system

Abstract

The system according to the embodiment aims to generate and package optimal lesson content and assignments based on the performance data of each student. [Solution] A system according to an embodiment includes a reception unit, a collection unit, an analysis unit, a generation unit, and a packaging unit. The reception unit receives as input a specific theme or unit set by a teacher. The collection unit collects grade data for each student based on the information received by the reception unit. The analysis unit analyzes the grade data collected by the collection unit and identifies missing learning items for each student. The generation unit generates lesson content and assignments based on the learning items identified by the analysis unit. The packaging unit packages the lesson content and assignments generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to generate optimal lesson content and assignments for each student based on their academic performance data, and there is room for improvement.

[0005] The system according to the embodiment aims to generate and package optimal lesson content and assignments based on the performance data of each student. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, an analysis unit, a generation unit, and a packaging unit. The reception unit receives as input a specific theme or unit set by a teacher. The collection unit collects grade data for each student based on the information received by the reception unit. The analysis unit analyzes the grade data collected by the collection unit and identifies missing learning items for each student. The generation unit generates lesson content and assignments based on the learning items identified by the analysis unit. The packaging unit packages the lesson content and assignments generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate and package optimal lesson content and assignments based on the performance data of each student. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A learning support system according to an embodiment of the present invention uses a generation AI to determine the learning areas each student needs to learn based on themes and units set by a teacher and each student's academic performance data, and then generates customized lesson content and assignments. The learning support system receives the themes and units set by a teacher as input, analyzes each student's academic performance data, identifies the learning areas most suitable for each student, and generates corresponding lesson content and assignments. The generated lesson content and assignments are optimized for each student's grade and packaged. This package is provided as a service to provide an optimal learning experience for each student. For example, if a specific student has difficulty with a particular learning area, the generation AI generates assignments specific to that learning area and customizes the lesson content for that student. This allows students to focus on the learning areas they need, improving their learning effectiveness. This allows the learning support system to provide optimal learning items for each student based on the themes and units set by a teacher. For example, students can focus on the learning areas they need, improving their learning effectiveness.

[0029] A learning support system according to an embodiment includes a reception unit, a collection unit, an analysis unit, a generation unit, and a packaging unit. The reception unit receives as input a specific theme or unit set by a teacher. For example, the reception unit provides an interface for inputting the theme or unit set by the teacher. The reception unit may also support multiple input methods, such as voice input and text input. The collection unit collects performance data for each student based on the information received by the reception unit. For example, the collection unit collects performance data such as students' test scores, assignment grades, and attendance records. The collection unit may also have a function for automatically updating the performance data for each student. The analysis unit analyzes the performance data collected by the collection unit and identifies missing learning items for each student. For example, the analysis unit uses a generation AI to analyze the student's performance data and identify missing learning items. The analysis unit may also perform more accurate analysis by taking into account the student's learning history and learning style. The generation unit generates lesson content and assignments based on the learning items identified by the analysis unit. For example, the generation unit uses a generation AI to generate lesson content and assignments that are optimal for each student. The generation unit can also adjust the level of detail of the generation depending on the importance and category of the learning item. The packaging unit packages the lesson content and assignments generated by the generation unit. For example, the packaging unit generates lesson packages that are optimal for each student's grade and provides them as a service. The packaging unit can also customize the contents of the package taking into account the student's emotions and attribute information. This allows the learning support system according to the embodiment to provide each student with learning items that are optimal for them based on themes and units set by the teacher. For example, students can focus on the learning items that they need, improving their learning effectiveness.

[0030] The reception unit can analyze the teacher's past theme setting history and suggest the optimal input method. For example, the reception unit can automatically display themes and units that the teacher has frequently set in the past as candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the teacher has used in the past. The reception unit can also predict and suggest themes and units to be used at a specific time period based on the teacher's past theme setting history. In this way, by analyzing the past theme setting history, the optimal input method for the teacher can be suggested. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the teacher's past theme setting history data into the generation AI and have the generation AI suggest the optimal input method.

[0031] When inputting themes and units, the reception unit can filter them based on the teacher's current educational policy and goals. For example, the reception unit can preferentially display related themes and units based on the teacher's current educational policy. The reception unit can also suggest optimal themes and units based on the teacher's goals. The reception unit can also filter unnecessary themes and units based on the teacher's educational policy and goals. This allows for more appropriate input by filtering themes and units based on the teacher's educational policy and goals. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the teacher's educational policy data to the generation AI and cause the generation AI to filter the themes and units.

[0032] When inputting a theme or unit, the reception unit can select the optimal input means depending on the teacher's input method. For example, when a teacher inputs a theme or unit by voice, the reception unit automatically converts it into text using voice recognition technology. Furthermore, when a teacher inputs text, the reception unit can provide an input completion function to improve input efficiency. Furthermore, when a teacher inputs by image, the reception unit can automatically analyze the theme or unit using image recognition technology. This improves input efficiency by selecting the optimal input means depending on the teacher's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the teacher's voice data into a generation AI and have the generation AI convert the voice data into text data.

[0033] When inputting themes and units, the reception unit can prioritize inputting highly relevant themes based on the teacher's geographical location information. For example, if the teacher is in a specific area, the reception unit prioritizes inputting themes and units related to that area. Furthermore, if the teacher is traveling, the reception unit can also suggest optimal themes and units based on the teacher's current location. Furthermore, if the teacher is in a specific school, the reception unit can also input themes and units based on the school's curriculum. In this way, by taking the teacher's geographical location information into consideration, highly relevant themes and units can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the teacher's geographical location information data to the generation AI and cause the generation AI to suggest highly relevant themes.

[0034] When a topic or unit is input, the reception unit can analyze the teacher's social media activity and suggest related topics. The reception unit can suggest related topics or units based on, for example, content shared by the teacher on social media. The reception unit can also analyze the teacher's social media activity and suggest topics or units that the teacher may be interested in. The reception unit can also suggest related topics or units based on the activity of the teacher's friends on social media. In this way, related topics and units can be suggested by analyzing the teacher's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the teacher's social media activity data into the generation AI and cause the generation AI to suggest related topics.

[0035] The reception unit can customize the input method based on the teacher's past feedback when inputting a theme or unit. The reception unit can, for example, suggest an optimal input method based on feedback provided by the teacher in the past. The reception unit can also analyze the teacher's past feedback and optimize the input procedure. The reception unit can also customize the input interface based on the teacher's feedback history. This makes it possible to optimize the input method by reflecting the teacher's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the teacher's feedback data into the generation AI and cause the generation AI to customize the input method.

[0036] The collection unit can analyze the student's past academic performance data and select the optimal collection method. The collection unit can, for example, determine the optimal collection timing based on the student's past academic performance data. The collection unit can also analyze trends in the student's academic performance data and select the optimal collection means. The collection unit can also customize the collection method by referring to the student's past academic performance data. In this way, the optimal collection method can be selected by analyzing the past academic performance data. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the student's past academic performance data into the generation AI and have the generation AI select the optimal collection method.

[0037] When collecting grade data, the collection unit can filter the grade data based on the student's current learning situation and areas of interest. The collection unit, for example, preferentially collects relevant grade data based on the student's current learning situation. The collection unit can also filter the grade data based on the student's areas of interest. The collection unit can also exclude unnecessary grade data based on the student's learning situation and areas of interest. In this way, by filtering the grade data based on the student's learning situation and areas of interest, highly relevant data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the student's learning situation data to the generation AI and cause the generation AI to filter the grade data.

[0038] When collecting grade data, the collection unit can select the optimal collection means depending on the student's input method. For example, when a student inputs grade data by voice, the collection unit automatically converts the data into text using voice recognition technology. Furthermore, when a student inputs grade data by text, the collection unit can provide an input completion function to improve input efficiency. Furthermore, when a student inputs grade data by image, the collection unit can automatically analyze the grade data using image recognition technology. This improves collection efficiency by selecting the optimal collection means depending on the student's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the student's voice data into a generation AI and have the generation AI convert the voice data into text data.

[0039] When collecting grade data, the collection unit can prioritize collecting highly relevant data based on the student's geographical location information. For example, if the student is in a specific area, the collection unit prioritizes collecting grade data related to that area. Furthermore, if the student is traveling, the collection unit can collect optimal grade data based on the student's current location. Furthermore, if the student is in a specific school, the collection unit can collect grade data based on the school's curriculum. In this way, by taking the student's geographical location information into consideration, highly relevant grade data can be collected preferentially. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the student's geographical location information data to the generation AI and cause the generation AI to collect highly relevant grade data.

[0040] When collecting grade data, the collection unit can analyze the student's social media activity and collect related data. For example, the collection unit collects related grade data based on content shared by the student on social media. The collection unit can also analyze the student's social media activity and collect grade data that may be of interest to the student. The collection unit can also collect related grade data by referring to the activity of the student's friends on social media. In this way, related grade data can be collected by analyzing the student's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the student's social media activity data into the generation AI and cause the generation AI to collect related grade data.

[0041] When collecting performance data, the collection unit can customize the collection method based on the student's past feedback. For example, the collection unit can suggest an optimal collection method based on feedback provided by the student in the past. The collection unit can also analyze the student's past feedback and optimize the collection procedure. The collection unit can also customize the collection interface based on the student's feedback history. This allows the collection method to be optimized by reflecting the student's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the student's feedback data into a generation AI and have the generation AI customize the collection method.

[0042] During analysis, the analysis unit can identify missing learning items based on the student's past learning history. The analysis unit can identify missing learning items based on, for example, the student's past learning history. The analysis unit can also analyze trends in the student's learning history to identify optimal learning items. The analysis unit can also customize the identification method by referring to the student's past learning history. This makes it possible to accurately identify missing learning items by referring to the past learning history. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the student's past learning history data into the generation AI and have the generation AI identify missing learning items.

[0043] During analysis, the analysis unit can use different analysis algorithms depending on the student's learning style. For example, if the student has a visual learning style, the analysis unit can apply a visual analysis algorithm. Furthermore, if the student has an auditory learning style, the analysis unit can also apply an auditory analysis algorithm. Furthermore, if the student has a tactile learning style, the analysis unit can also apply a tactile analysis algorithm. By applying an analysis algorithm according to the student's learning style, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the student's learning style data into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis based on the student's past academic performance data. The analysis unit, for example, adjusts parameters of the analysis algorithm based on the student's past academic performance data. The analysis unit can also analyze trends in the student's academic performance data to improve the accuracy of the analysis. The analysis unit can also refer to the student's past academic performance data to feed back the analysis results and improve the accuracy. In this way, by referring to the past academic performance data, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the student's past academic performance data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0045] During analysis, the analysis unit can determine the priority of missing learning items based on the student's submission date. For example, if the student's submission deadline is approaching, the analysis unit can prioritize missing learning items. Furthermore, if the student's submission deadline is far away, the analysis unit can also identify detailed learning items. Furthermore, the analysis unit can also identify optimal learning items based on the student's submission date. In this way, by determining the priority of learning items based on the submission date, learning items that match the submission deadline can be identified. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the student's submission date data into the generation AI and have the generation AI determine the priority of learning items.

[0046] During analysis, the analysis unit can adjust the order of missing learning items based on the relevance of the student. The analysis unit, for example, identifies missing learning items based on the relevance of the student's learning items. The analysis unit can also analyze the relevance of the student's learning items and identify learning items in an optimal order. The analysis unit can also exclude unnecessary learning items based on the student's relevance. This enables efficient learning by adjusting the order of learning items based on relevance. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the student's learning items into a generation AI and cause the generation AI to adjust the order of the learning items.

[0047] During analysis, the analysis unit can adjust the method for identifying missing learning items based on the student's expertise level. The analysis unit, for example, identifies optimal learning items based on the student's expertise level. The analysis unit can also analyze the student's expertise level and customize the identification method. The analysis unit can also exclude unnecessary learning items based on the student's expertise level. In this way, appropriate learning items can be identified by adjusting the method for identifying learning items based on the student's expertise level. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the student's expertise level data into the generation AI and cause the generation AI to adjust the method for identifying learning items.

[0048] The generation unit can adjust the level of detail of the generated lesson content or assignments according to the importance of the learning item when generating the lesson content or assignments. For example, the generation unit generates detailed lesson content for a learning item with high importance. The generation unit can also generate simple assignments for a learning item with low importance. The generation unit can also adjust the level of detail of the generated lesson content based on the importance of the learning item. In this way, by adjusting the level of detail of the generated lesson content based on the importance of the learning item, appropriate lesson content or assignments can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input importance data of the learning item to the generation AI and cause the generation AI to adjust the level of detail of the generated lesson content.

[0049] When generating lesson content and assignments, the generation unit can use different generation algorithms depending on the category of the learning item. For example, the generation unit applies a generation algorithm dedicated to science to a science learning item. The generation unit can also apply a generation algorithm dedicated to mathematics to a mathematics learning item. The generation unit can also apply a generation algorithm dedicated to Japanese to a Japanese learning item. In this way, by applying a generation algorithm according to the category of the learning item, appropriate lesson content and assignments can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input category data of the learning item into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0050] When generating lesson content and assignments, the generation unit can improve the accuracy of generation based on the student's past learning results. The generation unit, for example, adjusts the parameters of the generation algorithm based on the student's past learning results. The generation unit can also analyze trends in the student's learning results and improve the accuracy of generation. The generation unit can also refer to the student's past learning results to feed back the generation results and improve the accuracy. In this way, by referring to the past learning results, the accuracy of generation is improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the student's past learning result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0051] When generating lesson content and assignments, the generation unit can determine the generation priority based on the submission date of the learning item. For example, the generation unit prioritizes the generation of lesson content and assignments for learning items with upcoming submission deadlines. The generation unit can also generate detailed lesson content and assignments for learning items with distant submission deadlines. The generation unit can also generate optimal lesson content and assignments based on the submission date of the learning item. In this way, by determining the generation priority based on the submission date, lesson content and assignments that meet the submission deadlines can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input submission date data of learning items to the generation AI and have the generation AI determine the generation priority.

[0052] When generating lesson content and assignments, the generation unit can adjust the order of generation based on the relevance of the learning items. For example, the generation unit prioritizes the generation of lesson content and assignments based on the relevance of the learning items. The generation unit can also analyze the relevance of the learning items and generate lesson content and assignments in an optimal order. The generation unit can also exclude unnecessary lesson content and assignments based on the relevance of the learning items. This enables efficient learning by adjusting the order of generation based on the relevance. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input relevance data of learning items into the generation AI and cause the generation AI to adjust the order of generation.

[0053] When generating lesson content or assignments, the generation unit can adjust the use of technical terminology based on the student's level of expertise. For example, the generation unit uses optimal technical terminology based on the student's level of expertise. The generation unit can also analyze the student's level of expertise and customize the technical terminology used for generation. The generation unit can also exclude unnecessary technical terminology based on the student's level of expertise. This allows appropriate lesson content or assignments to be generated by adjusting the use of technical terminology based on the student's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input student's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0054] During packaging, the packaging unit can improve the accuracy of the package based on the interrelationships between the learning items. For example, the packaging unit generates an optimal package based on the interrelationships between the learning items. The packaging unit can also analyze the interrelationships between the learning items and improve the accuracy of the package. The packaging unit can also exclude unnecessary items based on the interrelationships between the learning items. This improves the accuracy of the package by taking the interrelationships between the learning items into consideration. Some or all of the above-mentioned processing in the packaging unit may be performed using AI, for example, or may be performed without using AI. For example, the packaging unit can input interrelationship data between the learning items into a generation AI and cause the generation AI to improve the accuracy of the package.

[0055] During packaging, the packaging unit can generate a package based on student attribute information. The packaging unit, for example, generates an optimal package based on student attribute information. The packaging unit can also analyze student attribute information to improve package accuracy. The packaging unit can also exclude unnecessary items based on student attribute information. This allows for the generation of an optimal package by taking student attribute information into consideration. Some or all of the above-described processing in the packaging unit may be performed using AI, for example, or may be performed without using AI. For example, the packaging unit can input student attribute information data into a generation AI and cause the generation AI to generate a package.

[0056] When packaging, the packaging unit can weight the packages based on the submission frequency of the learning items. For example, the packaging unit can prioritize packaging of learning items with a high submission frequency. The packaging unit can also generate detailed packages for learning items with a low submission frequency. The packaging unit can also generate optimal packages based on the submission frequency of the learning items. In this way, by weighting the packages based on the submission frequency, important learning items can be prioritized in the packaging. Some or all of the above-mentioned processing in the packaging unit can be performed using, for example, AI, or can be performed without using AI. For example, the packaging unit can input submission frequency data of learning items into a generation AI and have the generation AI perform package weighting.

[0057] During packaging, the packaging unit can generate a package based on the geographic distribution of the learning items. The packaging unit, for example, generates an optimal package based on the geographic distribution of the learning items. The packaging unit can also analyze the geographic distribution of the learning items to improve the accuracy of the package. The packaging unit can also exclude unnecessary items based on the geographic distribution of the learning items. This allows for the generation of an optimal package by taking geographic distribution into consideration. Some or all of the above-described processing in the packaging unit may be performed using, for example, AI, or may be performed without using AI. For example, the packaging unit can input geographic distribution data of the learning items into a generation AI and cause the generation AI to generate a package.

[0058] During packaging, the packaging unit can improve the accuracy of the package based on the related literature of the learning item. For example, the packaging unit generates an optimal package based on the related literature of the learning item. The packaging unit can also analyze the related literature of the learning item to improve the accuracy of the package. The packaging unit can also exclude unnecessary items based on the related literature of the learning item. This improves the accuracy of the package by referring to the related literature. Some or all of the above-mentioned processing in the packaging unit can be performed using, for example, AI, or can be performed without using AI. For example, the packaging unit can input related literature data of the learning item into the generation AI and have the generation AI improve the accuracy of the package.

[0059] During packaging, the packaging unit can generate a package based on the market value of the learning items. For example, the packaging unit generates an optimal package based on the market value of the learning items. The packaging unit can also analyze the market value of the learning items to improve the accuracy of the package. The packaging unit can also exclude unnecessary items based on the market value of the learning items. This allows for the generation of an optimal package by taking market value into consideration. Some or all of the above-described processing in the packaging unit may be performed using, for example, AI, or may be performed without using AI. For example, the packaging unit can input market value data of the learning items into a generation AI and have the generation AI generate a package.

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

[0061] The reception unit can analyze the teacher's past theme setting history and suggest the optimal input method. For example, themes and units that the teacher has frequently set in the past can be automatically displayed as candidates. The reception unit can also prioritize suggestions for input methods (voice, text, etc.) that the teacher has used in the past. Furthermore, the reception unit can predict and suggest themes and units to be used at specific times based on the teacher's past theme setting history. In this way, by analyzing the past theme setting history, the optimal input method for the teacher can be suggested. Some or all of the above-described processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the teacher's past theme setting history data into the generation AI and have the generation AI suggest the optimal input method.

[0062] When inputting themes and units, the reception unit can prioritize inputting highly relevant themes based on the teacher's geographical location information. For example, if the teacher is in a specific area, the reception unit can prioritize inputting themes and units related to that area. In addition, if the teacher is traveling, the reception unit can also suggest optimal themes and units based on the teacher's current location. Furthermore, if the teacher is in a specific school, the reception unit can also input themes and units based on the school's curriculum. In this way, by taking the teacher's geographical location information into consideration, highly relevant themes and units can be prioritized. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the teacher's geographical location information data to the generation AI and cause the generation AI to suggest highly relevant themes.

[0063] When collecting grade data, the collection unit can filter the grade data based on the student's current learning situation and areas of interest. For example, the collection unit can prioritize collection of relevant grade data based on the student's current learning situation. The collection unit can also filter the grade data based on the student's areas of interest. Furthermore, the collection unit can exclude unnecessary grade data based on the student's learning situation and areas of interest. In this way, by filtering the grade data based on the student's learning situation and areas of interest, highly relevant data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the student's learning situation data to the generation AI and cause the generation AI to filter the grade data.

[0064] During analysis, the analysis unit can use different analysis algorithms depending on the student's learning style. For example, if the student has a visual learning style, the analysis unit can apply a visual analysis algorithm. Furthermore, if the student has an auditory learning style, the analysis unit can also apply an auditory analysis algorithm. Furthermore, if the student has a tactile learning style, the analysis unit can also apply a tactile analysis algorithm. By applying an analysis algorithm according to the student's learning style, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the student's learning style data into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0065] When generating lesson content and assignments, the generation unit can use different generation algorithms depending on the category of the learning item. For example, the generation unit can apply a generation algorithm dedicated to science to science learning items. The generation unit can also apply a generation algorithm dedicated to mathematics to mathematics learning items. The generation unit can also apply a generation algorithm dedicated to Japanese to Japanese learning items. In this way, by applying a generation algorithm according to the category of the learning item, appropriate lesson content and assignments can be generated. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input category data of the learning item into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0066] During packaging, the packaging unit can improve the accuracy of the package based on the interrelationships between the learning items. For example, the packaging unit generates an optimal package based on the interrelationships between the learning items. The packaging unit can also analyze the interrelationships between the learning items and improve the accuracy of the package. Furthermore, the packaging unit can also exclude unnecessary items based on the interrelationships between the learning items. This improves the accuracy of the package by taking the interrelationships between the learning items into consideration. Some or all of the above-mentioned processing in the packaging unit may be performed using AI, for example, or may be performed without using AI. For example, the packaging unit can input interrelationship data between the learning items into a generation AI and have the generation AI improve the accuracy of the package.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The reception unit receives the specific theme or unit set by the teacher as input. For example, the reception unit provides an interface for inputting the theme or unit set by the teacher, and supports multiple input methods such as voice input and text input. Step 2: The collection unit collects the academic performance data for each student based on the information received by the reception unit. For example, the collection unit has the function of collecting and automatically updating academic performance data such as students' test scores, assignment grades, and attendance status. Step 3: The analysis unit analyzes the academic performance data collected by the collection unit and identifies the learning items that each student is lacking. For example, the analysis unit may use a generative AI to analyze the academic performance data, identify the learning items that are lacking, and perform a highly accurate analysis taking into account the student's learning history and learning style. Step 4: The generator generates lesson content and assignments based on the learning items identified by the analyzer. For example, the generator uses a generation AI to generate lesson content and assignments that are optimal for each student, adjusting the level of detail of the generated content depending on the importance and category of the learning items. Step 5: The packaging unit packages the lesson content and assignments generated by the generation unit. For example, the packaging unit generates the optimal lesson package for each student's grade and provides it as a service, customizing the package content taking into account the student's emotions and attribute information.

[0069] (Example 2) A learning support system according to an embodiment of the present invention uses a generation AI to determine the learning areas each student needs to learn based on themes and units set by a teacher and each student's academic performance data, and then generates customized lesson content and assignments. The learning support system receives the themes and units set by a teacher as input, analyzes each student's academic performance data, identifies the learning areas most suitable for each student, and generates corresponding lesson content and assignments. The generated lesson content and assignments are optimized for each student's grade and packaged. This package is provided as a service to provide an optimal learning experience for each student. For example, if a specific student has difficulty with a particular learning area, the generation AI generates assignments specific to that learning area and customizes the lesson content for that student. This allows students to focus on the learning areas they need, improving their learning effectiveness. This allows the learning support system to provide optimal learning items for each student based on the themes and units set by a teacher. For example, students can focus on the learning areas they need, improving their learning effectiveness.

[0070] A learning support system according to an embodiment includes a reception unit, a collection unit, an analysis unit, a generation unit, and a packaging unit. The reception unit receives as input a specific theme or unit set by a teacher. For example, the reception unit provides an interface for inputting the theme or unit set by the teacher. The reception unit may also support multiple input methods, such as voice input and text input. The collection unit collects performance data for each student based on the information received by the reception unit. For example, the collection unit collects performance data such as students' test scores, assignment grades, and attendance records. The collection unit may also have a function for automatically updating the performance data for each student. The analysis unit analyzes the performance data collected by the collection unit and identifies missing learning items for each student. For example, the analysis unit uses a generation AI to analyze the student's performance data and identify missing learning items. The analysis unit may also perform more accurate analysis by taking into account the student's learning history and learning style. The generation unit generates lesson content and assignments based on the learning items identified by the analysis unit. For example, the generation unit uses a generation AI to generate lesson content and assignments that are optimal for each student. The generation unit can also adjust the level of detail of the generation depending on the importance and category of the learning item. The packaging unit packages the lesson content and assignments generated by the generation unit. For example, the packaging unit generates lesson packages that are optimal for each student's grade and provides them as a service. The packaging unit can also customize the contents of the package taking into account the student's emotions and attribute information. This allows the learning support system according to the embodiment to provide each student with learning items that are optimal for them based on themes and units set by the teacher. For example, students can focus on the learning items that they need, improving their learning effectiveness.

[0071] The reception unit can analyze the teacher's emotions and adjust the method for inputting themes and units based on the analyzed emotions. For example, if the teacher is stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the teacher is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the teacher is in a hurry, the reception unit can prioritize voice input to enable quick input of themes and units. This allows for more appropriate input of themes and units by adjusting the input method according to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the teacher's facial expression data into the generation AI and have the generation AI estimate the teacher's emotions.

[0072] The reception unit can analyze the teacher's past theme setting history and suggest the optimal input method. For example, the reception unit can automatically display themes and units that the teacher has frequently set in the past as candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the teacher has used in the past. The reception unit can also predict and suggest themes and units to be used at a specific time period based on the teacher's past theme setting history. In this way, by analyzing the past theme setting history, the optimal input method for the teacher can be suggested. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the teacher's past theme setting history data into the generation AI and have the generation AI suggest the optimal input method.

[0073] When inputting themes and units, the reception unit can filter them based on the teacher's current educational policy and goals. For example, the reception unit can preferentially display related themes and units based on the teacher's current educational policy. The reception unit can also suggest optimal themes and units based on the teacher's goals. The reception unit can also filter unnecessary themes and units based on the teacher's educational policy and goals. This allows for more appropriate input by filtering themes and units based on the teacher's educational policy and goals. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the teacher's educational policy data to the generation AI and cause the generation AI to filter the themes and units.

[0074] When inputting a theme or unit, the reception unit can select the optimal input means depending on the teacher's input method. For example, when a teacher inputs a theme or unit by voice, the reception unit automatically converts it into text using voice recognition technology. Furthermore, when a teacher inputs text, the reception unit can provide an input completion function to improve input efficiency. Furthermore, when a teacher inputs by image, the reception unit can automatically analyze the theme or unit using image recognition technology. This improves input efficiency by selecting the optimal input means depending on the teacher's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the teacher's voice data into a generation AI and have the generation AI convert the voice data into text data.

[0075] The reception unit can analyze the teacher's emotions and determine the priority of the themes and units to be input based on the analyzed emotions. For example, if the teacher is feeling stressed, the reception unit can prioritize inputting themes and units with high importance. Furthermore, if the teacher is relaxed, the reception unit can also input detailed themes and units. Furthermore, if the teacher is in a hurry, the reception unit can also prioritize inputting the most important themes and units. Thus, by determining the priority of themes and units according to the teacher's emotions, important themes and units can be input preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI. For example, the reception unit can input the teacher's facial expression data into the generation AI and have the generation AI estimate the teacher's emotions.

[0076] When inputting themes and units, the reception unit can prioritize inputting highly relevant themes based on the teacher's geographical location information. For example, if the teacher is in a specific area, the reception unit prioritizes inputting themes and units related to that area. Furthermore, if the teacher is traveling, the reception unit can also suggest optimal themes and units based on the teacher's current location. Furthermore, if the teacher is in a specific school, the reception unit can also input themes and units based on the school's curriculum. In this way, by taking the teacher's geographical location information into consideration, highly relevant themes and units can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the teacher's geographical location information data to the generation AI and cause the generation AI to suggest highly relevant themes.

[0077] When a topic or unit is input, the reception unit can analyze the teacher's social media activity and suggest related topics. The reception unit can suggest related topics or units based on, for example, content shared by the teacher on social media. The reception unit can also analyze the teacher's social media activity and suggest topics or units that the teacher may be interested in. The reception unit can also suggest related topics or units based on the activity of the teacher's friends on social media. In this way, related topics and units can be suggested by analyzing the teacher's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the teacher's social media activity data into the generation AI and cause the generation AI to suggest related topics.

[0078] The reception unit can customize the input method based on the teacher's past feedback when inputting a theme or unit. The reception unit can, for example, suggest an optimal input method based on feedback provided by the teacher in the past. The reception unit can also analyze the teacher's past feedback and optimize the input procedure. The reception unit can also customize the input interface based on the teacher's feedback history. This makes it possible to optimize the input method by reflecting the teacher's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the teacher's feedback data into the generation AI and cause the generation AI to customize the input method.

[0079] The collection unit can analyze the student's emotions and adjust the timing of collecting grade data based on the analyzed emotions. For example, if the student is stressed, the collection unit can postpone the collection of grade data. Furthermore, if the student is relaxed, the collection unit can also prioritize the collection of grade data. Furthermore, if the student is concentrating, the collection unit can quickly collect grade data. By adjusting the timing of collecting grade data according to the student's emotions, data can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the student's facial expression data into the generation AI and have the generation AI estimate the student's emotions.

[0080] The collection unit can analyze the student's past academic performance data and select the optimal collection method. The collection unit can, for example, determine the optimal collection timing based on the student's past academic performance data. The collection unit can also analyze trends in the student's academic performance data and select the optimal collection means. The collection unit can also customize the collection method by referring to the student's past academic performance data. In this way, the optimal collection method can be selected by analyzing the past academic performance data. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the student's past academic performance data into the generation AI and have the generation AI select the optimal collection method.

[0081] When collecting grade data, the collection unit can filter the grade data based on the student's current learning situation and areas of interest. The collection unit, for example, preferentially collects relevant grade data based on the student's current learning situation. The collection unit can also filter the grade data based on the student's areas of interest. The collection unit can also exclude unnecessary grade data based on the student's learning situation and areas of interest. In this way, by filtering the grade data based on the student's learning situation and areas of interest, highly relevant data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the student's learning situation data to the generation AI and cause the generation AI to filter the grade data.

[0082] When collecting grade data, the collection unit can select the optimal collection means depending on the student's input method. For example, when a student inputs grade data by voice, the collection unit automatically converts the data into text using voice recognition technology. Furthermore, when a student inputs grade data by text, the collection unit can provide an input completion function to improve input efficiency. Furthermore, when a student inputs grade data by image, the collection unit can automatically analyze the grade data using image recognition technology. This improves collection efficiency by selecting the optimal collection means depending on the student's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the student's voice data into a generation AI and have the generation AI convert the voice data into text data.

[0083] The collection unit can analyze the student's emotions and determine the priority of the grade data to be collected based on the analyzed emotions. For example, if the student is stressed, the collection unit can prioritize collecting grade data with high importance. Furthermore, if the student is relaxed, the collection unit can also collect detailed grade data. Furthermore, if the student is concentrating, the collection unit can prioritize collecting the most important grade data. Thus, by prioritizing grade data according to the student's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the student's facial expression data into the generation AI and have the generation AI estimate the student's emotions.

[0084] When collecting grade data, the collection unit can prioritize collecting highly relevant data based on the student's geographical location information. For example, if the student is in a specific area, the collection unit prioritizes collecting grade data related to that area. Furthermore, if the student is traveling, the collection unit can collect optimal grade data based on the student's current location. Furthermore, if the student is in a specific school, the collection unit can collect grade data based on the school's curriculum. In this way, by taking the student's geographical location information into consideration, highly relevant grade data can be collected preferentially. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the student's geographical location information data to the generation AI and cause the generation AI to collect highly relevant grade data.

[0085] When collecting grade data, the collection unit can analyze the student's social media activity and collect related data. For example, the collection unit collects related grade data based on content shared by the student on social media. The collection unit can also analyze the student's social media activity and collect grade data that may be of interest to the student. The collection unit can also collect related grade data by referring to the activity of the student's friends on social media. In this way, related grade data can be collected by analyzing the student's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the student's social media activity data into the generation AI and cause the generation AI to collect related grade data.

[0086] When collecting performance data, the collection unit can customize the collection method based on the student's past feedback. For example, the collection unit can suggest an optimal collection method based on feedback provided by the student in the past. The collection unit can also analyze the student's past feedback and optimize the collection procedure. The collection unit can also customize the collection interface based on the student's feedback history. This allows the collection method to be optimized by reflecting the student's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the student's feedback data into a generation AI and have the generation AI customize the collection method.

[0087] The analysis unit can analyze the student's emotions and adjust the method for identifying missing learning items based on the analyzed emotions. For example, if the student is stressed, the analysis unit can prioritize identifying easy learning items. Furthermore, if the student is relaxed, the analysis unit can also prioritize identifying detailed learning items. Furthermore, if the student is concentrating, the analysis unit can also prioritize identifying the most important learning items. By adjusting the method for identifying learning items according to the student's emotions, more appropriate learning items can be identified. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the student's facial expression data into the generative AI and cause the generative AI to estimate the student's emotions.

[0088] During analysis, the analysis unit can identify missing learning items based on the student's past learning history. The analysis unit can identify missing learning items based on, for example, the student's past learning history. The analysis unit can also analyze trends in the student's learning history to identify optimal learning items. The analysis unit can also customize the identification method by referring to the student's past learning history. This makes it possible to accurately identify missing learning items by referring to the past learning history. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the student's past learning history data into the generation AI and have the generation AI identify missing learning items.

[0089] During analysis, the analysis unit can use different analysis algorithms depending on the student's learning style. For example, if the student has a visual learning style, the analysis unit can apply a visual analysis algorithm. Furthermore, if the student has an auditory learning style, the analysis unit can also apply an auditory analysis algorithm. Furthermore, if the student has a tactile learning style, the analysis unit can also apply a tactile analysis algorithm. By applying an analysis algorithm according to the student's learning style, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the student's learning style data into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0090] During analysis, the analysis unit can improve the accuracy of the analysis based on the student's past academic performance data. The analysis unit, for example, adjusts parameters of the analysis algorithm based on the student's past academic performance data. The analysis unit can also analyze trends in the student's academic performance data to improve the accuracy of the analysis. The analysis unit can also refer to the student's past academic performance data to feed back the analysis results and improve the accuracy. In this way, by referring to the past academic performance data, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the student's past academic performance data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0091] The analysis unit can analyze the student's emotions and prioritize missing learning items based on the analyzed emotions. For example, if the student is feeling stressed, the analysis unit can prioritize identifying learning items with high importance. Furthermore, if the student is relaxed, the analysis unit can also identify detailed learning items. Furthermore, if the student is concentrating, the analysis unit can prioritize identifying the most important learning items. Thus, by prioritizing learning items according to the student's emotions, important learning items can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the student's facial expression data into the generative AI and cause the generative AI to estimate the student's emotions.

[0092] During analysis, the analysis unit can determine the priority of missing learning items based on the student's submission date. For example, if the student's submission deadline is approaching, the analysis unit can prioritize missing learning items. Furthermore, if the student's submission deadline is far away, the analysis unit can also identify detailed learning items. Furthermore, the analysis unit can also identify optimal learning items based on the student's submission date. In this way, by determining the priority of learning items based on the submission date, learning items that match the submission deadline can be identified. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the student's submission date data into the generation AI and have the generation AI determine the priority of learning items.

[0093] During analysis, the analysis unit can adjust the order of missing learning items based on the relevance of the student. The analysis unit, for example, identifies missing learning items based on the relevance of the student's learning items. The analysis unit can also analyze the relevance of the student's learning items and identify learning items in an optimal order. The analysis unit can also exclude unnecessary learning items based on the student's relevance. This enables efficient learning by adjusting the order of learning items based on relevance. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the student's learning items into a generation AI and cause the generation AI to adjust the order of the learning items.

[0094] During analysis, the analysis unit can adjust the method for identifying missing learning items based on the student's expertise level. The analysis unit, for example, identifies optimal learning items based on the student's expertise level. The analysis unit can also analyze the student's expertise level and customize the identification method. The analysis unit can also exclude unnecessary learning items based on the student's expertise level. In this way, appropriate learning items can be identified by adjusting the method for identifying learning items based on the student's expertise level. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the student's expertise level data into the generation AI and cause the generation AI to adjust the method for identifying learning items.

[0095] The generation unit can analyze the student's emotions and adjust the method for generating lesson content and assignments based on the analyzed emotions. For example, if the student is feeling stressed, the generation unit can prioritize generating simple assignments. Furthermore, if the student is relaxed, the generation unit can also generate detailed lesson content. Furthermore, if the student is concentrating, the generation unit can prioritize generating the most important lesson content and assignments. By adjusting the method for generating lesson content and assignments according to the student's emotions, more appropriate lesson content and assignments can be generated. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI. For example, the generation unit can input the student's facial expression data into the generation AI and have the generation AI estimate the student's emotions.

[0096] The generation unit can adjust the level of detail of the generated lesson content or assignments according to the importance of the learning item when generating the lesson content or assignments. For example, the generation unit generates detailed lesson content for a learning item with high importance. The generation unit can also generate simple assignments for a learning item with low importance. The generation unit can also adjust the level of detail of the generated lesson content based on the importance of the learning item. In this way, by adjusting the level of detail of the generated lesson content based on the importance of the learning item, appropriate lesson content or assignments can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input importance data of the learning item to the generation AI and cause the generation AI to adjust the level of detail of the generated lesson content.

[0097] When generating lesson content and assignments, the generation unit can use different generation algorithms depending on the category of the learning item. For example, the generation unit applies a generation algorithm dedicated to science to a science learning item. The generation unit can also apply a generation algorithm dedicated to mathematics to a mathematics learning item. The generation unit can also apply a generation algorithm dedicated to Japanese to a Japanese learning item. In this way, by applying a generation algorithm according to the category of the learning item, appropriate lesson content and assignments can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input category data of the learning item into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0098] When generating lesson content and assignments, the generation unit can improve the accuracy of generation based on the student's past learning results. The generation unit, for example, adjusts the parameters of the generation algorithm based on the student's past learning results. The generation unit can also analyze trends in the student's learning results and improve the accuracy of generation. The generation unit can also refer to the student's past learning results to feed back the generation results and improve the accuracy. In this way, by referring to the past learning results, the accuracy of generation is improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the student's past learning result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0099] The generation unit can analyze the student's emotions and adjust the length of the lesson content or assignments based on the analyzed emotions. For example, if the student is feeling stressed, the generation unit can generate short lesson content or assignments. Furthermore, if the student is relaxed, the generation unit can also generate detailed lesson content or assignments. Furthermore, if the student is concentrating, the generation unit can prioritize generating the most important lesson content or assignments. This allows the generation of lesson content or assignments of appropriate length by adjusting the length of the lesson content or assignments according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the student's facial expression data into the generation AI and have the generation AI estimate the student's emotions.

[0100] When generating lesson content and assignments, the generation unit can determine the generation priority based on the submission date of the learning item. For example, the generation unit prioritizes the generation of lesson content and assignments for learning items with upcoming submission deadlines. The generation unit can also generate detailed lesson content and assignments for learning items with distant submission deadlines. The generation unit can also generate optimal lesson content and assignments based on the submission date of the learning item. In this way, by determining the generation priority based on the submission date, lesson content and assignments that meet the submission deadlines can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input submission date data of learning items to the generation AI and have the generation AI determine the generation priority.

[0101] When generating lesson content and assignments, the generation unit can adjust the order of generation based on the relevance of the learning items. For example, the generation unit prioritizes the generation of lesson content and assignments based on the relevance of the learning items. The generation unit can also analyze the relevance of the learning items and generate lesson content and assignments in an optimal order. The generation unit can also exclude unnecessary lesson content and assignments based on the relevance of the learning items. This enables efficient learning by adjusting the order of generation based on the relevance. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input relevance data of learning items into the generation AI and cause the generation AI to adjust the order of generation.

[0102] When generating lesson content or assignments, the generation unit can adjust the use of technical terminology based on the student's level of expertise. For example, the generation unit uses optimal technical terminology based on the student's level of expertise. The generation unit can also analyze the student's level of expertise and customize the technical terminology used for generation. The generation unit can also exclude unnecessary technical terminology based on the student's level of expertise. This allows appropriate lesson content or assignments to be generated by adjusting the use of technical terminology based on the student's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input student's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0103] The packaging unit can analyze the student's emotions and prioritize the lesson content and assignments to be packaged based on the analyzed emotions. For example, if the student is feeling stressed, the packaging unit can prioritize packaging the most important lesson content and assignments. Furthermore, if the student is relaxed, the packaging unit can package detailed lesson content and assignments. Furthermore, if the student is concentrating, the packaging unit can prioritize packaging the most important lesson content and assignments. Thus, by prioritizing lesson content and assignments according to the student's emotions, important content can be packaged preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the packaging unit can be performed using, for example, an AI. For example, the packaging unit can input the student's facial expression data into the generation AI and cause the generation AI to estimate the student's emotions.

[0104] During packaging, the packaging unit can improve the accuracy of the package based on the interrelationships between the learning items. For example, the packaging unit generates an optimal package based on the interrelationships between the learning items. The packaging unit can also analyze the interrelationships between the learning items and improve the accuracy of the package. The packaging unit can also exclude unnecessary items based on the interrelationships between the learning items. This improves the accuracy of the package by taking the interrelationships between the learning items into consideration. Some or all of the above-mentioned processing in the packaging unit may be performed using AI, for example, or may be performed without using AI. For example, the packaging unit can input interrelationship data between the learning items into a generation AI and cause the generation AI to improve the accuracy of the package.

[0105] During packaging, the packaging unit can generate a package based on student attribute information. The packaging unit, for example, generates an optimal package based on student attribute information. The packaging unit can also analyze student attribute information to improve package accuracy. The packaging unit can also exclude unnecessary items based on student attribute information. This allows for the generation of an optimal package by taking student attribute information into consideration. Some or all of the above-described processing in the packaging unit may be performed using AI, for example, or may be performed without using AI. For example, the packaging unit can input student attribute information data into a generation AI and cause the generation AI to generate a package.

[0106] When packaging, the packaging unit can weight the packages based on the submission frequency of the learning items. For example, the packaging unit can prioritize packaging of learning items with a high submission frequency. The packaging unit can also generate detailed packages for learning items with a low submission frequency. The packaging unit can also generate optimal packages based on the submission frequency of the learning items. In this way, by weighting the packages based on the submission frequency, important learning items can be prioritized in the packaging. Some or all of the above-mentioned processing in the packaging unit can be performed using, for example, AI, or can be performed without using AI. For example, the packaging unit can input submission frequency data of learning items into a generation AI and have the generation AI perform package weighting.

[0107] The packaging unit can analyze the student's emotions and adjust the display method of the lesson content and assignments to be packaged based on the analyzed emotions. For example, if the student is stressed, the packaging unit can provide a simple display method. Alternatively, if the student is relaxed, the packaging unit can provide a detailed display method. Alternatively, if the student is concentrating, the packaging unit can prioritize displaying the most important lesson content and assignments. This allows for an appropriate display method to be provided by adjusting the display method according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the packaging unit can be performed using, for example, an AI, or without an AI. For example, the packaging unit can input the student's facial expression data into the generation AI and have the generation AI estimate the student's emotions.

[0108] During packaging, the packaging unit can generate a package based on the geographic distribution of the learning items. The packaging unit, for example, generates an optimal package based on the geographic distribution of the learning items. The packaging unit can also analyze the geographic distribution of the learning items to improve the accuracy of the package. The packaging unit can also exclude unnecessary items based on the geographic distribution of the learning items. This allows for the generation of an optimal package by taking geographic distribution into consideration. Some or all of the above-described processing in the packaging unit may be performed using, for example, AI, or may be performed without using AI. For example, the packaging unit can input geographic distribution data of the learning items into a generation AI and cause the generation AI to generate a package.

[0109] During packaging, the packaging unit can improve the accuracy of the package based on the related literature of the learning item. For example, the packaging unit generates an optimal package based on the related literature of the learning item. The packaging unit can also analyze the related literature of the learning item to improve the accuracy of the package. The packaging unit can also exclude unnecessary items based on the related literature of the learning item. This improves the accuracy of the package by referring to the related literature. Some or all of the above-mentioned processing in the packaging unit can be performed using, for example, AI, or can be performed without using AI. For example, the packaging unit can input related literature data of the learning item into the generation AI and have the generation AI improve the accuracy of the package.

[0110] During packaging, the packaging unit can generate a package based on the market value of the learning items. For example, the packaging unit generates an optimal package based on the market value of the learning items. The packaging unit can also analyze the market value of the learning items to improve the accuracy of the package. The packaging unit can also exclude unnecessary items based on the market value of the learning items. This allows for the generation of an optimal package by taking market value into consideration. Some or all of the above-described processing in the packaging unit may be performed using, for example, AI, or may be performed without using AI. For example, the packaging unit can input market value data of the learning items into a generation AI and have the generation AI generate a package. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, collection unit, analysis unit, generation unit, and packaging unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and provides an interface for inputting themes and units set by the teacher. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects grade data for each student. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the grade data using a generation AI to identify missing learning items. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates lesson content and assignments optimal for each student. The packaging unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and packages the generated lesson content and assignments. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, collection unit, analysis unit, generation unit, and packaging unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for inputting themes and units set by the teacher. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects grade data for each student. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the grade data using a generation AI to identify missing learning items. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates lesson content and assignments optimal for each student. The packaging unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and packages the generated lesson content and assignments. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, collection unit, analysis unit, generation unit, and packaging unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and provides an interface for inputting themes and units set by the teacher. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects grade data for each student. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the grade data using a generation AI to identify missing learning items. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates lesson content and assignments optimal for each student. The packaging unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and packages the generated lesson content and assignments. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, collection unit, analysis unit, generation unit, and packaging unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and provides an interface for inputting themes and units set by the teacher. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects grade data for each student. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the grade data using a generation AI to identify missing learning items. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates lesson content and assignments optimal for each student. The packaging unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and packages the generated lesson content and assignments.

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

[0112] The reception unit can analyze the teacher's past theme setting history and suggest the optimal input method. For example, themes and units that the teacher has frequently set in the past can be automatically displayed as candidates. The reception unit can also prioritize suggestions for input methods (voice, text, etc.) that the teacher has used in the past. Furthermore, the reception unit can predict and suggest themes and units to be used at specific times based on the teacher's past theme setting history. In this way, by analyzing the past theme setting history, the optimal input method for the teacher can be suggested. Some or all of the above-described processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the teacher's past theme setting history data into the generation AI and have the generation AI suggest the optimal input method.

[0113] The collection unit can analyze the student's emotions and adjust the timing of collecting grade data based on the analyzed emotions. For example, if the student is feeling stressed, the collection unit can postpone the collection of grade data. Furthermore, if the student is relaxed, the collection unit can prioritize the collection of grade data. Furthermore, if the student is concentrating, the collection unit can quickly collect grade data. By adjusting the timing of collecting grade data according to the student's emotions, data can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the student's facial expression data into the generation AI and have the generation AI estimate the student's emotions.

[0114] The analysis unit can analyze the student's emotions and adjust the method for identifying missing learning items based on the analyzed emotions. For example, if the student is stressed, it can prioritize identifying simple learning items. The analysis unit can also prioritize identifying detailed learning items if the student is relaxed. Furthermore, the analysis unit can prioritize identifying the most important learning items if the student is concentrating. By adjusting the method for identifying learning items according to the student's emotions, more appropriate learning items can be identified. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the student's facial expression data into the generative AI and cause the generative AI to estimate the student's emotions.

[0115] The generation unit can analyze the student's emotions and adjust the method for generating lesson content and assignments based on the analyzed emotions. For example, if the student is feeling stressed, the generation unit can prioritize generating simple assignments. Furthermore, if the student is relaxed, the generation unit can also generate detailed lesson content. Furthermore, if the student is concentrating, the generation unit can prioritize generating the most important lesson content and assignments. This allows the generation of more appropriate lesson content and assignments by adjusting the method for generating lesson content and assignments according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI. For example, the generation unit can input the student's facial expression data into the generation AI and have the generation AI estimate the student's emotions.

[0116] The packaging unit can analyze the student's emotions and prioritize the lesson content and assignments to be packaged based on the analyzed emotions. For example, if the student is feeling stressed, it prioritizes packaging of the most important lesson content and assignments. The packaging unit can also package detailed lesson content and assignments if the student is relaxed. Furthermore, if the student is concentrating, it can prioritize packaging of the most important lesson content and assignments. This allows important content to be packaged preferentially by prioritizing lesson content and assignments based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the packaging unit can be performed using, for example, an AI. For example, the packaging unit can input the student's facial expression data into the generation AI and have the generation AI estimate the student's emotions.

[0117] When inputting themes and units, the reception unit can prioritize inputting highly relevant themes based on the teacher's geographical location information. For example, if the teacher is in a specific area, the reception unit can prioritize inputting themes and units related to that area. In addition, if the teacher is traveling, the reception unit can also suggest optimal themes and units based on the teacher's current location. Furthermore, if the teacher is in a specific school, the reception unit can also input themes and units based on the school's curriculum. In this way, by taking the teacher's geographical location information into consideration, highly relevant themes and units can be prioritized. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the teacher's geographical location information data to the generation AI and cause the generation AI to suggest highly relevant themes.

[0118] When collecting grade data, the collection unit can filter the grade data based on the student's current learning situation and areas of interest. For example, the collection unit can prioritize collection of relevant grade data based on the student's current learning situation. The collection unit can also filter the grade data based on the student's areas of interest. Furthermore, the collection unit can exclude unnecessary grade data based on the student's learning situation and areas of interest. In this way, by filtering the grade data based on the student's learning situation and areas of interest, highly relevant data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the student's learning situation data to the generation AI and cause the generation AI to filter the grade data.

[0119] During analysis, the analysis unit can use different analysis algorithms depending on the student's learning style. For example, if the student has a visual learning style, the analysis unit can apply a visual analysis algorithm. Furthermore, if the student has an auditory learning style, the analysis unit can also apply an auditory analysis algorithm. Furthermore, if the student has a tactile learning style, the analysis unit can also apply a tactile analysis algorithm. By applying an analysis algorithm according to the student's learning style, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the student's learning style data into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0120] When generating lesson content and assignments, the generation unit can use different generation algorithms depending on the category of the learning item. For example, the generation unit can apply a generation algorithm dedicated to science to science learning items. The generation unit can also apply a generation algorithm dedicated to mathematics to mathematics learning items. The generation unit can also apply a generation algorithm dedicated to Japanese to Japanese learning items. In this way, by applying a generation algorithm according to the category of the learning item, appropriate lesson content and assignments can be generated. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input category data of the learning item into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0121] During packaging, the packaging unit can improve the accuracy of the package based on the interrelationships between the learning items. For example, the packaging unit generates an optimal package based on the interrelationships between the learning items. The packaging unit can also analyze the interrelationships between the learning items and improve the accuracy of the package. Furthermore, the packaging unit can also exclude unnecessary items based on the interrelationships between the learning items. This improves the accuracy of the package by taking the interrelationships between the learning items into consideration. Some or all of the above-mentioned processing in the packaging unit may be performed using AI, for example, or may be performed without using AI. For example, the packaging unit can input interrelationship data between the learning items into a generation AI and have the generation AI improve the accuracy of the package.

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

[0123] Step 1: The reception unit receives the specific theme or unit set by the teacher as input. For example, the reception unit provides an interface for inputting the theme or unit set by the teacher, and supports multiple input methods such as voice input and text input. Step 2: The collection unit collects the academic performance data for each student based on the information received by the reception unit. For example, the collection unit has the function of collecting and automatically updating academic performance data such as students' test scores, assignment grades, and attendance status. Step 3: The analysis unit analyzes the academic performance data collected by the collection unit and identifies the learning items that each student is lacking. For example, the analysis unit may use a generative AI to analyze the academic performance data, identify the learning items that are lacking, and perform a highly accurate analysis taking into account the student's learning history and learning style. Step 4: The generator generates lesson content and assignments based on the learning items identified by the analyzer. For example, the generator uses a generation AI to generate lesson content and assignments that are optimal for each student, adjusting the level of detail of the generated content depending on the importance and category of the learning items. Step 5: The packaging unit packages the lesson content and assignments generated by the generation unit. For example, the packaging unit generates the optimal lesson package for each student's grade and provides it as a service, customizing the package content taking into account the student's emotions and attribute information.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0129] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0151] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0154] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0161] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0164] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0166] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0167] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0168] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0169] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0170] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0171] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0172] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0174] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0177] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0179] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0180] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0181] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0185] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0186] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0187] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0188] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0189] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0190] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0192] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0193] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0194] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0195] [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives as input a specific theme or unit set by a teacher; a collection unit that collects grade data for each student based on the information received by the reception unit; an analysis unit that analyzes the performance data collected by the collection unit and identifies learning items that are lacking for each student; a generation unit that generates lesson contents and assignments based on the learning items identified by the analysis unit; a packaging unit that packages the lesson contents and assignments generated by the generation unit; A system characterized by:

2. The reception unit Analyze teacher emotions and adjust the theme and unit input method based on the analyzed emotions.

2. The system of claim 1.

3. The reception unit Analyzes teachers' past topic setting history and suggests appropriate input methods 2. The system of claim 1.

4. The reception unit When entering a topic or unit, filter based on the teacher's current teaching philosophy or goals 2. The system of claim 1.

5. The reception unit When entering themes and units, select the appropriate input method according to the teacher's input method.

2. The system of claim 1.

6. The reception unit Analyze teachers' emotions and prioritize themes and units to be input based on the analyzed emotions.

2. The system of claim 1.

7. The reception unit When entering themes and units, prioritize the most relevant themes based on the teacher's geographic location 2. The system of claim 1.

8. The reception unit When entering a topic or unit, analyzes teachers' social media activity and suggests related topics.

2. The system of claim 1.

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