For example, the content management unit can input the user's learning history data into a generation AI, which then analyzes the relevance and provides the optimal learning path.

[0088] The reward unit can analyze the user's emotions and adjust the reward method based on the analyzed user's emotions. For example, if the user is feeling stressed, the reward unit can simplify the reward method and quickly grant the reward. Furthermore, if the user is relaxed, the reward unit can provide detailed reward options and suggest a customizable reward method. Furthermore, if the user is in a hurry, the reward unit can instantly grant a reward and make it available quickly. This maintains the user's motivation by adjusting the reward method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reward unit may be performed using AI, or may be performed without AI. For example, the reward unit can input the user's emotion data into the generation AI, which analyzes the emotion and adjusts the reward method.

[0089] The reward unit can dynamically change the reward awarding criteria according to the user's learning achievement. For example, if the user's learning achievement is high, the reward unit can relax the reward awarding criteria and award a larger reward. Furthermore, if the user's learning achievement is low, the reward unit can tighten the reward awarding criteria and limit the awarding of rewards. Furthermore, the reward unit can dynamically adjust the reward awarding criteria according to the user's learning achievement to provide an optimal reward. By dynamically changing the reward awarding criteria according to the user's learning achievement, an appropriate reward can be provided. The reward awarding criteria include, but are not limited to, for example, learning achievement evaluation criteria, reward type, and awarding timing. Some or all of the above-described processing in the reward unit may be performed using, for example, AI, or may be performed without AI. For example, the reward unit can input the user's learning achievement data into a generation AI, which can dynamically adjust the awarding criteria and provide an optimal reward.

[0090] The reward unit can analyze the reward history and provide an optimal reward plan to maintain the user's motivation. For example, the reward unit can analyze the user's past reward history and propose an optimal reward plan. The reward unit can also adjust the frequency and amount of rewards to maintain the user's motivation. Furthermore, the reward unit can provide an individually customized reward plan based on the user's reward history. This promotes continued learning by analyzing the reward history and providing an optimal reward plan to maintain the user's motivation. The reward history includes, but is not limited to, past reward types, timing of granting, and reward usage status. Some or all of the above-described processing in the reward unit may be performed using, for example, AI, or may be performed without AI. For example, the reward unit can input the user's reward history data into a generation AI, which analyzes the history and provides an optimal reward plan.

[0091] The reward unit can analyze the user's emotions and adjust the display method of the reward based on the analyzed user's emotions. For example, if the user is nervous, the reward unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the reward unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the reward unit can also provide a display method that focuses on the main points. This helps the user understand by adjusting the display method of the reward according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reward unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reward unit can input the user's emotion data into the generation AI, which can analyze the emotion and adjust the display method.

[0092] The reward unit can provide rewards in different formats to increase user choices. For example, the reward unit can allow the user to select a token-type reward. The reward unit can also allow the user to select a point-type reward. Furthermore, the reward unit can allow the user to select a badge-type reward. This increases user choices and improves motivation by providing rewards in different formats. Examples of different types of rewards include, but are not limited to, tokens, points, badges, etc. Some or all of the above-described processing in the reward unit can be performed using AI, or can be performed without AI. For example, the reward unit can input the user's reward selection data into a generation AI, which can then provide the optimal type of reward.

[0093] The reward unit can analyze the reward usage history and suggest the optimal way to use the reward to the user. For example, the reward unit can analyze the user's past reward usage history and suggest the optimal way to use the reward. The reward unit can also analyze how the user uses the reward and suggest the optimal way to use the reward. Furthermore, the reward unit can suggest an individually customized way to use the reward based on the user's reward usage history. This maximizes the effectiveness of the reward by analyzing the reward usage history and suggesting the optimal way to use the reward to the user. The reward usage history includes, for example, the reward usage status, frequency of use, and purpose of use, but is not limited to such examples. Some or all of the above-mentioned processing in the reward unit may be performed using, for example, AI, or may be performed without using AI. For example, the reward unit can input the user's reward usage history data into a generation AI, which analyzes the history and suggests the optimal way to use the reward.

[0094] The education management unit can analyze the user's emotions and adjust the education management method based on the analyzed user's emotions. For example, if the user is feeling stressed, the education management unit can simplify the education management method and reduce the burden. Furthermore, if the user is relaxed, the education management unit can provide a detailed education management method and suggest a customizable management method. Furthermore, if the user is in a hurry, the education management unit can quickly manage education and provide an efficient method. This improves the effectiveness of education by adjusting the education management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the education management unit can be performed using AI, for example, or without AI. For example, the education management unit can input the user's emotion data into the generation AI, which can analyze the emotion and adjust the education management method.

[0095] The education management unit can monitor the progress of education management in real time and intervene as necessary. For example, the education management unit can monitor the user's progress in education management in real time and intervene if a problem occurs. The education management unit can also intervene appropriately to promote progress if the user's progress is slow. Furthermore, the education management unit can provide real-time feedback to encourage improvement if the user's progress is not going well. This improves the effectiveness of education by monitoring the progress of education management in real time and intervening as necessary. Examples of real-time monitoring include, but are not limited to, the frequency of data updates, delay times, and synchronization methods. Some or all of the above-described processing in the education management unit may be performed using AI, or may be performed without AI. For example, the education management unit can input the user's progress data into a generation AI, which can monitor the data in real time and intervene as necessary.

[0096] The education management unit can analyze education management history and provide an optimal education plan. For example, the education management unit can analyze a user's past education management history and propose an optimal education plan. The education management unit can also identify and provide an effective education plan from the user's education management history. Furthermore, the education management unit can provide an individually customized education plan based on the user's education management history. This improves the effectiveness of education by analyzing education management history and providing an optimal education plan. The education management history includes, for example, past education plans, progress, and evaluation results, but is not limited to these examples. Some or all of the above-described processing in the education management unit may be performed using, or without, AI. For example, the education management unit can input the user's education management history data into a generation AI, which then analyzes the history and provides an optimal education plan.

[0097] The education management unit can analyze the user's emotions and determine education management priorities based on the analyzed user emotions. For example, if the user is feeling stressed, the education management unit can set a low priority to reduce the user's burden. Furthermore, if the user is relaxed, the education management unit can set a high priority to efficiently manage education. Furthermore, if the user is in a hurry, the education management unit can adjust the priority to respond quickly. This improves the effectiveness of education by determining education management priorities 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 can 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 processing in the education management unit can be performed using, for example, AI, or without AI. For example, the education management unit can input the user's emotion data into a generation AI, which then analyzes the emotions and determines the priorities.

[0098] The education management department can link the education management data with other systems to provide comprehensive educational support. For example, the education management department can link the education management data with other learning management systems to provide comprehensive educational support. The education management department can also link the education management data with a company's human resources system to provide employee educational support. Furthermore, the education management department can link the education management data with a local government's education system to provide educational support for citizens. This enables comprehensive educational support by linking the education management data with other systems. Linking with other systems includes, for example, a data sharing method, the type of system to be linked, and the purpose of the linkage, but is not limited to these examples. Some or all of the above-mentioned processing in the education management department may be performed using, or without, AI. For example, the education management department can input the education management data into a generation AI, which then links with other systems to provide comprehensive educational support.

[0099] The education management unit can visualize the results of education management and provide feedback to the user. For example, the education management unit can visualize the results of education management using graphs and charts and provide feedback to the user. The education management unit can also provide the results of education management in report format and provide detailed feedback to the user. Furthermore, the education management unit can display the results of education management in real time and provide immediate feedback to the user. This improves the effectiveness of education by visualizing the results of education management and providing feedback to the user. Examples of the results of education management include, but are not limited to, progress, evaluation results, and feedback formats. Some or all of the above-mentioned processing in the education management unit may be performed using, or without, AI. For example, the education management unit can input education management result data into a generation AI, which can visualize the results and provide feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the management unit, recommendation unit, content management unit, reward unit, and education management unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the management unit manages learning history and learning results via the control unit 46A of the smart device 14 and records learning time via the specific processing unit 290 of the data processing device 12. The recommendation unit analyzes the user's learning history and recommends optimal learning steps via the specific processing unit 290 of the data processing device 12. For example, the content management unit classifies learning content via the control unit 46A of the smart device 14 and evaluates the quality of the learning content via the specific processing unit 290 of the data processing device 12. For example, the reward unit grants rewards based on the user's learning results via the specific processing unit 290 of the data processing device 12 and manages tokens via the control unit 46A of the smart device 14. For example, the education management unit provides educational plans for companies and local governments via the specific processing unit 290 of the data processing device 12 and manages learning progress via the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the management unit, recommendation unit, content management unit, reward unit, and education management unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the management unit manages learning history and learning results via the control unit 46A of the smart glasses 214 and records learning time via the specific processing unit 290 of the data processing device 12. The recommendation unit analyzes the user's learning history and recommends optimal learning steps via the specific processing unit 290 of the data processing device 12. For example, the content management unit classifies learning content via the control unit 46A of the smart glasses 214 and evaluates the quality of the learning content via the specific processing unit 290 of the data processing device 12. For example, the reward unit grants rewards based on the user's learning results via the specific processing unit 290 of the data processing device 12 and manages tokens via the control unit 46A of the smart glasses 214. For example, the education management unit provides educational plans for companies and local governments via the specific processing unit 290 of the data processing device 12 and manages learning progress via the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the management unit, recommendation unit, content management unit, reward unit, and education management unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the management unit manages the user's learning history and learning results via the control unit 46A of the headset terminal 314 and records the learning time via the specific processing unit 290 of the data processing device 12. The recommendation unit analyzes the user's learning history and recommends optimal learning steps via the specific processing unit 290 of the data processing device 12. For example, the content management unit classifies learning content via the control unit 46A of the headset terminal 314 and evaluates the quality of the learning content via the specific processing unit 290 of the data processing device 12. For example, the reward unit grants rewards based on the user's learning results via the specific processing unit 290 of the data processing device 12 and manages tokens via the control unit 46A of the headset terminal 314. For example, the education management unit provides educational plans for companies and local governments via the specific processing unit 290 of the data processing device 12 and manages learning progress via the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the management unit, recommendation unit, content management unit, reward unit, and education management unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the management unit manages the user's learning history and learning results via the control unit 46A of the robot 414 and records the learning time via the specific processing unit 290 of the data processing device 12. The recommendation unit analyzes the user's learning history and recommends optimal learning steps via the specific processing unit 290 of the data processing device 12. For example, the content management unit classifies learning content via the control unit 46A of the robot 414 and evaluates the quality of the learning content via the specific processing unit 290 of the data processing device 12. For example, the reward unit grants rewards based on the user's learning results via the specific processing unit 290 of the data processing device 12 and manages tokens via the control unit 46A of the robot 414. For example, the education management unit provides educational plans for companies and local governments via the specific processing unit 290 of the data processing device 12 and manages learning progress via the control unit 46A of the robot 414.

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

[0101] The management unit can analyze the user's learning history and propose an optimal learning schedule to maximize the effectiveness of learning. For example, the management unit can identify the time periods in which the user was most effective in studying in the past and recommend studying during those times. The management unit can also propose appropriate break times based on the user's learning pace. Furthermore, the management unit can predict the user's learning progress based on the user's learning history and provide a schedule for achieving goals. This maximizes the effectiveness of the user's learning.

[0102] The recommendation unit can provide incentives to maintain the user's motivation to study based on the user's learning history and current learning situation. For example, the recommendation unit can provide a special reward when the user achieves a certain learning goal. The recommendation unit can also suggest challenges and competitions to encourage the user to continue studying. Furthermore, the recommendation unit can provide gradual rewards according to the user's learning progress based on the user's learning history. This helps maintain the user's motivation to study.

[0103] The content management unit can personalize learning content based on the user's learning history and feedback. For example, the content management unit can analyze the characteristics of content that the user has previously rated highly and recommend similar content. The content management unit can also adjust the format of content according to the user's learning style. Furthermore, the content management unit can identify areas for improvement in content and improve its quality based on user feedback. This allows the system to provide users with the most suitable learning content.

[0104] The reward unit can optimize the timing of reward provision based on the user's learning history and current learning situation. For example, the reward unit provides rewards at appropriate times to maintain the user's motivation to learn. The reward unit can also provide rewards in stages according to the user's learning progress. Furthermore, the reward unit can adjust the timing of reward provision based on the user's learning history to maximize the effectiveness of learning. This maintains the user's motivation to learn.

[0105] The education management unit can provide an individually customized education plan based on the user's learning history and current learning situation. For example, the education management unit can analyze the user's effective learning methods in the past and propose an education plan that incorporates those methods. The education management unit can also provide optimal learning steps based on the user's learning goals. Furthermore, the education management unit can predict the user's learning progress based on the user's learning history and provide a plan for achieving the goals. This maximizes the user's learning effectiveness.

[0106] The management unit can analyze the user's emotions and evaluate the learning progress based on the analyzed user emotions. For example, if the user is feeling stressed, the management unit can evaluate the learning progress more gently to reduce the burden. Alternatively, if the user is relaxed, the management unit can evaluate the learning progress more strictly to maximize the learning effect. Furthermore, if the user is in a hurry, the management unit can quickly evaluate the learning progress and suggest the next step. In this way, the learning progress is evaluated according to the user's emotions.

[0107] The recommendation unit can analyze the user's emotions and adjust the format of the learning content based on the analyzed user's emotions. For example, if the user is feeling stressed, the recommendation unit can recommend visually relaxing content. If the user is feeling relaxed, the recommendation unit can also recommend content containing detailed information. Furthermore, if the user is in a hurry, the recommendation unit can also recommend content that can be understood in a short time. In this way, the format of the learning content is provided according to the user's emotions.

[0108] The content management unit can analyze the user's emotions and adjust the content update frequency based on the analyzed user's emotions. For example, if the user is feeling stressed, the content management unit can set the update frequency low to reduce the burden on the user. Also, if the user is relaxed, the content management unit can set the update frequency high to provide the latest information. Furthermore, if the user is in a hurry, the content management unit can quickly update the content to provide the necessary information. In this way, the content update frequency is adjusted according to the user's emotions.

[0109] The reward unit can analyze the user's emotions and adjust the type of reward based on the analyzed user's emotions. For example, if the user is feeling stressed, the reward unit can provide a relaxing reward. Also, if the user is relaxed, the reward unit can provide a challenging reward. Furthermore, if the user is in a hurry, the reward unit can provide a reward that can be used quickly. In this way, the type of reward is provided according to the user's emotions.

[0110] The education management unit can analyze the user's emotions and adjust the flexibility of the education plan based on the analyzed user's emotions. For example, if the user is feeling stressed, the education management unit can provide a flexible plan to reduce the burden. Also, if the user is relaxed, the education management unit can provide a detailed plan to maximize the learning effect. Furthermore, if the user is in a hurry, the education management unit can provide a plan that can be quickly adapted. This provides flexibility in the education plan according to the user's emotions.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The management department manages the learning trajectory. The learning trajectory includes learning history, learning outcomes, and learning time. The management department saves the learning history in a database and evaluates learning outcomes. It can also record learning time and manage learning progress. For example, it can display learning history in chronological order to visualize learning progress. It can also evaluate learning outcomes and measure the effectiveness of learning. Step 2: The recommendation unit recommends appropriate information and learning steps to individuals based on the learning trajectory managed by the management unit. For example, it recommends optimal learning steps based on the user's learning history and goals. It can also provide appropriate information based on the user's learning style and interests. It analyzes the user's learning history and recommends the next learning step. It can also provide related information based on the user's interests. Step 3: The content management unit manages the content based on the information and learning steps recommended by the recommendation unit. For example, it classifies the learning content and organizes it into appropriate categories. It can also evaluate the quality of the learning content and improve it if necessary. It regularly updates the learning content to provide the latest information. It can also improve the learning content based on user feedback. Step 4: The reward unit awards rewards based on the content managed by the content management unit. For example, a user can be rewarded by sharing what they have learned with other users. Rewards can also be awarded based on the user's learning outcomes. If a user provides what they have learned as an article or video and it is evaluated as useful by other users, tokens are awarded. Points and badges can also be awarded based on the user's learning outcomes. Step 5: The Education Management Department provides education and management services for companies and local governments based on the rewards awarded by the Reward Department. For example, companies can use the platform to improve their employees' skills and manage their learning progress and results. Local governments can also provide educational programs for their citizens and manage their learning outcomes. The platform provides customized educational plans for companies and local governments, enabling efficient education and management.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

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

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0118] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

[0144] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0176] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0177] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0178] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0184] [Explanation of symbols]

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

Claims

1. The administrative department manages the learning trajectory, a recommendation unit that recommends appropriate information and learning steps to an individual based on the learning trajectory managed by the management unit; a content management unit that manages content based on the information and learning steps recommended by the recommendation unit; a reward unit that provides rewards based on the content managed by the content management unit; and an education management department that provides education and management for companies and local governments based on the remuneration granted by the remuneration department. A system characterized by:

2. The management unit Managing learning trajectories on the blockchain 2. The system of claim 1.

3. The recommendation unit Recommend appropriate learning steps and information based on user preferences 2. The system of claim 1.

4. The content management unit Manage content based on recommended information and learning steps 2. The system of claim 1.

5. The reward unit: Reward users for sharing what they learn with others 2. The system of claim 1.

6. The education management department Educating and managing employees and citizens for companies and local governments 2. The system of claim 1.

7. The management unit Analyze user emotions and adjust the way learning trajectories are recorded based on the analyzed user emotions.

2. The system of claim 1.

8. The management unit Real-time updates of learning trajectories are dynamically managed according to the user's progress.

2. The system of claim 1.

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