system

Abstract

The system according to this embodiment aims to support students' creative activities by having AI take over technical instruction for cultural club activities. [Solution] The system according to the embodiment comprises a reception unit, an evaluation unit, an example presentation unit, and an advice unit. The reception unit receives deliverables from students. The evaluation unit evaluates the deliverables received by the reception unit. The example presentation unit presents examples based on the evaluation results from the evaluation unit. The advice unit provides advice based on the examples presented by the example presentation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a shortage of advisors who provide technical guidance for cultural club activities, and there is a risk that the creative activities of students may be impaired.

[0005] The system according to the embodiment aims to have AI substitute for providing technical guidance for cultural club activities and support the creative activities of students.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an evaluation unit, an example presentation unit, and an advice unit. The reception unit receives deliverables from students. The evaluation unit evaluates the deliverables received by the reception unit. The example presentation unit presents examples based on the evaluation results from the evaluation unit. The advice unit provides advice based on the examples presented by the example presentation unit. [Effects of the Invention]

[0007] The system according to this embodiment can use AI to provide technical guidance for cultural club activities, thereby supporting students' creative activities. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The technical guidance system according to an embodiment of the present invention is a system in which AI is responsible for technical guidance in cultural club activities such as illustration, music composition, video production, and programming. This technical guidance system provides AI-based technical guidance to schools and club activities that lack advisors to provide technical guidance. Specifically, it consists of the following steps. First, students use an application that runs on a PC or tablet. Through this application, students can chat with the AI, submit their work, and receive technical guidance. The technical guidance system points out the differences between the work submitted by the student and similar AI-generated works as technical shortcomings. For example, in an anime-style illustration, it points out technical shortcomings such as shaky lines and incorrect shading. Next, in order to understand the student's intentions, the technical guidance system presents high-quality examples with similar styles. This allows students to confirm what kind of style they want to aim for. For example, if a student aims for a specific musical style, it presents examples of that style. Furthermore, in order to realize the target style, the technical guidance system explains, using text and diagrams, how to technically improve the shortcomings. For example, it explains specific methods for improving line shakyness. Through these three steps, technical guidance is provided, reducing the burden on teachers while broadening the scope of creative activities and fostering promising talent for the content industry. As a result, the technical guidance system can automatically evaluate students' work, present examples, and provide advice.

[0029] The technical guidance system according to this embodiment comprises a reception unit, an evaluation unit, an example presentation unit, and an advice unit. The reception unit receives deliverables from students. For example, students can submit deliverables via chat. The reception unit can also receive deliverables through an online platform. Furthermore, the reception unit can also accept physical submissions. For example, the reception unit receives deliverables submitted by students via mail. The evaluation unit evaluates the deliverables received by the reception unit. The evaluation unit uses AI to point out technical shortcomings in the deliverables. The evaluation unit points out differences between the deliverable and AI-generated works with similar styles as technical shortcomings. For example, in an anime-style illustration, it points out technical shortcomings such as shaky lines and incorrect shading. The example presentation unit presents examples based on the evaluation results from the evaluation unit. The example presentation unit uses AI to understand the student's intentions and presents high-quality examples with similar styles. The example presentation unit presents examples of a specific musical style if that style is being pursued. The advice unit provides advice based on the examples presented by the example presentation unit. For example, the advice unit uses AI to explain, in text and diagrams, how to technically improve weaknesses in order to achieve the target style. The advice unit also explains specific methods for improving line inconsistencies. As a result, the technical guidance system according to this embodiment can efficiently receive, evaluate, present examples of, and provide advice on students' work.

[0030] The reception desk receives deliverables from students. For example, students can submit their deliverables via chat. Specifically, a chatbot guides students through the process of uploading their deliverables and sends a confirmation message once submission is complete. The reception desk can also accept deliverables through an online platform. On the online platform, students log in, access a dedicated submission form, and upload their deliverables. The submission form clearly specifies file format and size restrictions, designed to ensure smooth submission for students. Furthermore, the reception desk can also accept physical submissions. For example, the reception desk accepts deliverables submitted by students via mail. Mailed deliverables are received at a dedicated reception desk, digitized, and imported into the system. This allows the reception desk to offer diverse submission methods, enabling students to submit their deliverables in the most convenient way. In addition, the reception desk manages submitted deliverables, recording the submission date and time, as well as information about the submitter. This allows for centralized monitoring of submission status and facilitates the subsequent evaluation process.

[0031] The evaluation department assesses the deliverables received by the reception department. For example, the evaluation department uses AI to identify technical shortcomings in the deliverables. Specifically, the AI ​​analyzes the submitted deliverables using image recognition and natural language processing technologies. For instance, in an anime-style illustration, the AI ​​detects technical shortcomings such as shaky lines or incorrect shading. The AI ​​identifies these shortcomings by comparing the delivered work with high-quality examples stored in a past database and extracting the differences. The AI ​​also evaluates the overall composition and balance of the deliverable, pointing out areas that need improvement. Based on the AI's evaluation results, the evaluation department provides students with specific feedback. This feedback is provided in detailed text format as well as visually easy-to-understand diagrams. This allows the evaluation department to help students clearly understand their technical shortcomings and obtain concrete guidance for improvement. Furthermore, the evaluation department continuously learns from the AI's evaluation results to improve evaluation accuracy. This enables the evaluation department to consistently provide highly accurate evaluations that reflect the latest technological trends and artistic styles.

[0032] The example presentation section presents examples based on the evaluation results from the evaluation section. For example, the example presentation section uses AI to understand the student's intentions and presents high-quality examples with similar styles. Specifically, the AI ​​analyzes the style and theme of the student's submission and searches the database for the closest high-quality example. For example, a student aiming for a specific anime-style illustration will be presented with professional examples in the same style. The example presentation section provides detailed explanations for the presented examples, describing the techniques and methods used. This allows students to learn specific techniques and methods and apply them to their own work. Furthermore, if a student aims for a specific musical style, the example presentation section will present examples accordingly. For example, a student studying classical music will be presented with scores and performance examples by famous composers. In this way, the example presentation section can provide the most suitable examples according to the style and theme the student is aiming for, maximizing the learning effect.

[0033] The Advice Department provides advice based on the examples presented by the Example Presentation Department. For example, the Advice Department uses AI to explain, in text and diagrams, how to technically improve weaknesses in order to achieve the target style. Specifically, the AI ​​compares the student's submission with the presented examples and analyzes in detail which parts need improvement and how. For example, when explaining specific methods to improve shading, it uses diagrams to explain hand movements, pen grip, and appropriate pressure application. Regarding shading, it specifically shows the position of the light source and how to create varying shades of shadow. The Advice Department provides this advice individually to each student, supporting them in learning at their own pace. Furthermore, the Advice Department can respond to student questions and feedback, providing advice in real time. This allows the Advice Department to provide concrete support to students in overcoming technical challenges and achieving their target style.

[0034] The evaluation unit can point out differences between the student's work and AI-generated works with similar styles as technical shortcomings. For example, the evaluation unit can use AI to compare the student's work with AI-generated works with similar styles and point out technical shortcomings. For example, in anime-style illustrations, the evaluation unit can point out technical shortcomings such as shaky lines or incorrect shading. In music composition, the evaluation unit can also point out technical shortcomings such as unnatural melodic flow or inconsistent harmonies. Furthermore, in video production, the evaluation unit can point out technical shortcomings such as unstable camera work or unnatural editing. By clearly pointing out technical shortcomings, the evaluation unit can support the improvement of students' skills. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the student's work into a generating AI, and the generating AI can point out technical shortcomings.

[0035] The example presentation unit can present high-quality examples in a similar style to understand the student's intentions. For example, the example presentation unit can use AI to analyze the student's intentions and present high-quality examples in a similar style. For example, if a student aims for a specific musical style, the example presentation unit can present examples of that style. In the case of illustration, if a student aims for a specific art style, the example presentation unit can present examples of that style. Furthermore, in the case of video production, if a student aims for a specific video style, the example presentation unit can present examples of that style. This makes it easier for students to confirm the style they are aiming for. Some or all of the above processing in the example presentation unit may be performed using AI, for example, or without AI. For example, the example presentation unit can input the student's intentions into a generating AI, and the generating AI can present high-quality examples in a similar style.

[0036] The advice section can explain, using text and diagrams, how to technically improve areas of weakness in order to achieve the target style. For example, the advice section can use AI to analyze a student's weaknesses and explain how to technically improve them using text and diagrams. For example, the advice section can explain specific methods for improving shaky lines. It can also explain specific methods for improving unnatural melodic flow. Furthermore, it can explain specific methods for improving unstable camera work. This makes it easier for students to understand how to technically improve. Some or all of the above processing in the advice section may be performed using AI, for example, or without AI. For example, the advice section can input a student's weaknesses into a generating AI, and the generating AI can explain how to technically improve them.

[0037] The reception desk can receive student deliverables via chat or submission. For example, students can submit their deliverables via chat. The reception desk can also receive deliverables through an online platform. Furthermore, the reception desk can also accept physical submissions. For example, the reception desk can accept deliverables submitted by students via mail. This makes it easy for students to submit their deliverables. Some or all of the above processes in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input student-submitted deliverables into a generating AI, which can then accept the deliverables.

[0038] The reception department can analyze a student's past submission history and select the optimal submission method. For example, the reception department can use AI to analyze a student's past submission history and select the optimal submission method. For example, the reception department can analyze the time slots when students frequently submitted in the past and prompt submissions during those times. The reception department can also analyze the types of deliverables students have submitted in the past and prioritize the acceptance of similar deliverables. Furthermore, the reception department can send submission reminders based on the frequency of submissions, using the student's past submission history. This enables efficient submission by selecting the optimal submission method based on the student's submission history. Some or all of the above processes in the reception department may be performed using AI, for example, or without AI. For example, the reception department can input student submission history data into a generating AI, which can then select the optimal submission method.

[0039] The reception unit can filter submitted deliverables based on the student's current projects and areas of interest. For example, the reception unit can use AI to analyze the student's current projects and areas of interest and prioritize the acceptance of highly relevant deliverables. For example, the reception unit can prioritize the acceptance of deliverables related to the project the student is currently working on. The reception unit can also filter and accept highly relevant deliverables based on the student's areas of interest. Furthermore, the reception unit can accept new deliverables that are relevant based on the themes of deliverables the student has submitted in the past. This allows for the priority acceptance of highly relevant deliverables based on the student's areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the student's project data and area of ​​interest data into a generating AI, which can then perform the filtering.

[0040] The reception department can prioritize accepting deliverables that are highly relevant, taking into account the student's geographical location information. For example, the reception department can use AI to analyze the student's geographical location information and prioritize accepting highly relevant deliverables. For example, if a student is in a specific region, the reception department can prioritize accepting deliverables related to that region. Furthermore, if a student is traveling, the reception department can prioritize accepting deliverables related to their travel destination. In addition, if a student is engaged in activities outside of school, the reception department can prioritize accepting deliverables related to those activities. This allows for the priority acceptance of highly relevant deliverables based on the student's geographical location information. Some or all of the above processing in the reception department may be performed using AI, for example, or without AI. For example, the reception department can input the student's geographical location information data into a generating AI, which can then prioritize accepting highly relevant deliverables.

[0041] The reception department can analyze students' social media activity when receiving deliverables and accept relevant deliverables. For example, the reception department can use AI to analyze students' social media activity and accept relevant deliverables. For example, the reception department can prioritize accepting deliverables related to themes shared by students on social media. The reception department can also accept deliverables related to artists and creators that students follow on social media. Furthermore, the reception department can accept deliverables related to events that students participate in on social media. This allows for the priority acceptance of highly relevant deliverables based on students' social media activity. Some or all of the above processing in the reception department may be performed using AI, for example, or without AI. For example, the reception department can input students' social media data into a generating AI, which can then accept relevant deliverables.

[0042] The evaluation unit can adjust the level of detail of the evaluation based on the importance of the deliverables during the evaluation process. For example, the evaluation unit can use AI to analyze the importance of the deliverables and adjust the level of detail of the evaluation. For example, if the deliverables are to be submitted to an important contest, the evaluation unit can use AI to perform a detailed evaluation. The evaluation unit can also use AI to perform a concise evaluation if the deliverables are for routine practice. Furthermore, if the deliverables are related to school projects, the evaluation unit can use AI to perform an evaluation with a moderate level of detail. This allows for efficient evaluation by adjusting the level of detail of the evaluation according to the importance of the deliverables. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input deliverable importance data into a generating AI, and the generating AI can adjust the level of detail of the evaluation.

[0043] The evaluation unit can apply different evaluation algorithms depending on the category of the deliverable during evaluation. For example, the evaluation unit can use AI to analyze the category of the deliverable and apply an appropriate evaluation algorithm. For example, in the case of an illustration, the evaluation unit can apply an algorithm that evaluates line shakyness and color usage. In the case of a musical composition, the evaluation unit can also apply an algorithm that evaluates melody and harmony. Furthermore, in the case of a programming work, the evaluation unit can apply an algorithm that evaluates the efficiency and readability of the code. By applying an appropriate evaluation algorithm according to the category of the deliverable, a highly accurate evaluation becomes possible. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the category data of the deliverable into a generating AI, and the generating AI can apply an appropriate evaluation algorithm.

[0044] The evaluation unit can determine the priority of evaluations based on the submission timing of deliverables during the evaluation process. For example, the evaluation unit can use AI to analyze the submission timing of deliverables and determine the evaluation priority. For example, the evaluation unit can prioritize the evaluation of deliverables with approaching deadlines. The evaluation unit can also evaluate deliverables in order of earliest submission. Furthermore, the evaluation unit can adjust the level of detail of the evaluation according to the submission timing. This enables efficient evaluation by determining the priority of evaluations based on the submission timing of deliverables. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input deliverable submission timing data into a generating AI, which can then determine the evaluation priority.

[0045] The evaluation unit can adjust the order of evaluation based on the relevance of the deliverables during the evaluation process. For example, the evaluation unit may use AI to analyze the relevance of deliverables and adjust the order of evaluation. For example, the evaluation unit may prioritize evaluating deliverables related to the student's current project. It may also prioritize evaluating deliverables related to the student's areas of interest. Furthermore, the evaluation unit may prioritize evaluating deliverables that are highly relevant to deliverables previously submitted by the student. This allows for efficient evaluation by adjusting the order of evaluation based on the relevance of deliverables. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit may input data on the relevance of deliverables into a generating AI, which can then adjust the order of evaluation.

[0046] The example presentation unit can adjust the level of detail of the examples based on the importance of the deliverables when presenting them. For example, the example presentation unit can use AI to analyze the importance of the deliverables and adjust the level of detail of the examples. For example, if the deliverable is to be submitted to an important contest, the example presentation unit will have the AI ​​present a detailed example. In the case of deliverables for daily practice, the example presentation unit can also have the AI ​​present a concise example. Furthermore, if the deliverable is related to a school project, the example presentation unit can have the AI ​​present an example with a moderate level of detail. This allows for efficient example presentation by adjusting the level of detail of the examples according to the importance of the deliverables. Some or all of the above processing in the example presentation unit may be performed using AI, for example, or without AI. For example, the example presentation unit can input the importance data of the deliverables into a generating AI, and the generating AI can adjust the level of detail of the examples.

[0047] The example presentation unit can apply different example presentation algorithms depending on the category of the deliverable when presenting examples. For example, the example presentation unit can use AI to analyze the category of the deliverable and apply an appropriate example presentation algorithm. For example, in the case of illustrations, the example presentation unit can present examples to improve line shakyness and color usage. In the case of music composition, the example presentation unit can also present examples to improve melody and harmony. Furthermore, in the case of programming, the example presentation unit can present examples to improve code efficiency and readability. By applying an appropriate example presentation algorithm according to the category of the deliverable, highly accurate example presentation becomes possible. Some or all of the above processing in the example presentation unit may be performed using AI, for example, or without AI. For example, the example presentation unit can input the category data of the deliverable into a generating AI, and the generating AI can apply an appropriate example presentation algorithm.

[0048] The example presentation unit can determine the priority of examples based on the submission timing of deliverables when presenting examples. For example, the example presentation unit can use AI to analyze the submission timing of deliverables and determine the priority of examples. For example, the example presentation unit can prioritize presenting examples related to deliverables with approaching deadlines. The example presentation unit can also present examples in order of earliest submission. Furthermore, the example presentation unit can adjust the level of detail of the examples according to the submission timing. This enables efficient example presentation by determining the priority of examples based on the submission timing of deliverables. Some or all of the above processing in the example presentation unit may be performed using AI, for example, or without using AI. For example, the example presentation unit can input deliverable submission timing data into a generating AI, and the generating AI can determine the priority of examples.

[0049] The example presentation unit can adjust the order of examples based on the relevance of the deliverables when presenting them. For example, the example presentation unit can use AI to analyze the relevance of deliverables and adjust the order of examples. For example, the example presentation unit can prioritize presenting examples related to the student's current project. It can also prioritize presenting examples related to the student's areas of interest. Furthermore, it can prioritize presenting examples that are highly relevant to deliverables previously submitted by the student. This allows for efficient example presentation by adjusting the order of examples based on the relevance of deliverables. Some or all of the above processing in the example presentation unit may be performed using AI, for example, or without AI. For example, the example presentation unit can input data on the relevance of deliverables into a generating AI, which can then adjust the order of examples.

[0050] The advice unit can adjust the level of detail of its advice based on the importance of the deliverable. For example, the advice unit can use AI to analyze the importance of the deliverable and adjust the level of detail of the advice. For example, if the deliverable is to be submitted to an important contest, the AI ​​will provide detailed advice. The advice unit can also use AI to provide concise advice for deliverables used in daily practice. Furthermore, if the deliverable is related to a school project, the AI ​​can provide advice at a moderate level of detail. This allows for efficient advice by adjusting the level of detail of the advice according to the importance of the deliverable. Some or all of the above processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input deliverable importance data into a generating AI, which can then adjust the level of detail of the advice.

[0051] The advice unit can apply different advice algorithms depending on the category of the deliverable when providing advice. For example, the advice unit can use AI to analyze the category of the deliverable and apply an appropriate advice algorithm. For example, in the case of illustrations, the advice unit can provide advice to improve line shakyness and color usage. In the case of music composition, the advice unit can also provide advice to improve melody and harmony. Furthermore, in the case of programming, the advice unit can provide advice to improve code efficiency and readability. By applying an appropriate advice algorithm according to the category of the deliverable, highly accurate advice becomes possible. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the category data of the deliverable into a generating AI, and the generating AI can apply an appropriate advice algorithm.

[0052] The advice unit can prioritize advice based on the submission timing of deliverables. For example, the advice unit can use AI to analyze the submission timing of deliverables and determine the priority of advice. For example, the advice unit can prioritize advice related to deliverables with approaching deadlines. The advice unit can also provide advice in order of earliest submission. Furthermore, the advice unit can adjust the level of detail of advice according to the submission timing. This enables efficient advice by prioritizing advice based on the submission timing of deliverables. Some or all of the above processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input deliverable submission timing data into a generating AI, which can then determine the priority of advice.

[0053] The advice unit can adjust the order of advice based on the relevance of the deliverables. For example, the advice unit can use AI to analyze the relevance of deliverables and adjust the order of advice. For example, the advice unit can prioritize advice related to the student's current project. It can also prioritize advice related to the student's areas of interest. Furthermore, it can prioritize advice that is highly relevant to deliverables the student has submitted in the past. This allows for efficient advice by adjusting the order of advice based on the relevance of deliverables. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input data on the relevance of deliverables into a generating AI, which can then adjust the order of advice.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The technical instruction system can also include a feedback section. This feedback section can collect student responses to advice received and incorporate them into future instruction. For example, it can evaluate whether students found the advice easy to understand and whether areas for improvement were clear. The feedback section can also track how well students implemented the advice and adjust the content of future instruction accordingly. Furthermore, the feedback section can assess student motivation and satisfaction, providing data to improve the quality of technical instruction. This allows the technical instruction system to improve its teaching methods based on student feedback and provide more effective technical instruction.

[0056] The technical instruction system can also include a communication section. This section can facilitate the exchange of opinions and information sharing among students and between students and teachers. For example, students can share their work with other students and provide each other with feedback. The communication section can also provide a platform for teachers to offer additional advice and supplementary explanations to students. Furthermore, the communication section can include a forum function where students can post technical questions and receive answers from other students and teachers. This allows the technical instruction system to revitalize communication among students and with teachers, thereby enhancing learning effectiveness.

[0057] The technical instruction system can also include a customization section. This customization section allows for individualized adjustment of instruction content according to each student's learning style and progress. For example, if a student has difficulty with a particular skill, the system can provide instruction that focuses on that skill. Furthermore, the customization section can adjust the pace of instruction to match the student's learning speed. Additionally, the customization section can select instruction content based on the student's interests and preferences, enhancing the enjoyment of learning. As a result, the technical instruction system can provide optimal instruction for each student, maximizing learning effectiveness.

[0058] The technical instruction system can also include a reminder function. This function can send reminders to students to ensure they adhere to deadlines and study schedules. For example, students can receive reminders when deadlines are approaching. The reminder function can also send periodic reminders based on the student's set study schedule. Furthermore, the reminder function can notify students of the next task when they complete a specific task. This allows the technical instruction system to support students in planning their learning and improve learning efficiency.

[0059] The technical instruction system can also include a progress tracking unit. This unit can track and visualize students' learning progress in detail. For example, it can display graphs and charts showing how much work students have completed and which skills they have improved. The progress tracking unit can also display the student's progress toward their set goals in real time. Furthermore, it can suggest the next tasks students should tackle based on their progress. This allows the technical instruction system to help students understand their own progress and develop effective learning plans.

[0060] The technical instruction system can also include a performance analysis unit. This unit can analyze students' technical performance in detail and identify areas for improvement. For example, it can analyze the technical elements of students' work and evaluate which parts are particularly strong and which parts have room for improvement. The performance analysis unit can also evaluate technical progress by comparing it to students' past work. Furthermore, the performance analysis unit can identify students' technical strengths and weaknesses and propose individualized instruction plans. This allows the technical instruction system to provide specific advice for improving students' technical performance.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives deliverables from students. For example, students can submit their deliverables via chat. The reception desk can also receive deliverables through an online platform. Furthermore, the reception desk can accept physical submissions. For example, the reception desk can accept deliverables submitted by students via mail. Step 2: The evaluation department evaluates the deliverables received by the reception department. The evaluation department uses AI, for example, to point out technical shortcomings in the deliverables. The evaluation department points out differences between the deliverables and AI-generated works with similar styles as technical shortcomings. For example, in anime-style illustrations, it points out technical shortcomings such as shaky lines and incorrect shading. Step 3: The example presentation section presents examples based on the evaluation results from the evaluation section. For example, the example presentation section may use AI to understand the student's intentions and present high-quality examples with similar styles. If a student is aiming for a specific musical style, the example presentation section will present examples in that style. Step 4: The advice section provides advice based on the examples presented by the example presentation section. For example, the advice section uses AI to explain, in text and diagrams, how to technically improve areas for improvement in order to achieve the target style. The advice section explains specific methods for improving line inconsistencies.

[0063] (Example of form 2) The technical guidance system according to an embodiment of the present invention is a system in which AI is responsible for technical guidance in cultural club activities such as illustration, music composition, video production, and programming. This technical guidance system provides AI-based technical guidance to schools and club activities that lack advisors to provide technical guidance. Specifically, it consists of the following steps. First, students use an application that runs on a PC or tablet. Through this application, students can chat with the AI, submit their work, and receive technical guidance. The technical guidance system points out the differences between the work submitted by the student and similar AI-generated works as technical shortcomings. For example, in an anime-style illustration, it points out technical shortcomings such as shaky lines and incorrect shading. Next, in order to understand the student's intentions, the technical guidance system presents high-quality examples with similar styles. This allows students to confirm what kind of style they want to aim for. For example, if a student aims for a specific musical style, it presents examples of that style. Furthermore, in order to realize the target style, the technical guidance system explains, using text and diagrams, how to technically improve the shortcomings. For example, it explains specific methods for improving line shakyness. Through these three steps, technical guidance is provided, reducing the burden on teachers while broadening the scope of creative activities and fostering promising talent for the content industry. As a result, the technical guidance system can automatically evaluate students' work, present examples, and provide advice.

[0064] The technical guidance system according to this embodiment comprises a reception unit, an evaluation unit, an example presentation unit, and an advice unit. The reception unit receives deliverables from students. For example, students can submit deliverables via chat. The reception unit can also receive deliverables through an online platform. Furthermore, the reception unit can also accept physical submissions. For example, the reception unit receives deliverables submitted by students via mail. The evaluation unit evaluates the deliverables received by the reception unit. The evaluation unit uses AI to point out technical shortcomings in the deliverables. The evaluation unit points out differences between the deliverable and AI-generated works with similar styles as technical shortcomings. For example, in an anime-style illustration, it points out technical shortcomings such as shaky lines and incorrect shading. The example presentation unit presents examples based on the evaluation results from the evaluation unit. The example presentation unit uses AI to understand the student's intentions and presents high-quality examples with similar styles. The example presentation unit presents examples of a specific musical style if that style is being pursued. The advice unit provides advice based on the examples presented by the example presentation unit. For example, the advice unit uses AI to explain, in text and diagrams, how to technically improve weaknesses in order to achieve the target style. The advice unit also explains specific methods for improving line inconsistencies. As a result, the technical guidance system according to this embodiment can efficiently receive, evaluate, present examples of, and provide advice on students' work.

[0065] The reception desk receives deliverables from students. For example, students can submit their deliverables via chat. Specifically, a chatbot guides students through the process of uploading their deliverables and sends a confirmation message once submission is complete. The reception desk can also accept deliverables through an online platform. On the online platform, students log in, access a dedicated submission form, and upload their deliverables. The submission form clearly specifies file format and size restrictions, designed to ensure smooth submission for students. Furthermore, the reception desk can also accept physical submissions. For example, the reception desk accepts deliverables submitted by students via mail. Mailed deliverables are received at a dedicated reception desk, digitized, and imported into the system. This allows the reception desk to offer diverse submission methods, enabling students to submit their deliverables in the most convenient way. In addition, the reception desk manages submitted deliverables, recording the submission date and time, as well as information about the submitter. This allows for centralized monitoring of submission status and facilitates the subsequent evaluation process.

[0066] The evaluation department assesses the deliverables received by the reception department. For example, the evaluation department uses AI to identify technical shortcomings in the deliverables. Specifically, the AI ​​analyzes the submitted deliverables using image recognition and natural language processing technologies. For instance, in an anime-style illustration, the AI ​​detects technical shortcomings such as shaky lines or incorrect shading. The AI ​​identifies these shortcomings by comparing the delivered work with high-quality examples stored in a past database and extracting the differences. The AI ​​also evaluates the overall composition and balance of the deliverable, pointing out areas that need improvement. Based on the AI's evaluation results, the evaluation department provides students with specific feedback. This feedback is provided in detailed text format as well as visually easy-to-understand diagrams. This allows the evaluation department to help students clearly understand their technical shortcomings and obtain concrete guidance for improvement. Furthermore, the evaluation department continuously learns from the AI's evaluation results to improve evaluation accuracy. This enables the evaluation department to consistently provide highly accurate evaluations that reflect the latest technological trends and artistic styles.

[0067] The example presentation section presents examples based on the evaluation results from the evaluation section. For example, the example presentation section uses AI to understand the student's intentions and presents high-quality examples with similar styles. Specifically, the AI ​​analyzes the style and theme of the student's submission and searches the database for the closest high-quality example. For example, a student aiming for a specific anime-style illustration will be presented with professional examples in the same style. The example presentation section provides detailed explanations for the presented examples, describing the techniques and methods used. This allows students to learn specific techniques and methods and apply them to their own work. Furthermore, if a student aims for a specific musical style, the example presentation section will present examples accordingly. For example, a student studying classical music will be presented with scores and performance examples by famous composers. In this way, the example presentation section can provide the most suitable examples according to the style and theme the student is aiming for, maximizing the learning effect.

[0068] The Advice Department provides advice based on the examples presented by the Example Presentation Department. For example, the Advice Department uses AI to explain, in text and diagrams, how to technically improve weaknesses in order to achieve the target style. Specifically, the AI ​​compares the student's submission with the presented examples and analyzes in detail which parts need improvement and how. For example, when explaining specific methods to improve shading, it uses diagrams to explain hand movements, pen grip, and appropriate pressure application. Regarding shading, it specifically shows the position of the light source and how to create varying shades of shadow. The Advice Department provides this advice individually to each student, supporting them in learning at their own pace. Furthermore, the Advice Department can respond to student questions and feedback, providing advice in real time. This allows the Advice Department to provide concrete support to students in overcoming technical challenges and achieving their target style.

[0069] The evaluation unit can point out differences between the student's work and AI-generated works with similar styles as technical shortcomings. For example, the evaluation unit can use AI to compare the student's work with AI-generated works with similar styles and point out technical shortcomings. For example, in anime-style illustrations, the evaluation unit can point out technical shortcomings such as shaky lines or incorrect shading. In music composition, the evaluation unit can also point out technical shortcomings such as unnatural melodic flow or inconsistent harmonies. Furthermore, in video production, the evaluation unit can point out technical shortcomings such as unstable camera work or unnatural editing. By clearly pointing out technical shortcomings, the evaluation unit can support the improvement of students' skills. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the student's work into a generating AI, and the generating AI can point out technical shortcomings.

[0070] The example presentation unit can present high-quality examples in a similar style to understand the student's intentions. For example, the example presentation unit can use AI to analyze the student's intentions and present high-quality examples in a similar style. For example, if a student aims for a specific musical style, the example presentation unit can present examples of that style. In the case of illustration, if a student aims for a specific art style, the example presentation unit can present examples of that style. Furthermore, in the case of video production, if a student aims for a specific video style, the example presentation unit can present examples of that style. This makes it easier for students to confirm the style they are aiming for. Some or all of the above processing in the example presentation unit may be performed using AI, for example, or without AI. For example, the example presentation unit can input the student's intentions into a generating AI, and the generating AI can present high-quality examples in a similar style.

[0071] The advice section can explain, using text and diagrams, how to technically improve areas of weakness in order to achieve the target style. For example, the advice section can use AI to analyze a student's weaknesses and explain how to technically improve them using text and diagrams. For example, the advice section can explain specific methods for improving shaky lines. It can also explain specific methods for improving unnatural melodic flow. Furthermore, it can explain specific methods for improving unstable camera work. This makes it easier for students to understand how to technically improve. Some or all of the above processing in the advice section may be performed using AI, for example, or without AI. For example, the advice section can input a student's weaknesses into a generating AI, and the generating AI can explain how to technically improve them.

[0072] The reception desk can receive student deliverables via chat or submission. For example, students can submit their deliverables via chat. The reception desk can also receive deliverables through an online platform. Furthermore, the reception desk can also accept physical submissions. For example, the reception desk can accept deliverables submitted by students via mail. This makes it easy for students to submit their deliverables. Some or all of the above processes in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input student-submitted deliverables into a generating AI, which can then accept the deliverables.

[0073] The reception desk can estimate a student's emotions and adjust the timing of submitting deliverables based on the estimated emotions. For example, the reception desk can use AI to estimate a student's emotions and adjust the timing of submitting deliverables based on those emotions. For example, if a student is stressed, the reception desk can wait for the AI ​​to find a time when the student can relax before submitting the deliverables. Also, if a student is focused, the reception desk can wait for the AI ​​to submit the deliverables immediately to take advantage of that focus. Furthermore, if a student is tired, the reception desk can wait for the AI ​​to find a time when the student has finished resting before submitting the deliverables. This allows for submitting deliverables at an appropriate time according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input student emotion data into a generative AI, which can estimate the emotions and adjust the submission timing.

[0074] The reception department can analyze a student's past submission history and select the optimal submission method. For example, the reception department can use AI to analyze a student's past submission history and select the optimal submission method. For example, the reception department can analyze the time slots when students frequently submitted in the past and prompt submissions during those times. The reception department can also analyze the types of deliverables students have submitted in the past and prioritize the acceptance of similar deliverables. Furthermore, the reception department can send submission reminders based on the frequency of submissions, using the student's past submission history. This enables efficient submission by selecting the optimal submission method based on the student's submission history. Some or all of the above processes in the reception department may be performed using AI, for example, or without AI. For example, the reception department can input student submission history data into a generating AI, which can then select the optimal submission method.

[0075] The reception unit can filter submitted deliverables based on the student's current projects and areas of interest. For example, the reception unit can use AI to analyze the student's current projects and areas of interest and prioritize the acceptance of highly relevant deliverables. For example, the reception unit can prioritize the acceptance of deliverables related to the project the student is currently working on. The reception unit can also filter and accept highly relevant deliverables based on the student's areas of interest. Furthermore, the reception unit can accept new deliverables that are relevant based on the themes of deliverables the student has submitted in the past. This allows for the priority acceptance of highly relevant deliverables based on the student's areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the student's project data and area of ​​interest data into a generating AI, which can then perform the filtering.

[0076] The reception desk can estimate a student's emotions and determine the priority of the deliverables to be received based on the estimated emotions. For example, the reception desk can use AI to estimate a student's emotions and determine the priority of the deliverables to be received based on those emotions. For example, if a student is excited, the AI ​​can immediately receive the deliverable to capitalize on that momentum. Also, if a student is calm, the AI ​​can prioritize receiving deliverables from other students. Furthermore, if a student is feeling anxious, the AI ​​can prioritize receiving that student's deliverable to provide reassurance. In this way, by determining the priority of deliverables according to the student's emotions, the deliverables can be received in the appropriate order. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input student emotional data into a generating AI, which can then estimate the emotions and determine priorities.

[0077] The reception department can prioritize accepting deliverables that are highly relevant, taking into account the student's geographical location information. For example, the reception department can use AI to analyze the student's geographical location information and prioritize accepting highly relevant deliverables. For example, if a student is in a specific region, the reception department can prioritize accepting deliverables related to that region. Furthermore, if a student is traveling, the reception department can prioritize accepting deliverables related to their travel destination. In addition, if a student is engaged in activities outside of school, the reception department can prioritize accepting deliverables related to those activities. This allows for the priority acceptance of highly relevant deliverables based on the student's geographical location information. Some or all of the above processing in the reception department may be performed using AI, for example, or without AI. For example, the reception department can input the student's geographical location information data into a generating AI, which can then prioritize accepting highly relevant deliverables.

[0078] The reception department can analyze students' social media activity when receiving deliverables and accept relevant deliverables. For example, the reception department can use AI to analyze students' social media activity and accept relevant deliverables. For example, the reception department can prioritize accepting deliverables related to themes shared by students on social media. The reception department can also accept deliverables related to artists and creators that students follow on social media. Furthermore, the reception department can accept deliverables related to events that students participate in on social media. This allows for the priority acceptance of highly relevant deliverables based on students' social media activity. Some or all of the above processing in the reception department may be performed using AI, for example, or without AI. For example, the reception department can input students' social media data into a generating AI, which can then accept relevant deliverables.

[0079] The evaluation unit can estimate a student's emotions and adjust the expression of the evaluation based on the estimated emotions. For example, the evaluation unit can use AI to estimate a student's emotions and adjust the expression of the evaluation based on those emotions. For example, if a student is nervous, the evaluation unit can use the AI ​​to provide a gentle evaluation. If a student is relaxed, the evaluation unit can also use the AI ​​to provide detailed feedback. Furthermore, if a student is confident, the evaluation unit can use the AI ​​to provide a tough evaluation to encourage further growth. This allows for evaluations to be conducted using an appropriate expression method that corresponds to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not using AI. For example, the evaluation unit can input student emotion data into a generative AI, which can estimate emotions and adjust the expression method.

[0080] The evaluation unit can adjust the level of detail of the evaluation based on the importance of the deliverables during the evaluation process. For example, the evaluation unit can use AI to analyze the importance of the deliverables and adjust the level of detail of the evaluation. For example, if the deliverables are to be submitted to an important contest, the evaluation unit can use AI to perform a detailed evaluation. The evaluation unit can also use AI to perform a concise evaluation if the deliverables are for routine practice. Furthermore, if the deliverables are related to school projects, the evaluation unit can use AI to perform an evaluation with a moderate level of detail. This allows for efficient evaluation by adjusting the level of detail of the evaluation according to the importance of the deliverables. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input deliverable importance data into a generating AI, and the generating AI can adjust the level of detail of the evaluation.

[0081] The evaluation unit can apply different evaluation algorithms depending on the category of the deliverable during evaluation. For example, the evaluation unit can use AI to analyze the category of the deliverable and apply an appropriate evaluation algorithm. For example, in the case of an illustration, the evaluation unit can apply an algorithm that evaluates line shakyness and color usage. In the case of a musical composition, the evaluation unit can also apply an algorithm that evaluates melody and harmony. Furthermore, in the case of a programming work, the evaluation unit can apply an algorithm that evaluates the efficiency and readability of the code. By applying an appropriate evaluation algorithm according to the category of the deliverable, a highly accurate evaluation becomes possible. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the category data of the deliverable into a generating AI, and the generating AI can apply an appropriate evaluation algorithm.

[0082] The evaluation unit can estimate a student's emotions and adjust the length of the evaluation based on the estimated emotions. For example, the evaluation unit can use AI to estimate a student's emotions and adjust the length of the evaluation based on those emotions. For example, if a student is tired, the evaluation unit can have the AI ​​provide a short evaluation. If a student is excited, the evaluation unit can have the AI ​​provide a detailed evaluation. Furthermore, if a student is feeling anxious, the evaluation unit can have the AI ​​provide a short evaluation in gentle words to reassure them. This allows for evaluations of appropriate length according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not using AI. For example, the evaluation unit can input student emotion data into a generative AI, which can estimate emotions and adjust the length of the evaluation.

[0083] The evaluation unit can determine the priority of evaluations based on the submission timing of deliverables during the evaluation process. For example, the evaluation unit can use AI to analyze the submission timing of deliverables and determine the evaluation priority. For example, the evaluation unit can prioritize the evaluation of deliverables with approaching deadlines. The evaluation unit can also evaluate deliverables in order of earliest submission. Furthermore, the evaluation unit can adjust the level of detail of the evaluation according to the submission timing. This enables efficient evaluation by determining the priority of evaluations based on the submission timing of deliverables. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input deliverable submission timing data into a generating AI, which can then determine the evaluation priority.

[0084] The evaluation unit can adjust the order of evaluation based on the relevance of the deliverables during the evaluation process. For example, the evaluation unit may use AI to analyze the relevance of deliverables and adjust the order of evaluation. For example, the evaluation unit may prioritize evaluating deliverables related to the student's current project. It may also prioritize evaluating deliverables related to the student's areas of interest. Furthermore, the evaluation unit may prioritize evaluating deliverables that are highly relevant to deliverables previously submitted by the student. This allows for efficient evaluation by adjusting the order of evaluation based on the relevance of deliverables. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit may input data on the relevance of deliverables into a generating AI, which can then adjust the order of evaluation.

[0085] The example presentation unit can estimate the student's emotions and adjust the method of presenting examples based on the estimated emotions. For example, the example presentation unit can use AI to estimate the student's emotions and adjust the method of presenting examples based on those emotions. For example, if the student is nervous, the AI ​​may present a simple and highly visual example. If the student is relaxed, the AI ​​may also present a more detailed example. Furthermore, if the student is excited, the AI ​​may also present a visually stimulating example. This allows examples to be presented in an appropriate manner according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the example presentation unit may be performed using AI, for example, or without using AI. For example, the example presentation unit can input student emotional data into a generating AI, which can then estimate the emotions and adjust the presentation method accordingly.

[0086] The example presentation unit can adjust the level of detail of the examples based on the importance of the deliverables when presenting them. For example, the example presentation unit can use AI to analyze the importance of the deliverables and adjust the level of detail of the examples. For example, if the deliverable is to be submitted to an important contest, the example presentation unit will have the AI ​​present a detailed example. In the case of deliverables for daily practice, the example presentation unit can also have the AI ​​present a concise example. Furthermore, if the deliverable is related to a school project, the example presentation unit can have the AI ​​present an example with a moderate level of detail. This allows for efficient example presentation by adjusting the level of detail of the examples according to the importance of the deliverables. Some or all of the above processing in the example presentation unit may be performed using AI, for example, or without AI. For example, the example presentation unit can input the importance data of the deliverables into a generating AI, and the generating AI can adjust the level of detail of the examples.

[0087] The example presentation unit can apply different example presentation algorithms depending on the category of the deliverable when presenting examples. For example, the example presentation unit can use AI to analyze the category of the deliverable and apply an appropriate example presentation algorithm. For example, in the case of illustrations, the example presentation unit can present examples to improve line shakyness and color usage. In the case of music composition, the example presentation unit can also present examples to improve melody and harmony. Furthermore, in the case of programming, the example presentation unit can present examples to improve code efficiency and readability. By applying an appropriate example presentation algorithm according to the category of the deliverable, highly accurate example presentation becomes possible. Some or all of the above processing in the example presentation unit may be performed using AI, for example, or without AI. For example, the example presentation unit can input the category data of the deliverable into a generating AI, and the generating AI can apply an appropriate example presentation algorithm.

[0088] The example presentation unit can estimate the student's emotions and adjust the length of the example based on the estimated emotions. For example, the example presentation unit can use AI to estimate the student's emotions and adjust the length of the example based on those emotions. For example, if the student is tired, the AI ​​will present a short example. If the student is excited, the AI ​​can also present a more detailed example. Furthermore, if the student is feeling anxious, the AI ​​can present a short example using gentle language to provide reassurance. This allows for the presentation of examples of appropriate length according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the example presentation unit may be performed using AI, for example, or without AI. For example, the example presentation unit can input student emotional data into a generating AI, which can then estimate the emotions and adjust the length of the example.

[0089] The example presentation unit can determine the priority of examples based on the submission timing of deliverables when presenting examples. For example, the example presentation unit can use AI to analyze the submission timing of deliverables and determine the priority of examples. For example, the example presentation unit can prioritize presenting examples related to deliverables with approaching deadlines. The example presentation unit can also present examples in order of earliest submission. Furthermore, the example presentation unit can adjust the level of detail of the examples according to the submission timing. This enables efficient example presentation by determining the priority of examples based on the submission timing of deliverables. Some or all of the above processing in the example presentation unit may be performed using AI, for example, or without using AI. For example, the example presentation unit can input deliverable submission timing data into a generating AI, and the generating AI can determine the priority of examples.

[0090] The example presentation unit can adjust the order of examples based on the relevance of the deliverables when presenting them. For example, the example presentation unit can use AI to analyze the relevance of deliverables and adjust the order of examples. For example, the example presentation unit can prioritize presenting examples related to the student's current project. It can also prioritize presenting examples related to the student's areas of interest. Furthermore, it can prioritize presenting examples that are highly relevant to deliverables previously submitted by the student. This allows for efficient example presentation by adjusting the order of examples based on the relevance of deliverables. Some or all of the above processing in the example presentation unit may be performed using AI, for example, or without AI. For example, the example presentation unit can input data on the relevance of deliverables into a generating AI, which can then adjust the order of examples.

[0091] The advice unit can estimate a student's emotions and adjust the way it expresses advice based on those emotions. For example, the advice unit can use AI to estimate a student's emotions and adjust the way it expresses advice based on those emotions. For example, if a student is nervous, the AI ​​can give advice in gentle terms. If a student is relaxed, the AI ​​can also provide detailed advice. Furthermore, if a student is confident, the AI ​​can give tough advice to encourage further growth. This allows for advice to be expressed in an appropriate way according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not using AI. For example, the advice unit can input student emotion data into a generative AI, which can estimate the emotions and adjust the way it is expressed.

[0092] The advice unit can adjust the level of detail of its advice based on the importance of the deliverable. For example, the advice unit can use AI to analyze the importance of the deliverable and adjust the level of detail of the advice. For example, if the deliverable is to be submitted to an important contest, the AI ​​will provide detailed advice. The advice unit can also use AI to provide concise advice for deliverables used in daily practice. Furthermore, if the deliverable is related to a school project, the AI ​​can provide advice at a moderate level of detail. This allows for efficient advice by adjusting the level of detail of the advice according to the importance of the deliverable. Some or all of the above processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input deliverable importance data into a generating AI, which can then adjust the level of detail of the advice.

[0093] The advice unit can apply different advice algorithms depending on the category of the deliverable when providing advice. For example, the advice unit can use AI to analyze the category of the deliverable and apply an appropriate advice algorithm. For example, in the case of illustrations, the advice unit can provide advice to improve line shakyness and color usage. In the case of music composition, the advice unit can also provide advice to improve melody and harmony. Furthermore, in the case of programming, the advice unit can provide advice to improve code efficiency and readability. By applying an appropriate advice algorithm according to the category of the deliverable, highly accurate advice becomes possible. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the category data of the deliverable into a generating AI, and the generating AI can apply an appropriate advice algorithm.

[0094] The advice unit can estimate a student's emotions and adjust the length of the advice based on the estimated emotions. For example, the advice unit can use AI to estimate a student's emotions and adjust the length of the advice based on those emotions. For example, if a student is tired, the AI ​​can provide short advice. If a student is excited, the AI ​​can also provide detailed advice. Furthermore, if a student is feeling anxious, the AI ​​can provide short advice in gentle words to reassure them. This allows for advice of an appropriate length according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not using AI. For example, the advice unit can input student emotion data into a generative AI, which can estimate the emotions and adjust the length of the advice.

[0095] The advice unit can prioritize advice based on the submission timing of deliverables. For example, the advice unit can use AI to analyze the submission timing of deliverables and determine the priority of advice. For example, the advice unit can prioritize advice related to deliverables with approaching deadlines. The advice unit can also provide advice in order of earliest submission. Furthermore, the advice unit can adjust the level of detail of advice according to the submission timing. This enables efficient advice by prioritizing advice based on the submission timing of deliverables. Some or all of the above processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input deliverable submission timing data into a generating AI, which can then determine the priority of advice.

[0096] The advice unit can adjust the order of advice based on the relevance of the deliverables. For example, the advice unit can use AI to analyze the relevance of deliverables and adjust the order of advice. For example, the advice unit can prioritize advice related to the student's current project. It can also prioritize advice related to the student's areas of interest. Furthermore, it can prioritize advice that is highly relevant to deliverables the student has submitted in the past. This allows for efficient advice by adjusting the order of advice based on the relevance of deliverables. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input data on the relevance of deliverables into a generating AI, which can then adjust the order of advice.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The technical instruction system can also include a feedback section. This feedback section can collect student responses to advice received and incorporate them into future instruction. For example, it can evaluate whether students found the advice easy to understand and whether areas for improvement were clear. The feedback section can also track how well students implemented the advice and adjust the content of future instruction accordingly. Furthermore, the feedback section can assess student motivation and satisfaction, providing data to improve the quality of technical instruction. This allows the technical instruction system to improve its teaching methods based on student feedback and provide more effective technical instruction.

[0099] The technical instruction system can also include a communication section. This section can facilitate the exchange of opinions and information sharing among students and between students and teachers. For example, students can share their work with other students and provide each other with feedback. The communication section can also provide a platform for teachers to offer additional advice and supplementary explanations to students. Furthermore, the communication section can include a forum function where students can post technical questions and receive answers from other students and teachers. This allows the technical instruction system to revitalize communication among students and with teachers, thereby enhancing learning effectiveness.

[0100] The technical instruction system can also include a motivation management section. This section provides functions to maintain and improve students' motivation to learn. For example, it allows students to set goals and visualize their progress. The motivation management section can also incorporate gamification elements by awarding badges or points for student achievements. Furthermore, it can provide encouraging messages and advice based on students' progress. This allows the technical instruction system to enhance student motivation and promote continuous learning.

[0101] The technical instruction system can also include a customization section. This customization section allows for individualized adjustment of instruction content according to each student's learning style and progress. For example, if a student has difficulty with a particular skill, the system can provide instruction that focuses on that skill. Furthermore, the customization section can adjust the pace of instruction to match the student's learning speed. Additionally, the customization section can select instruction content based on the student's interests and preferences, enhancing the enjoyment of learning. As a result, the technical instruction system can provide optimal instruction for each student, maximizing learning effectiveness.

[0102] The technical instruction system can also include a reminder function. This function can send reminders to students to ensure they adhere to deadlines and study schedules. For example, students can receive reminders when deadlines are approaching. The reminder function can also send periodic reminders based on the student's set study schedule. Furthermore, the reminder function can notify students of the next task when they complete a specific task. This allows the technical instruction system to support students in planning their learning and improve learning efficiency.

[0103] The technical instruction system can also be equipped with an emotion analysis unit. This unit can analyze students' emotions in real time and adjust instruction content and methods accordingly. For example, if a student is stressed, the emotion analysis unit can suggest relaxing instruction methods. If a student is excited, the unit can provide challenging tasks to capitalize on that energy. Furthermore, if a student is feeling down, the emotion analysis unit can send encouraging messages to restore their motivation. This allows the technical instruction system to provide flexible instruction tailored to students' emotions, thereby enhancing learning effectiveness.

[0104] The technical instruction system can also include a progress tracking unit. This unit can track and visualize students' learning progress in detail. For example, it can display graphs and charts showing how much work students have completed and which skills they have improved. The progress tracking unit can also display the student's progress toward their set goals in real time. Furthermore, it can suggest the next tasks students should tackle based on their progress. This allows the technical instruction system to help students understand their own progress and develop effective learning plans.

[0105] The technical instruction system can also include an emotional feedback unit. This unit can collect students' emotional responses to instruction and incorporate them into future instruction. For example, it can evaluate how students felt about the advice they received. The emotional feedback unit can also track whether students have positive feelings towards the instruction and adjust the teaching method accordingly. Furthermore, the emotional feedback unit can analyze students' emotional data and provide insights to improve the quality of instruction. This allows the technical instruction system to provide instruction based on students' emotions, thereby enhancing learning effectiveness.

[0106] The technical instruction system can also include a performance analysis unit. This unit can analyze students' technical performance in detail and identify areas for improvement. For example, it can analyze the technical elements of students' work and evaluate which parts are particularly strong and which parts have room for improvement. The performance analysis unit can also evaluate technical progress by comparing it to students' past work. Furthermore, the performance analysis unit can identify students' technical strengths and weaknesses and propose individualized instruction plans. This allows the technical instruction system to provide specific advice for improving students' technical performance.

[0107] The technical instruction system can also be equipped with an emotion monitoring unit. This unit can continuously monitor students' emotions and adjust instruction content and methods in real time. For example, if a student is stressed, the emotion monitoring unit can suggest instructional methods that help them relax. If a student is excited, the unit can provide challenging tasks to capitalize on that energy. Furthermore, if a student is feeling down, the unit can send encouraging messages to help them regain motivation. This allows the technical instruction system to provide flexible instruction tailored to students' emotions, thereby enhancing learning effectiveness.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The reception desk receives deliverables from students. For example, students can submit their deliverables via chat. The reception desk can also receive deliverables through an online platform. Furthermore, the reception desk can accept physical submissions. For example, the reception desk can accept deliverables submitted by students via mail. Step 2: The evaluation department evaluates the deliverables received by the reception department. The evaluation department uses AI, for example, to point out technical shortcomings in the deliverables. The evaluation department points out differences between the deliverables and AI-generated works with similar styles as technical shortcomings. For example, in anime-style illustrations, it points out technical shortcomings such as shaky lines and incorrect shading. Step 3: The example presentation section presents examples based on the evaluation results from the evaluation section. For example, the example presentation section may use AI to understand the student's intentions and present high-quality examples with similar styles. If a student is aiming for a specific musical style, the example presentation section will present examples in that style. Step 4: The advice section provides advice based on the examples presented by the example presentation section. For example, the advice section uses AI to explain, in text and diagrams, how to technically improve areas for improvement in order to achieve the target style. The advice section explains specific methods for improving line inconsistencies.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the reception unit, evaluation unit, example presentation unit, and advice unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, allowing students to submit their work via chat. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, using AI to point out technical shortcomings in the work. The example presentation unit is implemented by the specific processing unit 290 of the data processing unit 12, presenting high-quality examples in a similar style to understand the student's intentions. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12, explaining how to technically improve the shortcomings using text and diagrams. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the reception unit, evaluation unit, example presentation unit, and advice unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, allowing students to submit their work via chat. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, using AI to point out technical shortcomings in the work. The example presentation unit is implemented by the specific processing unit 290 of the data processing unit 12, presenting high-quality examples in a similar style to understand the student's intentions. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12, explaining how to technically improve the shortcomings using text and diagrams. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the reception unit, evaluation unit, example presentation unit, and advice unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, allowing students to submit their work via chat. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, which uses AI to point out technical shortcomings in the work. The example presentation unit is implemented by the specific processing unit 290 of the data processing unit 12, which presents high-quality examples in a similar style to understand the student's intentions. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12, which explains how to technically improve the shortcomings using text and diagrams. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the reception unit, evaluation unit, example presentation unit, and advice unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, allowing students to submit their work via chat. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, which uses AI to point out technical shortcomings in the work. The example presentation unit is implemented by the specific processing unit 290 of the data processing unit 12, which presents high-quality examples in a similar style to understand the student's intentions. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12, which explains how to technically improve the shortcomings using text and diagrams. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0163] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) The reception area receives student work samples, An evaluation unit that evaluates the deliverables received by the aforementioned reception unit, An example presentation unit presents examples based on the results evaluated by the evaluation unit, An advice unit provides advice based on the examples presented by the example presentation unit, and A system characterized by the following features. (Note 2) The evaluation unit, The differences between this AI-generated work and similar AI-generated works are pointed out as technical immaturities. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned example presentation unit is, To understand the students' intentions, we will present high-quality examples in a similar style. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned advice section, To achieve the desired style, I will explain, using text and diagrams, how to improve my technical weaknesses. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is We accept student work samples via chat and submission. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates students' emotions and adjusts the timing of submitting deliverables based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze students' past submission history to select the most suitable submission method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving deliverables, filtering will be performed based on the students' current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates students' emotions and prioritizes the deliverables to be accepted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving deliverables, the system prioritizes accepting deliverables that are highly relevant, taking into account the students' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving deliverables, the system analyzes students' social media activity and accepts relevant deliverables. The system described in Appendix 1, characterized by the features described herein. (Note 12) The evaluation unit, The system estimates students' emotions and adjusts the way evaluations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The evaluation unit, During evaluation, adjust the level of detail based on the importance of the deliverables. The system described in Appendix 1, characterized by the features described herein. (Note 14) The evaluation unit, During evaluation, different evaluation algorithms are applied depending on the category of the deliverable. The system described in Appendix 1, characterized by the features described herein. (Note 15) The evaluation unit, The system estimates the students' emotions and adjusts the length of the assessment based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The evaluation unit, During the evaluation process, the priority of evaluation will be determined based on the submission timing of the deliverables. The system described in Appendix 1, characterized by the features described herein. (Note 17) The evaluation unit, During evaluation, the order of evaluation will be adjusted based on the relevance of the deliverables. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned example presentation unit is, The system estimates the students' emotions and adjusts the presentation method of the examples based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned example presentation unit is, When presenting examples, adjust the level of detail in the examples based on the importance of the deliverables. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned example presentation unit is, When presenting examples, different example presentation algorithms are applied depending on the category of the deliverable. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned example presentation unit is, The system estimates the students' emotions and adjusts the length of the example based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned example presentation unit is, When presenting examples, prioritize the examples based on the submission deadline for the deliverables. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned example presentation unit is, When presenting examples, adjust the order of the examples based on the relevance of the deliverables. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned advice section, The system estimates the student's emotions and adjusts the way advice is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned advice section, When providing advice, adjust the level of detail based on the importance of the deliverable. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned advice section, When providing advice, different advice algorithms are applied depending on the category of the deliverable. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned advice section, The system estimates the student's emotions and adjusts the length of the advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned advice section, When providing advice, prioritize the advice based on the deadline for submitting deliverables. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned advice section, When providing advice, adjust the order of advice based on the relevance of the deliverables. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area receives student work samples, An evaluation unit that evaluates the deliverables received by the aforementioned reception unit, An example presentation unit presents examples based on the results evaluated by the evaluation unit, An advice unit provides advice based on the examples presented by the example presentation unit, and A system characterized by the following features.

2. The evaluation unit, The differences between this AI-generated work and similar AI-generated works are pointed out as technical immaturities. The system according to feature 1.

3. The aforementioned example presentation unit is, To understand the students' intentions, we will present high-quality examples in a similar style. The system according to feature 1.

4. The aforementioned advice section, To achieve the desired style, I will explain, using text and diagrams, how to improve my technical weaknesses. The system according to feature 1.

5. The aforementioned reception unit is We accept student work samples via chat and submission. The system according to feature 1.

6. The aforementioned reception unit is The system estimates students' emotions and adjusts the timing of submitting deliverables based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze students' past submission history to select the most suitable submission method. The system according to feature 1.

8. The aforementioned reception unit is When receiving deliverables, filtering will be performed based on the students' current projects and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is The system estimates students' emotions and prioritizes the deliverables to be accepted based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is When receiving deliverables, the system prioritizes accepting deliverables that are highly relevant, taking into account the students' geographical location. The system according to feature 1.

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