system

Abstract

The system according to the embodiment aims to provide a personalized educational curriculum based on the user's goals and interests. [Solution] A system according to an embodiment includes a reception unit, a generation unit, and a distribution unit. The reception unit inputs a user's goals and interests. The generation unit analyzes the information input by the reception unit and creates an educational curriculum. The distribution unit distributes webinar-style videos based on the educational curriculum created by the generation 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] Conventional techniques have not adequately addressed providing a personalized educational curriculum based on a user's goals and interests, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a personalized educational curriculum based on the user's goals and interests. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a distribution unit. The reception unit inputs a user's goals and interests. The generation unit analyzes the information input by the reception unit and creates an educational curriculum. The distribution unit distributes webinar-style videos based on the educational curriculum created by the generation unit. [Effects of the Invention]

[0007] An embodiment of the system can provide a personalized educational curriculum based on a user's goals and interests. [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) A personal webinar system according to an embodiment of the present invention provides an optimal educational curriculum based on a user's goals and interests and delivers webinar-style videos. In the personal webinar system, a user inputs their goals and interests, a generation AI analyzes the information, creates an optimal educational curriculum, and delivers webinar-style videos based on the generated curriculum. For example, a user inputs their goals and interests. The user inputs information such as the skills they want to acquire and the career they are aiming for. The generation AI then analyzes the input information and creates an optimal educational curriculum for the user to become the person they want to be. Webinar-style videos are delivered based on the generated educational curriculum. The user can progress through the learning process at their own pace and can ask questions and receive feedback as needed. This allows the personal webinar system to efficiently learn and acquire the skills and knowledge needed to become the person they want to be. This allows the personal webinar system to provide an optimal educational curriculum for the user and support efficient learning. For example, students can acquire skills for their future careers, and working adults can acquire skills for career advancement or job change. Retired seniors can also enjoy learning to discover new hobbies and interests.

[0029] A personal webinar system according to an embodiment includes a reception unit, a generation unit, and a distribution unit. The reception unit inputs a user's goals and interests. Examples of the user's goals and interests include, but are not limited to, learning goals, career goals, and hobbies. The reception unit can input information such as the skills the user wants to acquire and the career they are aiming for. The reception unit can also transmit the information input by the user to a generation AI. The generation unit analyzes the information input by the reception unit using the generation AI to create an optimal educational curriculum. The generation AI uses, for example, text analysis and data mining techniques to propose optimal teaching materials and study plans based on the user's goals and interests. For example, the generation AI can propose programming courses from basic to advanced to a user who wants to improve their programming skills, and propose courses on the latest marketing strategies and tools to a user who wants to deepen their marketing knowledge. Some or all of the above-described processing in the generation unit is performed using a generation AI (e.g., LLM), a multimodal generation AI, or the like. The distribution unit distributes webinar-style videos based on the educational curriculum created by the generation unit. Webinar-style videos include, but are not limited to, live streaming, recorded streaming, and interactive elements. The distribution unit distributes the webinar-style videos, for example, so that the user can progress through their learning at their own pace. The distribution unit can also allow the user to ask questions or receive feedback as needed. As a result, the personal webinar system according to the embodiment provides an optimal educational curriculum based on the user's goals and interests, and distributes the webinar-style videos, allowing the user to learn efficiently.

[0030] The personal webinar system further includes a collection unit that collects data based on the user's past learning history and interests. The collection unit collects data based on the user's past learning history and interests. The learning history includes, but is not limited to, past learning courses, grades, and study time. The interests include, but are not limited to, learning fields, hobbies, and interests. The collection unit, for example, acquires from a database information about courses the user has taken in the past and their grades. The collection unit can also collect information about fields and hobbies in which the user has shown interest in the past. This allows the collection unit to provide a more accurate educational curriculum by collecting data based on the user's past learning history and interests. Some or all of the above-mentioned processing in the collection unit may be performed using AI or without AI.

[0031] The personal webinar system further includes an evaluation unit that evaluates the effectiveness of the generated curriculum and modifies it as necessary. The evaluation unit evaluates the effectiveness of the generated curriculum and modifies it as necessary. Criteria for evaluating effectiveness include, but are not limited to, learning outcomes, user satisfaction, and achievement level. For example, the evaluation unit may conduct a survey after the user completes the curriculum to evaluate the user's satisfaction. The evaluation unit may also evaluate the user's learning outcomes based on the status of test and assignment submissions. The evaluation unit may also evaluate the effectiveness of the curriculum based on the user's achievement level. For example, the evaluation unit may evaluate the extent to which the user has achieved the goals set by the user and modify the curriculum based on the results. In this way, the evaluation unit can provide the user with an optimal educational curriculum by evaluating the effectiveness of the curriculum and modifying it as necessary. Some or all of the above-described processing in the evaluation unit may be performed using AI or without AI.

[0032] The reception unit can analyze the user's past input history of goals and interests and suggest an input method. For example, the reception unit can automatically display goals and interests that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest goals and interests to be used in a specific time period based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI.

[0033] When inputting goals and interests, the reception unit can filter the input based on the user's living situation and career stage. For example, if the user is a student, the reception unit can prioritize displaying goals and interests related to academics. Furthermore, if the user is a working adult, the reception unit can also prioritize displaying goals and interests related to career advancement. Furthermore, if the user is a retired senior, the reception unit can also prioritize displaying goals and interests for discovering new hobbies and interests. This allows for the provision of a more appropriate curriculum by prioritized display of goals and interests according to the user's living situation and career stage. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI.

[0034] When inputting goals and interests, the reception unit can select an input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the goals and interests using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also provide an interface that supports keyboard input. Furthermore, if the user selects image input, the reception unit can also input the goals and interests using image recognition technology. This allows the user to smoothly input their goals and interests by providing the optimal input means according to the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI.

[0035] When inputting goals and interests, the reception unit can prioritize inputting highly relevant information based on the user's geographical location information. For example, if the user lives in a specific area, the reception unit can prioritize displaying goals and interests related to that area. Furthermore, if the user is traveling, the reception unit can prioritize displaying goals and interests related to the travel destination. Furthermore, if the user is planning to move, the reception unit can prioritize displaying goals and interests related to the user's new residence. This allows for the provision of highly relevant information based on the user's geographical location information, thereby providing a more appropriate curriculum. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI.

[0036] When a goal or interest is input, the reception unit can analyze the user's online activity and input related information. For example, the reception unit can suggest topics that the user frequently mentions on social media as goals or interests. The reception unit can also suggest related goals or interests by referring to the activities of the user's friends on social media. The reception unit can also analyze the content of the user's social media posts and suggest related goals or interests. In this way, related information can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using AI or without AI.

[0037] The reception unit can customize the input method by reflecting the user's feedback when inputting goals and interests. The reception unit can, for example, suggest the optimal input method based on feedback provided by the user in the past. The reception unit can also avoid input methods that the user has been dissatisfied with in the past and suggest an input method that provides high satisfaction. The reception unit can also analyze the user's past feedback and continuously improve the input method. In this way, a more appropriate input method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI.

[0038] When generating a curriculum, the generation unit can adjust the level of detail of the curriculum based on the user's goals. For example, if the user has almost achieved their goal, the generation unit can generate a curriculum that includes applied content. Alternatively, if the user is still far from achieving their goal, the generation unit can generate a curriculum that includes basic content. Alternatively, the generation unit can generate a curriculum that gradually increases in difficulty depending on the user's level of goal achievement. This allows for more effective learning by adjusting the level of detail of the curriculum depending on the user's level of goal achievement. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0039] The generation unit can apply different generation algorithms depending on the user's interests when generating a curriculum. For example, if the user has an interest in technology, the generation unit can apply an algorithm that generates a technology-related curriculum. Furthermore, if the user has an interest in art, the generation unit can apply an algorithm that generates an art-related curriculum. Furthermore, if the user has an interest in business, the generation unit can apply an algorithm that generates a business-related curriculum. In this way, by applying a generation algorithm according to the category of the user's interests, a more appropriate curriculum can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0040] When generating a curriculum, the generation unit can improve the accuracy of the curriculum by referring to the user's learning results. For example, the generation unit preferentially incorporates into the curriculum learning materials that the user has previously rated highly. The generation unit can also prioritize incorporating into the curriculum areas in which the user has previously struggled. The generation unit can also analyze the user's past learning results and suggest an optimal learning order. In this way, the accuracy of the curriculum can be improved by referring to the user's past learning results. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0041] When generating a curriculum, the generation unit can determine the priority of the curriculum based on the user's goals. For example, if the user wants to achieve a goal in a short period of time, the generation unit can prioritize generating a short-term intensive curriculum. Also, if the user has a long-term goal, the generation unit can also prioritize generating a curriculum that progresses in stages. The generation unit can also propose an optimal learning schedule depending on the time period for achieving the user's goal. In this way, by determining the priority of the curriculum depending on the time period for achieving the user's goal, more effective learning can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0042] The generation unit can adjust the order of the curriculum based on the user's interests when generating the curriculum. For example, the generation unit can prioritize placing topics that the user is interested in at the beginning of the curriculum. The generation unit can also progress the learning content step by step based on the relevance of the user's interests. The generation unit can also dynamically adjust the order of the curriculum in response to changes in the user's interests. This makes it possible to provide more effective learning by adjusting the order of the curriculum based on the relevance of the user's interests. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0043] When generating a curriculum, the generation unit can adjust the use of technical terminology in the curriculum according to the user's knowledge level. For example, if the user is a beginner, the generation unit generates a curriculum that avoids technical terminology and explains things in easy-to-understand language. Furthermore, if the user is an intermediate learner, the generation unit can also generate a curriculum that uses technical terminology appropriately to deepen understanding. Furthermore, if the user is an advanced learner, the generation unit can also generate a curriculum that uses a lot of technical terminology and includes advanced content. In this way, by adjusting the use of technical terminology according to the user's knowledge level, it is possible to provide a curriculum that is easier to understand. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0044] The distribution unit can adjust the content of a video based on the user's learning status when distributing the video. For example, the distribution unit adjusts the timing of distributing the next video according to the pace at which the user progresses in their learning. The distribution unit can also distribute a video containing supplementary explanations if the user is struggling with a particular topic. The distribution unit can also distribute review videos according to the user's learning progress. This allows for more effective learning by adjusting the content of the video according to the user's learning progress. Some or all of the above-described processing in the distribution unit may be performed using AI, or may be performed without using AI.

[0045] When distributing a video, the distribution unit can select a distribution method based on the user's device information. For example, if the user is using a smartphone, the distribution unit distributes a video that matches the screen size. Furthermore, if the user is using a tablet, the distribution unit can distribute a video optimized for a large screen. Furthermore, if the user is using a smartwatch, the distribution unit can distribute a concise video with high visibility. This allows for the selection of the optimal distribution method based on the user's device information, thereby providing a more comfortable learning experience. Some or all of the above-described processing in the distribution unit may be performed using AI, or may be performed without using AI.

[0046] The distribution unit can improve the content of a video distribution by reflecting user opinions. For example, the distribution unit can adjust the content of the next video based on feedback provided by the user. Furthermore, if a user is dissatisfied with a particular topic, the distribution unit can improve the explanation of that topic. Furthermore, the distribution unit can analyze user feedback and improve the overall quality of video distribution. In this way, by reflecting user feedback, higher quality video distribution can be provided. Some or all of the above-mentioned processing in the distribution unit may be performed using AI, or may be performed without using AI.

[0047] When distributing videos, the distribution unit can select a distribution method based on the user's geographical location information. For example, if the user lives in a specific area, the distribution unit can prioritize distributing videos related to that area. Furthermore, if the user is traveling, the distribution unit can prioritize distributing videos related to the user's travel destination. Furthermore, if the user is planning to move, the distribution unit can prioritize distributing videos related to the user's new residence. This allows for the selection of an optimal distribution method based on the user's geographical location information, thereby providing a more appropriate learning experience. Some or all of the above-described processing in the distribution unit may be performed using AI, or may be performed without using AI.

[0048] The distribution unit can analyze the user's online activities and distribute related videos when distributing videos. For example, the distribution unit distributes videos related to topics frequently mentioned by the user on social media. The distribution unit can also distribute related videos by referring to the activities of the user's friends on social media. The distribution unit can also analyze the content of the user's posts on social media and distribute related videos. In this way, related videos can be provided by analyzing the user's social media activities. Some or all of the above-mentioned processing in the distribution unit may be performed using AI, or may be performed without using AI.

[0049] The distribution unit can customize the distribution method by reflecting user feedback when distributing videos. For example, the distribution unit can propose an optimal distribution method based on feedback provided by the user in the past. The distribution unit can also avoid distribution methods that users have been dissatisfied with in the past and propose distribution methods that provide high satisfaction. The distribution unit can also analyze users' past feedback and continuously improve the distribution method. This makes it possible to provide a more appropriate distribution method by reflecting users' past feedback. Some or all of the above-mentioned processing in the distribution unit may be performed using AI, or may be performed without using AI.

[0050] When collecting data, the collection unit can analyze the user's learning history and select a collection method. For example, the collection unit prioritizes collection of learning materials that the user has previously rated highly. The collection unit can also focus on collecting areas in which the user has previously struggled. The collection unit can also analyze the user's past learning history and suggest the optimal data collection method. In this way, the optimal data collection method can be provided by analyzing the user's past learning history. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0051] When collecting data, the collection unit can filter the data based on the user's lifestyle and career stage. For example, if the user is a student, the collection unit can prioritize collecting data related to their academic studies. Furthermore, if the user is a working adult, the collection unit can also prioritize collecting data related to career advancement. Furthermore, if the user is a retired senior, the collection unit can also prioritize collecting data for discovering new hobbies and interests. This allows for the provision of a more appropriate curriculum by prioritizing the collection of data according to the user's lifestyle and career stage. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI.

[0052] When collecting data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, if the user lives in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, if the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, if the user is planning to move, the collection unit can prioritize collecting data related to the user's new residence. In this way, by collecting highly relevant data based on the user's geographical location information, a more appropriate curriculum can be provided. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0053] During data collection, the collection unit can analyze the user's online activities and collect related data. For example, the collection unit collects data related to topics frequently mentioned by the user on social media. The collection unit can also collect related data by referring to the activities of the user's friends on social media. The collection unit can also analyze the content of the user's posts on social media and collect related data. In this way, related data can be provided by analyzing the user's social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using AI, or may be performed without using AI.

[0054] The evaluation unit can analyze the user's learning status and adjust the evaluation criteria when evaluating the curriculum. The evaluation unit can adjust the evaluation criteria, for example, according to the pace at which the user progresses in their studies. Furthermore, if the user has difficulty with a particular topic, the evaluation unit can relax the evaluation criteria for that topic. Furthermore, the evaluation unit can gradually tighten the evaluation criteria according to the user's learning progress. This allows for more appropriate evaluation by adjusting the evaluation criteria according to the user's learning progress. Some or all of the above-described processing in the evaluation unit may be performed using AI, or may be performed without using AI.

[0055] The evaluation unit can improve the evaluation method by reflecting the user's opinions when evaluating a curriculum. For example, the evaluation unit can adjust the next evaluation method based on the feedback provided by the user. Furthermore, if the user is dissatisfied with a particular evaluation method, the evaluation unit can improve that evaluation method. Furthermore, the evaluation unit can analyze the user's feedback and improve the quality of the overall evaluation method. In this way, by reflecting the user's feedback, a more appropriate evaluation method can be provided. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI or may be performed without using AI.

[0056] The evaluation unit can adjust the evaluation criteria based on the user's geographic location information when evaluating a curriculum. For example, if the user lives in a particular area, the evaluation unit applies evaluation criteria related to that area. If the user is traveling, the evaluation unit can also apply evaluation criteria related to the user's travel destination. If the user is planning to move, the evaluation unit can also apply evaluation criteria related to the user's new residence. In this way, adjusting the evaluation criteria based on the user's geographic location information can provide a more appropriate evaluation. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, or may be performed without using AI.

[0057] The evaluation unit can analyze a user's online activities to improve the evaluation method when evaluating a curriculum. For example, the evaluation unit can provide an evaluation method related to topics frequently mentioned by the user on social media. The evaluation unit can also provide a related evaluation method by referring to the activities of the user's friends on social media. The evaluation unit can also analyze the content of the user's social media posts and provide a related evaluation method. In this way, a more appropriate evaluation method can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI or without AI.

[0058] The evaluation unit can customize the evaluation criteria by reflecting user feedback when evaluating a curriculum. For example, the evaluation unit can propose optimal evaluation criteria based on feedback provided by the user in the past. The evaluation unit can also avoid evaluation criteria that the user was dissatisfied with in the past and propose evaluation criteria that provide high satisfaction. The evaluation unit can also analyze the user's past feedback and continuously improve the evaluation criteria. In this way, more appropriate evaluation criteria can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, or may be performed without using AI.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can provide related additional information in real time based on the user's input. For example, if a user inputs that they want to learn a specific skill, the reception unit can display the latest industry trends and news related to that skill. If a user inputs a specific career goal, the reception unit can provide success stories and interview articles related to that goal. Furthermore, if a user inputs information about a hobby, the reception unit can display event and community information related to that hobby. This allows the user to obtain more specific and useful information based on the input. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI.

[0061] The collection unit can visualize the user's learning progress based on the user's learning history. For example, it can display the user's progress in courses that they have taken in the past using graphs and charts. It can also visually show the steps the user needs to take to master a specific skill. It can also display the user's progress toward goals set by the user in real time. This allows the user to grasp their learning progress at a glance, making it easier for them to stay motivated. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0062] The evaluation unit can customize the curriculum evaluation method based on the user's learning style. For example, if the user is a visual learner, an evaluation method that makes heavy use of visual aids can be provided. Alternatively, if the user is an auditory learner, an evaluation method that emphasizes audio feedback can be provided. Furthermore, if the user is a hands-on learner, evaluation can be performed through practical assignments or projects. This allows for more effective learning support by providing an optimal evaluation method that suits the user's learning style. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, or may be performed without using AI.

[0063] The reception unit can analyze the user's past input history of goals and interests and propose a personalized study plan based on the input content. For example, it can propose related new courses and learning materials based on the user's past input goals and interests. It can also propose the next content to study based on the user's progress in courses they have taken in the past. It can also propose an optimal study schedule based on the user's past study history. This makes it possible to provide a more effective study plan by utilizing the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI.

[0064] The reception unit can suggest learning priorities based on the user's living situation and career stage. For example, if the user is a student, goals and interests related to their studies can be displayed with priority. Also, if the user is a working adult, goals and interests related to career advancement can be displayed with priority. Furthermore, if the user is a retired senior, goals and interests for discovering new hobbies and interests can be displayed with priority. This makes it possible to provide an optimal learning plan according to the user's living situation and career stage. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI.

[0065] The reception unit can dynamically change the design of the interface depending on the user's input method. For example, if the user selects voice input, the system inputs goals and interests using voice recognition technology. If the user selects text input, the system can provide an interface that supports keyboard input. If the user selects image input, the system can also input goals and interests using image recognition technology. This allows the user to smoothly input goals and interests by providing an optimal interface depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI.

[0066] The reception unit can suggest region-specific learning resources based on the user's geographical location information. For example, if the user lives in a specific region, learning resources and events related to that region can be displayed preferentially. If the user is traveling, learning resources and tourist information related to the user's travel destination can be provided. Furthermore, if the user is planning to move, learning resources and community information related to the user's new residence can be provided. This makes it possible to provide more appropriate learning resources based on the user's geographical location information. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI.

[0067] The reception unit can analyze the user's online activities and suggest relevant learning resources. For example, it can suggest learning resources related to topics frequently mentioned by the user on social media. It can also suggest relevant learning resources based on the activities of the user's friends on social media. It can also analyze the content of the user's social media posts and suggest relevant learning resources. In this way, more appropriate learning resources can be provided by analyzing the user's online activities. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The reception unit inputs the user's goals and interests. The user's goals and interests include learning goals, career goals, hobbies, etc. For example, the user can input information such as what skills they want to acquire and what career they are aiming for. The reception unit then sends the information input by the user to the generation AI. Step 2: The generation unit uses generation AI to analyze the information entered by the reception unit and create an optimal educational curriculum. Using text analysis and data mining techniques, the generation AI suggests optimal learning materials and study plans based on the user's goals and interests. For example, a user who wants to improve their programming skills might be offered a programming course covering everything from basics to advanced courses, while a user who wants to deepen their marketing knowledge might be offered a course on the latest marketing strategies and tools. Step 3: The distribution unit distributes webinar-style videos based on the educational curriculum created by the generation unit. Webinar-style videos may include live broadcasts, recorded broadcasts, and interactive elements. The distribution unit distributes the webinar-style videos so that users can progress through their learning at their own pace and can receive questions and feedback as needed.

[0070] (Example 2) A personal webinar system according to an embodiment of the present invention provides an optimal educational curriculum based on a user's goals and interests and delivers webinar-style videos. In the personal webinar system, a user inputs their goals and interests, a generation AI analyzes the information, creates an optimal educational curriculum, and delivers webinar-style videos based on the generated curriculum. For example, a user inputs their goals and interests. The user inputs information such as the skills they want to acquire and the career they are aiming for. The generation AI then analyzes the input information and creates an optimal educational curriculum for the user to become the person they want to be. Webinar-style videos are delivered based on the generated educational curriculum. The user can progress through the learning process at their own pace and can ask questions and receive feedback as needed. This allows the personal webinar system to efficiently learn and acquire the skills and knowledge needed to become the person they want to be. This allows the personal webinar system to provide an optimal educational curriculum for the user and support efficient learning. For example, students can acquire skills for their future careers, and working adults can acquire skills for career advancement or job change. Retired seniors can also enjoy learning to discover new hobbies and interests.

[0071] A personal webinar system according to an embodiment includes a reception unit, a generation unit, and a distribution unit. The reception unit inputs a user's goals and interests. Examples of the user's goals and interests include, but are not limited to, learning goals, career goals, and hobbies. The reception unit can input information such as the skills the user wants to acquire and the career they are aiming for. The reception unit can also transmit the information input by the user to a generation AI. The generation unit analyzes the information input by the reception unit using the generation AI to create an optimal educational curriculum. The generation AI uses, for example, text analysis and data mining techniques to propose optimal teaching materials and study plans based on the user's goals and interests. For example, the generation AI can propose programming courses from basic to advanced to a user who wants to improve their programming skills, and propose courses on the latest marketing strategies and tools to a user who wants to deepen their marketing knowledge. Some or all of the above-described processing in the generation unit is performed using a generation AI (e.g., LLM), a multimodal generation AI, or the like. The distribution unit distributes webinar-style videos based on the educational curriculum created by the generation unit. Webinar-style videos include, but are not limited to, live streaming, recorded streaming, and interactive elements. The distribution unit distributes the webinar-style videos, for example, so that the user can progress through their learning at their own pace. The distribution unit can also allow the user to ask questions or receive feedback as needed. As a result, the personal webinar system according to the embodiment provides an optimal educational curriculum based on the user's goals and interests, and distributes the webinar-style videos, allowing the user to learn efficiently.

[0072] The personal webinar system further includes a collection unit that collects data based on the user's past learning history and interests. The collection unit collects data based on the user's past learning history and interests. The learning history includes, but is not limited to, past learning courses, grades, and study time. The interests include, but are not limited to, learning fields, hobbies, and interests. The collection unit, for example, acquires from a database information about courses the user has taken in the past and their grades. The collection unit can also collect information about fields and hobbies in which the user has shown interest in the past. This allows the collection unit to provide a more accurate educational curriculum by collecting data based on the user's past learning history and interests. Some or all of the above-mentioned processing in the collection unit may be performed using AI or without AI.

[0073] The personal webinar system further includes an evaluation unit that evaluates the effectiveness of the generated curriculum and modifies it as necessary. The evaluation unit evaluates the effectiveness of the generated curriculum and modifies it as necessary. Criteria for evaluating effectiveness include, but are not limited to, learning outcomes, user satisfaction, and achievement level. For example, the evaluation unit may conduct a survey after the user completes the curriculum to evaluate the user's satisfaction. The evaluation unit may also evaluate the user's learning outcomes based on the status of test and assignment submissions. The evaluation unit may also evaluate the effectiveness of the curriculum based on the user's achievement level. For example, the evaluation unit may evaluate the extent to which the user has achieved the goals set by the user and modify the curriculum based on the results. In this way, the evaluation unit can provide the user with an optimal educational curriculum by evaluating the effectiveness of the curriculum and modifying it as necessary. Some or all of the above-described processing in the evaluation unit may be performed using AI or without AI.

[0074] The reception unit can adjust the input method for goals and interests based on the user's emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly input goals and interests. This allows the input method to be adjusted according to the user's emotions, allowing the user to input goals and interests without stress. 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 reception unit may be performed using AI, or may be performed without AI.

[0075] The reception unit can analyze the user's past input history of goals and interests and suggest an input method. For example, the reception unit can automatically display goals and interests that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest goals and interests to be used in a specific time period based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI.

[0076] When inputting goals and interests, the reception unit can filter the input based on the user's living situation and career stage. For example, if the user is a student, the reception unit can prioritize displaying goals and interests related to academics. Furthermore, if the user is a working adult, the reception unit can also prioritize displaying goals and interests related to career advancement. Furthermore, if the user is a retired senior, the reception unit can also prioritize displaying goals and interests for discovering new hobbies and interests. This allows for the provision of a more appropriate curriculum by prioritized display of goals and interests according to the user's living situation and career stage. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI.

[0077] When inputting goals and interests, the reception unit can select an input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the goals and interests using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also provide an interface that supports keyboard input. Furthermore, if the user selects image input, the reception unit can also input the goals and interests using image recognition technology. This allows the user to smoothly input their goals and interests by providing the optimal input means according to the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI.

[0078] The reception unit can determine the priority of the goals and interests to be input based on the user's emotions. For example, if the user is excited, the reception unit can prioritize displaying challenging goals and interests. Furthermore, if the user is relaxed, the reception unit can prioritize displaying relaxing goals and interests. Furthermore, if the user is stressed, the reception unit can prioritize displaying goals and interests that are useful for stress reduction. This allows for the provision of a more appropriate curriculum by prioritizing goals and interests 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 reception unit can be performed using AI, or without AI.

[0079] When inputting goals and interests, the reception unit can prioritize inputting highly relevant information based on the user's geographical location information. For example, if the user lives in a specific area, the reception unit can prioritize displaying goals and interests related to that area. Furthermore, if the user is traveling, the reception unit can prioritize displaying goals and interests related to the travel destination. Furthermore, if the user is planning to move, the reception unit can prioritize displaying goals and interests related to the user's new residence. This allows for the provision of highly relevant information based on the user's geographical location information, thereby providing a more appropriate curriculum. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI.

[0080] When a goal or interest is input, the reception unit can analyze the user's online activity and input related information. For example, the reception unit can suggest topics that the user frequently mentions on social media as goals or interests. The reception unit can also suggest related goals or interests by referring to the activities of the user's friends on social media. The reception unit can also analyze the content of the user's social media posts and suggest related goals or interests. In this way, related information can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using AI or without AI.

[0081] The reception unit can customize the input method by reflecting the user's feedback when inputting goals and interests. The reception unit can, for example, suggest the optimal input method based on feedback provided by the user in the past. The reception unit can also avoid input methods that the user has been dissatisfied with in the past and suggest an input method that provides high satisfaction. The reception unit can also analyze the user's past feedback and continuously improve the input method. In this way, a more appropriate input method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI.

[0082] The generation unit can adjust the curriculum content based on the user's emotions. For example, if the user is relaxed, the generation unit generates a curriculum that progresses at a leisurely pace. Furthermore, if the user is in a hurry, the generation unit can generate a curriculum that allows the user to learn effectively in a short period of time. Furthermore, if the user is excited, the generation unit can generate a curriculum that includes visually stimulating content. This allows for a more appropriate learning experience by adjusting the curriculum content 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 generation unit may be performed using the generation AI, or may be performed without using the generation AI.

[0083] When generating a curriculum, the generation unit can adjust the level of detail of the curriculum based on the user's goals. For example, if the user has almost achieved their goal, the generation unit can generate a curriculum that includes applied content. Alternatively, if the user is still far from achieving their goal, the generation unit can generate a curriculum that includes basic content. Alternatively, the generation unit can generate a curriculum that gradually increases in difficulty depending on the user's level of goal achievement. This allows for more effective learning by adjusting the level of detail of the curriculum depending on the user's level of goal achievement. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0084] The generation unit can apply different generation algorithms depending on the user's interests when generating a curriculum. For example, if the user has an interest in technology, the generation unit can apply an algorithm that generates a technology-related curriculum. Furthermore, if the user has an interest in art, the generation unit can apply an algorithm that generates an art-related curriculum. Furthermore, if the user has an interest in business, the generation unit can apply an algorithm that generates a business-related curriculum. In this way, by applying a generation algorithm according to the category of the user's interests, a more appropriate curriculum can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0085] When generating a curriculum, the generation unit can improve the accuracy of the curriculum by referring to the user's learning results. For example, the generation unit preferentially incorporates into the curriculum learning materials that the user has previously rated highly. The generation unit can also prioritize incorporating into the curriculum areas in which the user has previously struggled. The generation unit can also analyze the user's past learning results and suggest an optimal learning order. In this way, the accuracy of the curriculum can be improved by referring to the user's past learning results. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0086] The generation unit can adjust the length of the curriculum based on the user's emotions. For example, if the user is in a hurry, the generation unit generates a curriculum that allows the user to learn effectively in a short period of time. Furthermore, if the user is relaxed, the generation unit can generate a longer curriculum that includes detailed explanations. Furthermore, if the user is excited, the generation unit can generate a curriculum that includes visually stimulating content. This allows the length of the curriculum to be adjusted according to the user's emotions, thereby providing a more appropriate learning experience. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI.

[0087] When generating a curriculum, the generation unit can determine the priority of the curriculum based on the user's goals. For example, if the user wants to achieve a goal in a short period of time, the generation unit can prioritize generating a short-term intensive curriculum. Also, if the user has a long-term goal, the generation unit can also prioritize generating a curriculum that progresses in stages. The generation unit can also propose an optimal learning schedule depending on the time period for achieving the user's goal. In this way, by determining the priority of the curriculum depending on the time period for achieving the user's goal, more effective learning can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0088] The generation unit can adjust the order of the curriculum based on the user's interests when generating the curriculum. For example, the generation unit can prioritize placing topics that the user is interested in at the beginning of the curriculum. The generation unit can also progress the learning content step by step based on the relevance of the user's interests. The generation unit can also dynamically adjust the order of the curriculum in response to changes in the user's interests. This makes it possible to provide more effective learning by adjusting the order of the curriculum based on the relevance of the user's interests. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0089] When generating a curriculum, the generation unit can adjust the use of technical terminology in the curriculum according to the user's knowledge level. For example, if the user is a beginner, the generation unit generates a curriculum that avoids technical terminology and explains things in easy-to-understand language. Furthermore, if the user is an intermediate learner, the generation unit can also generate a curriculum that uses technical terminology appropriately to deepen understanding. Furthermore, if the user is an advanced learner, the generation unit can also generate a curriculum that uses a lot of technical terminology and includes advanced content. In this way, by adjusting the use of technical terminology according to the user's knowledge level, it is possible to provide a curriculum that is easier to understand. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0090] The delivery unit can adjust the video delivery method based on the user's emotions. For example, if the user is relaxed, the delivery unit can deliver a video that progresses at a leisurely pace. Furthermore, if the user is in a hurry, the delivery unit can deliver a video that allows the user to learn effectively in a short period of time. Furthermore, if the user is excited, the delivery unit can deliver a video that includes visually stimulating content. This allows for a more appropriate learning experience by adjusting the video delivery method 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 delivery unit may be performed using AI, or may be performed without AI.

[0091] The distribution unit can adjust the content of a video based on the user's learning status when distributing the video. For example, the distribution unit adjusts the timing of distributing the next video according to the pace at which the user progresses in their learning. The distribution unit can also distribute a video containing supplementary explanations if the user is struggling with a particular topic. The distribution unit can also distribute review videos according to the user's learning progress. This allows for more effective learning by adjusting the content of the video according to the user's learning progress. Some or all of the above-described processing in the distribution unit may be performed using AI, or may be performed without using AI.

[0092] When distributing a video, the distribution unit can select a distribution method based on the user's device information. For example, if the user is using a smartphone, the distribution unit distributes a video that matches the screen size. Furthermore, if the user is using a tablet, the distribution unit can distribute a video optimized for a large screen. Furthermore, if the user is using a smartwatch, the distribution unit can distribute a concise video with high visibility. This allows for the selection of the optimal distribution method based on the user's device information, thereby providing a more comfortable learning experience. Some or all of the above-described processing in the distribution unit may be performed using AI, or may be performed without using AI.

[0093] The distribution unit can improve the content of a video distribution by reflecting user opinions. For example, the distribution unit can adjust the content of the next video based on feedback provided by the user. Furthermore, if a user is dissatisfied with a particular topic, the distribution unit can improve the explanation of that topic. Furthermore, the distribution unit can analyze user feedback and improve the overall quality of video distribution. In this way, by reflecting user feedback, higher quality video distribution can be provided. Some or all of the above-mentioned processing in the distribution unit may be performed using AI, or may be performed without using AI.

[0094] The delivery unit can adjust the timing of video delivery based on the user's emotions. For example, if the user is relaxed, the delivery unit can deliver a video that progresses at a leisurely pace. Furthermore, if the user is in a hurry, the delivery unit can deliver a video that allows the user to learn effectively in a short period of time. Furthermore, if the user is excited, the delivery unit can deliver a video that includes visually stimulating content. This allows for a more appropriate learning experience by adjusting the timing of video delivery 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 delivery unit may be performed using AI, or may be performed without using AI.

[0095] When distributing videos, the distribution unit can select a distribution method based on the user's geographical location information. For example, if the user lives in a specific area, the distribution unit can prioritize distributing videos related to that area. Furthermore, if the user is traveling, the distribution unit can prioritize distributing videos related to the user's travel destination. Furthermore, if the user is planning to move, the distribution unit can prioritize distributing videos related to the user's new residence. This allows for the selection of an optimal distribution method based on the user's geographical location information, thereby providing a more appropriate learning experience. Some or all of the above-described processing in the distribution unit may be performed using AI, or may be performed without using AI.

[0096] The distribution unit can analyze the user's online activities and distribute related videos when distributing videos. For example, the distribution unit distributes videos related to topics frequently mentioned by the user on social media. The distribution unit can also distribute related videos by referring to the activities of the user's friends on social media. The distribution unit can also analyze the content of the user's posts on social media and distribute related videos. In this way, related videos can be provided by analyzing the user's social media activities. Some or all of the above-mentioned processing in the distribution unit may be performed using AI, or may be performed without using AI.

[0097] The distribution unit can customize the distribution method by reflecting user feedback when distributing videos. For example, the distribution unit can propose an optimal distribution method based on feedback provided by the user in the past. The distribution unit can also avoid distribution methods that users have been dissatisfied with in the past and propose distribution methods that provide high satisfaction. The distribution unit can also analyze users' past feedback and continuously improve the distribution method. This makes it possible to provide a more appropriate distribution method by reflecting users' past feedback. Some or all of the above-mentioned processing in the distribution unit may be performed using AI, or may be performed without using AI.

[0098] The collection unit can adjust the data collection method based on the user's emotions. For example, if the user is relaxed, the collection unit can collect detailed data. If the user is in a hurry, the collection unit can also collect concise data. If the user is excited, the collection unit can provide a visually stimulating data collection method. This allows for more appropriate data to be collected by adjusting the data collection 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-mentioned processing in the collection unit can be performed using AI, or can be performed without using AI.

[0099] When collecting data, the collection unit can analyze the user's learning history and select a collection method. For example, the collection unit prioritizes collection of learning materials that the user has previously rated highly. The collection unit can also focus on collecting areas in which the user has previously struggled. The collection unit can also analyze the user's past learning history and suggest the optimal data collection method. In this way, the optimal data collection method can be provided by analyzing the user's past learning history. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0100] When collecting data, the collection unit can filter the data based on the user's lifestyle and career stage. For example, if the user is a student, the collection unit can prioritize collecting data related to their academic studies. Furthermore, if the user is a working adult, the collection unit can also prioritize collecting data related to career advancement. Furthermore, if the user is a retired senior, the collection unit can also prioritize collecting data for discovering new hobbies and interests. This allows for the provision of a more appropriate curriculum by prioritizing the collection of data according to the user's lifestyle and career stage. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI.

[0101] The collection unit can determine the priority of data to be collected based on the user's emotions. For example, if the user is excited, the collection unit can prioritize collecting challenging data. Furthermore, if the user is relaxed, the collection unit can prioritize collecting relaxing data. Furthermore, if the user is stressed, the collection unit can prioritize collecting data that helps reduce stress. Thus, by determining the priority of data according to the user's emotions, more appropriate data can be collected. Emotion estimation is realized 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 collection unit may be performed using AI, or may be performed without using AI.

[0102] When collecting data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, if the user lives in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, if the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, if the user is planning to move, the collection unit can prioritize collecting data related to the user's new residence. In this way, by collecting highly relevant data based on the user's geographical location information, a more appropriate curriculum can be provided. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0103] During data collection, the collection unit can analyze the user's online activities and collect related data. For example, the collection unit collects data related to topics frequently mentioned by the user on social media. The collection unit can also collect related data by referring to the activities of the user's friends on social media. The collection unit can also analyze the content of the user's posts on social media and collect related data. In this way, related data can be provided by analyzing the user's social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using AI, or may be performed without using AI.

[0104] The evaluation unit can adjust the curriculum evaluation method based on the user's emotions. For example, if the user is relaxed, the evaluation unit can provide an evaluation method that requests detailed feedback. If the user is in a hurry, the evaluation unit can also provide an evaluation method that requests brief feedback. If the user is excited, the evaluation unit can also provide a visually stimulating evaluation method. This allows for adjusting the evaluation method according to the user's emotions to provide a more appropriate evaluation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 evaluation unit can be performed using AI, or can be performed without using AI.

[0105] The evaluation unit can analyze the user's learning status and adjust the evaluation criteria when evaluating the curriculum. The evaluation unit can adjust the evaluation criteria, for example, according to the pace at which the user progresses in their studies. Furthermore, if the user has difficulty with a particular topic, the evaluation unit can relax the evaluation criteria for that topic. Furthermore, the evaluation unit can gradually tighten the evaluation criteria according to the user's learning progress. This allows for more appropriate evaluation by adjusting the evaluation criteria according to the user's learning progress. Some or all of the above-described processing in the evaluation unit may be performed using AI, or may be performed without using AI.

[0106] The evaluation unit can improve the evaluation method by reflecting the user's opinions when evaluating a curriculum. For example, the evaluation unit can adjust the next evaluation method based on the feedback provided by the user. Furthermore, if the user is dissatisfied with a particular evaluation method, the evaluation unit can improve that evaluation method. Furthermore, the evaluation unit can analyze the user's feedback and improve the quality of the overall evaluation method. In this way, by reflecting the user's feedback, a more appropriate evaluation method can be provided. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI or may be performed without using AI.

[0107] The evaluation unit can adjust the display method of the evaluation results based on the user's emotions. For example, if the user is relaxed, the evaluation unit can display detailed evaluation results. If the user is in a hurry, the evaluation unit can also display concise evaluation results. If the user is excited, the evaluation unit can also display visually stimulating evaluation results. This allows for adjusting the display method of the evaluation results according to the user's emotions, thereby providing more appropriate evaluation results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 evaluation unit may be performed using AI, or may be performed without using AI.

[0108] The evaluation unit can adjust the evaluation criteria based on the user's geographic location information when evaluating a curriculum. For example, if the user lives in a particular area, the evaluation unit applies evaluation criteria related to that area. If the user is traveling, the evaluation unit can also apply evaluation criteria related to the user's travel destination. If the user is planning to move, the evaluation unit can also apply evaluation criteria related to the user's new residence. In this way, adjusting the evaluation criteria based on the user's geographic location information can provide a more appropriate evaluation. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, or may be performed without using AI.

[0109] The evaluation unit can analyze a user's online activities to improve the evaluation method when evaluating a curriculum. For example, the evaluation unit can provide an evaluation method related to topics frequently mentioned by the user on social media. The evaluation unit can also provide a related evaluation method by referring to the activities of the user's friends on social media. The evaluation unit can also analyze the content of the user's social media posts and provide a related evaluation method. In this way, a more appropriate evaluation method can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI or without AI.

[0110] The evaluation unit can customize the evaluation criteria by reflecting user feedback when evaluating a curriculum. For example, the evaluation unit can propose optimal evaluation criteria based on feedback provided by the user in the past. The evaluation unit can also avoid evaluation criteria that the user was dissatisfied with in the past and propose evaluation criteria that provide high satisfaction. The evaluation unit can also analyze the user's past feedback and continuously improve the evaluation criteria. In this way, more appropriate evaluation criteria can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, or may be performed without using AI. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, distribution unit, collection unit, and evaluation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and inputs the user's goals and interests. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates an optimal educational curriculum using a generation AI. The distribution unit is realized by the control unit 46A of the smart device 14 and distributes webinar-style videos. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects data based on the user's past learning history and interests. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the effectiveness of the generated curriculum and modifies it as necessary. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, distribution unit, collection unit, and evaluation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and inputs the user's goals and interests. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates an optimal educational curriculum using a generation AI. The distribution unit is realized by the control unit 46A of the smart glasses 214 and distributes webinar-style videos. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects data based on the user's past learning history and interests. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the effectiveness of the generated curriculum and modifies it as necessary. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, distribution unit, collection unit, and evaluation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and inputs the user's goals and interests. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates an optimal educational curriculum using a generation AI. The distribution unit is realized by the control unit 46A of the headset-type terminal 314 and distributes webinar-style videos. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects data based on the user's past learning history and interests. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the effectiveness of the generated curriculum and modifies it as necessary. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, distribution unit, collection unit, and evaluation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and inputs the user's goals and interests. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates an optimal educational curriculum using a generation AI. The distribution unit is realized by the control unit 46A of the robot 414 and distributes webinar-style videos. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects data based on the user's past learning history and interests. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the effectiveness of the generated curriculum and modifies it as necessary.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] The reception unit can provide related additional information in real time based on the user's input. For example, if a user inputs that they want to learn a specific skill, the reception unit can display the latest industry trends and news related to that skill. If a user inputs a specific career goal, the reception unit can provide success stories and interview articles related to that goal. Furthermore, if a user inputs information about a hobby, the reception unit can display event and community information related to that hobby. This allows the user to obtain more specific and useful information based on the input. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI.

[0113] The collection unit can visualize the user's learning progress based on the user's learning history. For example, it can display the user's progress in courses that they have taken in the past using graphs and charts. It can also visually show the steps the user needs to take to master a specific skill. It can also display the user's progress toward goals set by the user in real time. This allows the user to grasp their learning progress at a glance, making it easier for them to stay motivated. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0114] The evaluation unit can customize the curriculum evaluation method based on the user's learning style. For example, if the user is a visual learner, an evaluation method that makes heavy use of visual aids can be provided. Alternatively, if the user is an auditory learner, an evaluation method that emphasizes audio feedback can be provided. Furthermore, if the user is a hands-on learner, evaluation can be performed through practical assignments or projects. This allows for more effective learning support by providing an optimal evaluation method that suits the user's learning style. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, or may be performed without using AI.

[0115] The reception unit can provide feedback on the input content based on the user's emotions. For example, if the user is feeling stressed, it can provide an encouraging message or relaxing content. If the user is excited, it can suggest challenging goals or new learning opportunities. Furthermore, if the user is relaxed, it can provide detailed information or additional learning resources. This allows for a more personalized learning experience by providing feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI.

[0116] The reception unit can analyze the user's past input history of goals and interests and propose a personalized study plan based on the input content. For example, it can propose related new courses and learning materials based on the user's past input goals and interests. It can also propose the next content to study based on the user's progress in courses they have taken in the past. It can also propose an optimal study schedule based on the user's past study history. This makes it possible to provide a more effective study plan by utilizing the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI.

[0117] The reception unit can suggest learning priorities based on the user's living situation and career stage. For example, if the user is a student, goals and interests related to their studies can be displayed with priority. Also, if the user is a working adult, goals and interests related to career advancement can be displayed with priority. Furthermore, if the user is a retired senior, goals and interests for discovering new hobbies and interests can be displayed with priority. This makes it possible to provide an optimal learning plan according to the user's living situation and career stage. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI.

[0118] The reception unit can dynamically change the design of the interface depending on the user's input method. For example, if the user selects voice input, the system inputs goals and interests using voice recognition technology. If the user selects text input, the system can provide an interface that supports keyboard input. If the user selects image input, the system can also input goals and interests using image recognition technology. This allows the user to smoothly input goals and interests by providing an optimal interface depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI.

[0119] The reception unit can provide real-time feedback on the input content based on the user's emotions. For example, if the user is excited, the reception unit can suggest challenging goals or new learning opportunities. If the user is relaxed, the reception unit can provide detailed information or additional learning resources. Furthermore, if the user is stressed, the reception unit can provide encouraging messages or relaxing content. This provides feedback according to the user's emotions, thereby providing a more personalized learning experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative 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 reception unit can be performed using AI, or can be performed without using AI.

[0120] The reception unit can suggest region-specific learning resources based on the user's geographical location information. For example, if the user lives in a specific region, learning resources and events related to that region can be displayed preferentially. If the user is traveling, learning resources and tourist information related to the user's travel destination can be provided. Furthermore, if the user is planning to move, learning resources and community information related to the user's new residence can be provided. This makes it possible to provide more appropriate learning resources based on the user's geographical location information. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI.

[0121] The reception unit can analyze the user's online activities and suggest relevant learning resources. For example, it can suggest learning resources related to topics frequently mentioned by the user on social media. It can also suggest relevant learning resources based on the activities of the user's friends on social media. It can also analyze the content of the user's social media posts and suggest relevant learning resources. In this way, more appropriate learning resources can be provided by analyzing the user's online activities. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI.

[0122] The processing flow of the second embodiment will be briefly explained below.

[0123] Step 1: The reception unit inputs the user's goals and interests. The user's goals and interests include learning goals, career goals, hobbies, etc. For example, the user can input information such as what skills they want to acquire and what career they are aiming for. The reception unit then sends the information input by the user to the generation AI. Step 2: The generation unit uses generation AI to analyze the information entered by the reception unit and create an optimal educational curriculum. Using text analysis and data mining techniques, the generation AI suggests optimal learning materials and study plans based on the user's goals and interests. For example, a user who wants to improve their programming skills might be offered a programming course covering everything from basics to advanced courses, while a user who wants to deepen their marketing knowledge might be offered a course on the latest marketing strategies and tools. Step 3: The distribution unit distributes webinar-style videos based on the educational curriculum created by the generation unit. Webinar-style videos may include live broadcasts, recorded broadcasts, and interactive elements. The distribution unit distributes the webinar-style videos so that users can progress through their learning at their own pace and can receive questions and feedback as needed.

[0124] 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.

[0125] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0145] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0175] 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.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. 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 expressed, and when they approach the ideal, a state of pleasure is expressed. 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] [Explanation of symbols]

[0196] 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. a reception unit for inputting the user's goals and interests; a generation unit that analyzes the information input by the reception unit and creates an educational curriculum; a distribution unit that distributes videos in a webinar format based on the educational curriculum created by the generation unit. A system characterized by:

2. It has a collection unit that collects data based on the user's past learning history and interests.

2. The system of claim 1.

3. Equipped with an evaluation section to evaluate the effectiveness of the created curriculum and make corrections 2. The system of claim 1.

4. The reception unit Adjust how you enter your goals and interests based on your emotions 2. The system of claim 1.

5. The reception unit Analyzes the user's past input history of goals and interests and suggests input methods 2. The system of claim 1.

6. The reception unit Filter goals and interests based on your life situation and career stage 2. The system of claim 1.

7. The reception unit When entering goals and interests, select the input method according to the user's input method.

2. The system of claim 1.

8. The reception unit Prioritize input goals and interests based on user emotions 2. The system of claim 1.

9. The reception unit Prioritize relevant information based on your geographic location when entering goals and interests 2. The system of claim 1.

10. The reception unit Analyze your online activity and fill in relevant information when you enter your goals and interests 2. The system of claim 1.

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