System

Abstract

An object of a system according to an embodiment is to efficiently manage teacher's work and automate grade evaluation.SOLUTION: A system includes a registration part, a management part, an update part, a teaching material provision part, a proposal part, an evaluation part, and a security part. The registration part lists up the work of the teacher and registers it in the platform. The management unit manages the progress status of the work registered by the registration unit. The update unit updates the progress of the work managed by the management unit. The teaching material providing unit provides a teaching material for class preparation. The proposing section proposes the teaching material by the AI based on the teaching material provided by the teaching material providing section. The evaluation unit automates the result evaluation. The security unit controls access to the platform.SELECTED DRAWING: Figure 1

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] In conventional technology, teacher work management is done manually, and efficient progress management and automation of grade evaluation are not sufficiently implemented, leaving room for improvement.

[0005] The system according to the embodiment aims to efficiently manage the work of teachers and automate the evaluation of grades. [Means for solving the problem]

[0006] The system according to the embodiment includes a registration unit, a management unit, an update unit, a teaching material providing unit, a suggestion unit, an evaluation unit, and a security unit. The registration unit lists the teacher's work and registers it on the platform. The management unit manages the progress of the work registered by the registration unit. The update unit updates the progress of the work managed by the management unit. The teaching material providing unit provides teaching materials for lesson preparation. The suggestion unit uses AI to suggest teaching materials based on the teaching materials provided by the teaching material providing unit. The evaluation unit automates grade evaluation. The security unit controls access to the platform. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage the work of teachers and automate the evaluation of grades. [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 system according to an embodiment of the present invention is a work management system designed to eliminate long working hours for public school teachers. This system creates a detailed list of tasks performed by teachers, registers them on a platform, and manages their progress in real time. Teachers can update their work progress on the platform and share it with other teachers and administrators. Furthermore, the system provides support functions to reduce teachers' workloads. These functions include searching for and downloading teaching materials needed for lesson preparation, suggesting teaching materials using AI, automating grade evaluations, and providing templates for parental support. The system also enables meeting schedule management and automatic generation of meeting minutes. This allows the system to efficiently manage teachers' work and eliminate long working hours. For example, teachers can efficiently manage their own work and share information with other teachers and administrators. This reduces teachers' long working hours and improves work efficiency.

[0029] The work management system according to the embodiment includes a registration unit, a management unit, an update unit, a teaching material provision unit, a suggestion unit, an evaluation unit, and a security unit. The registration unit lists the tasks of teachers and registers them on the platform. For example, tasks such as lesson preparation, grade evaluation, and teaching material creation can be registered. The management unit manages the progress of the tasks registered by the registration unit. For example, it manages the degree of completion of tasks and compliance with deadlines in real time. The update unit updates the progress of tasks managed by the management unit. For example, teachers can report the progress of their tasks and share it with other teachers and administrators. The teaching material provision unit provides teaching materials necessary for lesson preparation. For example, it can search for and download teaching materials. The suggestion unit uses AI to suggest teaching materials necessary for lesson preparation. For example, AI suggests optimal teaching materials based on the lesson content. The evaluation unit automates grade evaluation. For example, AI automatically evaluates grades. The security unit controls access to the platform. For example, it manages the access rights of teachers and administrators and has data encryption and backup functions. As a result, the work management system according to the embodiment can efficiently manage the work of teachers and eliminate long working hours.

[0030] The teaching material providing unit can search for or download teaching materials. The teaching material providing unit, for example, searches for teaching materials. For example, a search algorithm can be used to quickly find the teaching materials needed by the teacher. The teaching material providing unit can also download teaching materials. For example, teaching materials can be downloaded according to the download format or file size. This diversifies the methods for providing teaching materials and makes lesson preparations by teachers more efficient.

[0031] The suggestion unit can use AI to suggest teaching materials necessary for lesson preparation. The suggestion unit, for example, uses AI to suggest teaching materials necessary for lesson preparation. For example, AI suggests optimal teaching materials based on the content of the lesson. AI can suggest teaching materials needed by teachers using machine learning algorithms and natural language processing technology. As a result, the use of AI improves the accuracy of teaching material suggestions.

[0032] The evaluation unit can automatically evaluate grades using AI. The evaluation unit, for example, automatically evaluates grades using AI. For example, AI uses a grading algorithm to automatically evaluate grades. AI can evaluate grades based on evaluation items and evaluation criteria. This reduces the burden on teachers by automating grade evaluation.

[0033] The security department can manage the access rights of teachers and administrators. The security department manages, for example, the access rights of teachers and administrators. For example, the security department can set access rights based on the authority level and the method of granting the authority. This improves the security of the system by managing the access rights.

[0034] The security unit may have a data encryption or backup function. The security unit, for example, performs data encryption. For example, the security unit may encrypt data using an encryption algorithm. The security unit may also have a data backup function. For example, the security unit may back up data depending on the backup frequency and storage location. This improves data security by encrypting or backing up data.

[0035] The management unit can manage meeting schedules and automatically generate minutes. The management unit, for example, manages meeting schedules. For example, it can manage meeting schedules based on how the schedules are entered and shared. The management unit can also automatically generate minutes. For example, it can automatically generate minutes using voice recognition technology. This makes the work of faculty more efficient by managing meeting schedules and automatically generating minutes.

[0036] The update unit allows faculty members to update the progress of their work and share it with other faculty members and administrators. The update unit allows, for example, faculty members to update the progress of their work. For example, the progress of their work can be updated based on the progress reporting method and update frequency. The update unit can also share the progress of their work with other faculty members and administrators. For example, the progress of their work can be shared based on the scope of information to be shared and the sharing method. This improves collaboration between faculty members by sharing the progress of their work.

[0037] The registration unit can analyze the teacher's past work history and select the optimal registration method. The registration unit, for example, analyzes the teacher's past work history. For example, it can analyze the past work history based on the type of work and the date and time the work was completed. The registration unit can also select the optimal registration method. For example, it can automatically display tasks that the teacher has frequently performed in the past as candidates. It can preferentially suggest registration methods (voice, text, etc.) that the teacher has used in the past. It can predict and suggest work to be performed in a specific time period based on the teacher's past work history. This makes work registration more efficient by selecting the optimal registration method based on the past work history.

[0038] When registering work, the registration unit can determine the priority of registration taking into account the faculty member's current workload. The registration unit, for example, considers the faculty member's current workload. For example, it can evaluate the faculty member's workload based on the working time and workload. The registration unit can also determine the priority of registration. For example, it can analyze the faculty member's current workload in real time and prioritize registering important work when the workload is low. When the faculty member is busy, it prioritizes registering easy work to reduce the burden. When the faculty member's workload is high, it automates registration and minimizes manual input. In this way, work priorities are appropriately determined by registration taking into account the faculty member's workload.

[0039] When registering work, the registration unit can select the optimal registration means according to the teacher's input method. The registration unit takes into account, for example, the teacher's input method. For example, the registration unit can select the optimal registration means based on the input method, such as voice input, text input, or image input. For example, if the teacher uses voice input, the work is registered using voice recognition technology. If the teacher uses text input, the work is registered using text analysis technology. If the teacher uses image input, the work is registered using image recognition technology. This makes work registration more efficient by selecting the registration means according to the teacher's input method.

[0040] When registering tasks, the registration unit can prioritize registering highly relevant tasks by taking into account the geographical location information of the teacher. The registration unit, for example, considers the geographical location information of the teacher. For example, the teacher's current location can be determined using GPS data or location information services. The registration unit can also prioritize registering highly relevant tasks. For example, if the teacher is in a specific location, the registration unit prioritizes registering tasks that should be done at that location. If the teacher is traveling, the registration unit prioritizes registering tasks that should be done at the teacher's destination. If the teacher is at home, the registration unit prioritizes registering tasks that should be done at home. In this way, highly relevant tasks are registered with a high priority by taking into account the geographical location information of the teacher.

[0041] The registration unit can analyze the social media activity of the teacher when registering a task and register related tasks. The registration unit, for example, analyzes the social media activity of the teacher. For example, it can analyze the social media activity of the teacher based on the content of posts and the frequency of activity. The registration unit can also register related tasks. For example, it can register related tasks based on information shared by the teacher on social media. It can analyze the content of the teacher's social media activity and suggest related tasks. It can register related tasks based on the activity of the teacher's friends on social media. In this way, related tasks are registered based on the teacher's social media activity.

[0042] The registration unit can customize the registration method by reflecting the teacher's past feedback when registering work. The registration unit, for example, reflects the teacher's past feedback. For example, it can reflect past feedback based on opinions and evaluation results from the teacher. The registration unit can also customize the registration method. For example, it can suggest the optimal registration method based on feedback provided by the teacher in the past. It can analyze the teacher's past feedback and improve the registration procedure. It can customize the registration interface by reflecting the teacher's feedback. As a result, the registration method that reflects the teacher's past feedback makes work registration more efficient.

[0043] When managing work, the management unit can refer to the teacher's past work history to select the optimal management method. The management unit, for example, refers to the teacher's past work history. For example, it can refer to past work history based on the type of work or the date and time the work was completed. The management unit can also select the optimal management method. For example, it can automatically display work that the teacher has frequently performed in the past as candidates. It can prioritize and suggest management methods (audio, text, etc.) that the teacher has used in the past. It can predict and suggest work to be performed during specific time periods based on the teacher's past work history. This makes work management more efficient by selecting the optimal management method based on past work history.

[0044] When managing work, the management department can determine management priorities by taking into account the faculty member's current workload. The management department, for example, considers the faculty member's current workload. For example, it can evaluate the faculty member's workload based on the working hours and workload. The management department can also determine management priorities. For example, it can analyze the faculty member's current workload in real time and prioritize management of important work when the workload is low. When the faculty member is busy, it can prioritize management of easy work to reduce the burden. When the faculty member's workload is high, it automates management and minimizes manual input. In this way, work priorities are appropriately determined through management that takes into account the faculty member's workload.

[0045] When managing work, the management unit can select the optimal management means according to the teacher's input method. The management unit takes into consideration, for example, the teacher's input method. For example, the management unit can select the optimal management means based on the input method, such as voice input, text input, or image input. For example, if the teacher uses voice input, the work is managed using voice recognition technology. If the teacher uses text input, the work is managed using text analysis technology. If the teacher uses image input, the work is managed using image recognition technology. This makes work management more efficient by selecting a management means according to the teacher's input method.

[0046] When managing work, the management department can prioritize highly relevant work by taking into account the geographical location information of the faculty member. The management department, for example, takes into account the geographical location information of the faculty member. For example, the current location of the faculty member can be determined using GPS data or location information services. The management department can also prioritize managing highly relevant work. For example, if the faculty member is in a specific location, the management department prioritizes managing work that should be done at that location. If the faculty member is traveling, the management department prioritizes managing work that should be done at the travel destination. If the faculty member is at home, the management department prioritizes managing work that should be done at home. In this way, highly relevant work is managed with priority by management that takes into account the geographical location information of the faculty member.

[0047] The management unit can analyze the social media activity of faculty members and manage related tasks when managing tasks. The management unit, for example, analyzes the social media activity of faculty members. For example, it can analyze the social media activity of faculty members based on the content of posts and the frequency of activity. The management unit can also manage related tasks. For example, it can manage related tasks based on information shared by faculty members on social media. It can analyze the content of the faculty members' social media activities and suggest related tasks. It can manage related tasks based on the activities of the faculty members' friends on social media. In this way, related tasks are managed based on the social media activity of the faculty members.

[0048] When managing work, the management unit can customize the management method by reflecting past feedback from teachers. The management unit, for example, reflects past feedback from teachers. For example, it can reflect past feedback based on opinions and evaluation results from teachers. The management unit can also customize the management method. For example, it can propose an optimal management method based on feedback provided by teachers in the past. It can analyze past feedback from teachers and improve management procedures. It can customize the management interface by reflecting teacher feedback. As a result, work management becomes more efficient through a management method that reflects past feedback from teachers.

[0049] When updating work, the update unit can refer to the teacher's past work history and select the optimal update method. The update unit, for example, refers to the teacher's past work history. For example, it can refer to past work history based on the type of work or the date and time the work was completed. The update unit can also select the optimal update method. For example, it can automatically display work that the teacher has frequently performed in the past as candidates. It can preferentially suggest update methods (audio, text, etc.) that the teacher has used in the past. It can predict and suggest work to be performed in a specific time period based on the teacher's past work history. This makes work updates more efficient by selecting the optimal update method based on the past work history.

[0050] When updating work, the update unit can determine the priority of updates by taking into account the faculty member's current workload. The update unit, for example, considers the faculty member's current workload. For example, it can evaluate the faculty member's workload based on the work time and workload. The update unit can also determine the priority of updates. For example, it can analyze the faculty member's current workload in real time and prioritize updating important work when the workload is low. When the faculty member is busy, it prioritizes updating simple work to reduce the burden. When the faculty member's workload is high, it automates updates and minimizes manual input. In this way, work priorities are appropriately determined by updates that take into account the faculty member's workload.

[0051] When updating work, the update unit can select the optimal update means according to the teacher's input method. The update unit takes into account, for example, the teacher's input method. For example, the update unit can select the optimal update means based on the input method, such as voice input, text input, or image input. For example, if the teacher uses voice input, the work is updated using voice recognition technology. If the teacher uses text input, the work is updated using text analysis technology. If the teacher uses image input, the work is updated using image recognition technology. This makes work updates more efficient by selecting the update means according to the teacher's input method.

[0052] When updating tasks, the update unit can prioritize updating highly relevant tasks by taking into account the geographical location information of the teacher. The update unit, for example, considers the geographical location information of the teacher. For example, the teacher's current location can be determined using GPS data or location information services. The update unit can also prioritize updating highly relevant tasks. For example, if the teacher is in a specific location, it prioritizes updating tasks that should be done at that location. If the teacher is traveling, it prioritizes updating tasks that should be done at the teacher's destination. If the teacher is at home, it prioritizes updating tasks that should be done at home. In this way, highly relevant tasks are prioritized by updates that take into account the geographical location information of the teacher.

[0053] The update unit can analyze the social media activity of the teacher and update related tasks when updating tasks. The update unit, for example, analyzes the social media activity of the teacher. For example, it can analyze the social media activity of the teacher based on the content of posts and the frequency of activity. The update unit can also update related tasks. For example, it can update related tasks based on information shared by the teacher on social media. It can analyze the content of the teacher's social media activity and suggest related tasks. It can update related tasks based on the activity of the teacher's friends on social media. In this way, related tasks are updated based on the teacher's social media activity.

[0054] The update unit can customize the update method by reflecting the teacher's past feedback when updating work. The update unit, for example, reflects the teacher's past feedback. For example, it can reflect past feedback based on opinions and evaluation results from the teacher. The update unit can also customize the update method. For example, it can propose an optimal update method based on feedback provided by the teacher in the past. It can analyze the teacher's past feedback and improve the update procedure. It can customize the update interface by reflecting the teacher's feedback. As a result, work updates are made more efficient using an update method that reflects the teacher's past feedback.

[0055] When providing teaching materials, the teaching material provision unit can select the optimal delivery method by referring to the teacher's past teaching material use history. The teaching material provision unit, for example, refers to the teacher's past teaching material use history. For example, the past teaching material use history can be referenced based on the type of teaching material used and the date and time of use. The teaching material provision unit can also select the optimal delivery method. For example, teaching materials that the teacher has frequently used in the past can be automatically displayed as candidates. Delivery methods (audio, text, etc.) that the teacher has used in the past can be preferentially suggested. Teaching materials that will be used in a specific time period can be predicted and suggested based on the teacher's past teaching material use history. In this way, the delivery of teaching materials can be made more efficient by selecting the optimal delivery method based on the teacher's past teaching material use history.

[0056] When providing teaching materials, the teaching material providing unit can determine the priority of provision by taking into consideration the teacher's current lesson content. The teaching material providing unit, for example, considers the teacher's current lesson content. For example, the teacher's current lesson content can be considered based on the lesson theme and the progress of the lesson. The teaching material providing unit can also determine the priority of provision. For example, the teaching material providing unit can analyze the teacher's current lesson content in real time and provide highly relevant teaching materials preferentially. When the teacher is busy, teaching materials that are easily available can be provided preferentially. When the teacher's lesson content changes, related teaching materials are automatically provided. In this way, highly relevant teaching materials are provided preferentially by providing teaching materials that take into consideration the teacher's lesson content.

[0057] When providing teaching materials, the teaching material providing unit can select the optimal providing means according to the teacher's input method. The teaching material providing unit takes into account, for example, the teacher's input method. For example, the optimal providing means can be selected based on the input method, such as voice input, text input, or image input. For example, if the teacher uses voice input, the teaching material is provided using voice recognition technology. If the teacher uses text input, the teaching material is provided using text analysis technology. If the teacher uses image input, the teaching material is provided using image recognition technology. This makes the provision of teaching materials more efficient by selecting a providing means according to the teacher's input method.

[0058] When providing teaching materials, the teaching material providing unit can provide highly relevant teaching materials preferentially by taking into account the geographical location information of the teacher. The teaching material providing unit, for example, takes into account the geographical location information of the teacher. For example, the teacher's current location can be determined using GPS data or location information services. The teaching material providing unit can also provide highly relevant teaching materials preferentially. For example, if the teacher is in a specific location, teaching materials that should be used in that location are provided preferentially. If the teacher is traveling, teaching materials that should be used at the teacher's destination are provided preferentially. If the teacher is at home, teaching materials that should be used at home are provided preferentially. In this way, highly relevant teaching materials are provided preferentially by providing teaching materials that take into account the geographical location information of the teacher.

[0059] The teaching material providing unit can analyze the social media activity of the teacher and provide related teaching materials when providing teaching materials. The teaching material providing unit, for example, analyzes the social media activity of the teacher. For example, the teaching material providing unit can analyze the social media activity of the teacher based on the content of posts and the frequency of activity. The teaching material providing unit can also provide related teaching materials. For example, the teaching material providing unit can provide related teaching materials based on information shared by the teacher on social media. The teaching material providing unit can analyze the content of the teacher's social media activity and suggest related teaching materials. The teaching material providing unit can provide related teaching materials based on the activity of the teacher's friends on social media. In this way, related teaching materials are provided based on the teacher's social media activity.

[0060] When providing teaching materials, the teaching material providing unit can customize the method of providing teaching materials by reflecting the teacher's past feedback. The teaching material providing unit, for example, reflects the teacher's past feedback. For example, it can reflect past feedback based on the teacher's opinions and evaluation results. The teaching material providing unit can also customize the method of providing teaching materials. For example, it can propose the optimal method of providing teaching materials based on feedback provided by the teacher in the past. It can analyze the teacher's past feedback and improve the provision procedure. It can customize the provision interface by reflecting the teacher's feedback. As a result, the provision of teaching materials becomes more efficient through a method of providing teaching materials that reflects the teacher's past feedback.

[0061] When proposing teaching materials, the suggestion unit can select the optimal suggestion method by referring to the teacher's past teaching material usage history. The suggestion unit, for example, refers to the teacher's past teaching material usage history. For example, the past teaching material usage history can be referenced based on the type of teaching material used and the date and time of use. The suggestion unit can also select the optimal suggestion method. For example, teaching materials that the teacher has frequently used in the past can be automatically displayed as candidates. The suggestion unit gives priority to suggesting proposal methods (audio, text, etc.) that the teacher has used in the past. The teaching materials that will be used in a specific time period can be predicted and suggested based on the teacher's past teaching material usage history. In this way, the optimal suggestion method can be selected based on the teacher's past teaching material usage history, making teaching material suggestions more efficient.

[0062] When suggesting teaching materials, the suggestion unit can determine the priority of suggestions by taking into account the teacher's current lesson content. The suggestion unit, for example, considers the teacher's current lesson content. For example, the suggestion unit can consider the teacher's current lesson content based on the lesson theme and the progress of the lesson. The suggestion unit can also determine the priority of suggestions. For example, the suggestion unit can analyze the teacher's current lesson content in real time and prioritize suggesting highly relevant teaching materials. When the teacher is busy, the suggestion unit can prioritize suggesting easily accessible teaching materials. When the teacher's lesson content changes, the suggestion unit can automatically suggest related teaching materials. In this way, highly relevant teaching materials are prioritized by suggestions that take into account the teacher's lesson content.

[0063] When suggesting teaching materials, the suggestion unit can select the optimal suggestion means according to the teacher's input method. The suggestion unit takes into account, for example, the teacher's input method. For example, the suggestion unit can select the optimal suggestion means based on the input method, such as voice input, text input, or image input. For example, if the teacher uses voice input, teaching materials are suggested using voice recognition technology. If the teacher uses text input, teaching materials are suggested using text analysis technology. If the teacher uses image input, teaching materials are suggested using image recognition technology. This makes it possible to efficiently suggest teaching materials by selecting a suggestion means according to the teacher's input method.

[0064] When suggesting teaching materials, the suggestion unit can prioritize suggesting highly relevant teaching materials by taking into account the geographical location information of the teacher. The suggestion unit, for example, considers the geographical location information of the teacher. For example, the teacher's current location can be determined using GPS data or location information services. The suggestion unit can also prioritize suggesting highly relevant teaching materials. For example, if the teacher is in a specific location, the suggestion unit prioritizes suggesting teaching materials that should be used in that location. If the teacher is traveling, the suggestion unit prioritizes suggesting teaching materials that should be used at the teacher's destination. If the teacher is at home, the suggestion unit prioritizes suggesting teaching materials that should be used at home. In this way, highly relevant teaching materials are prioritized by suggestions that take into account the geographical location information of the teacher.

[0065] When suggesting teaching materials, the suggestion unit can analyze the social media activity of the teacher and suggest related teaching materials. The suggestion unit, for example, analyzes the social media activity of the teacher. For example, the suggestion unit can analyze the social media activity of the teacher based on the content of posts and the frequency of activity. The suggestion unit can also suggest related teaching materials. For example, the suggestion unit can suggest related teaching materials based on information shared by the teacher on social media. The suggestion unit can analyze the content of the teacher's social media activity and suggest related teaching materials. The suggestion unit can suggest related teaching materials based on the activity of the teacher's friends on social media. In this way, related teaching materials are suggested based on the teacher's social media activity.

[0066] When proposing teaching materials, the suggestion unit can customize the suggestion method by reflecting the teacher's past feedback. The suggestion unit, for example, reflects the teacher's past feedback. For example, it can reflect past feedback based on opinions and evaluation results from the teacher. The suggestion unit can also customize the suggestion method. For example, it can propose an optimal suggestion method based on feedback provided by the teacher in the past. It can analyze the teacher's past feedback and improve the suggestion procedure. It can customize the suggestion interface by reflecting the teacher's feedback. As a result, the suggestion method that reflects the teacher's past feedback makes teaching material suggestions more efficient.

[0067] When evaluating grades, the evaluation unit can refer to the teacher's past evaluation history to select the optimal evaluation method. The evaluation unit, for example, refers to the teacher's past evaluation history. For example, it can refer to past evaluation history based on the type of grade evaluated and the evaluation date and time. The evaluation unit can also select the optimal evaluation method. For example, it can automatically display evaluation methods that the teacher has frequently used in the past as candidates. It can prioritize and suggest evaluation criteria that the teacher has used in the past. It can predict and suggest the evaluation method to be used at a specific time period based on the teacher's past evaluation history. This makes grade evaluation more efficient by selecting the optimal evaluation method based on past evaluation history.

[0068] The evaluation department can determine the priority of evaluation by taking into account the faculty member's current workload when evaluating grades. The evaluation department, for example, considers the faculty member's current workload. For example, it can evaluate the faculty member's workload based on the working hours and workload. The evaluation department can also determine the priority of evaluation. For example, it can analyze the faculty member's current workload in real time and prioritize important evaluations when the workload is low. When the faculty member is busy, it can prioritize simple evaluations to reduce the burden. When the faculty member's workload is high, it can automate evaluations and minimize manual input. In this way, the priority of grades can be appropriately determined through evaluations that take the faculty member's workload into account.

[0069] When evaluating grades, the evaluation unit can select the optimal evaluation means according to the teacher's input method. The evaluation unit takes into account, for example, the teacher's input method. For example, the evaluation unit can select the optimal evaluation means based on the input method, such as voice input, text input, or image input. For example, if the teacher uses voice input, the grades are evaluated using voice recognition technology. If the teacher uses text input, the grades are evaluated using text analysis technology. If the teacher uses image input, the grades are evaluated using image recognition technology. This makes grade evaluation more efficient by selecting an evaluation means according to the teacher's input method.

[0070] When evaluating grades, the evaluation unit can prioritize highly relevant grades by taking into account the geographical location information of the instructor. The evaluation unit, for example, considers the geographical location information of the instructor. For example, the instructor's current location can be determined using GPS data or location information services. The evaluation unit can also prioritize highly relevant grades. For example, if the instructor is in a specific location, the evaluation that should be done at that location is prioritized. If the instructor is traveling, the evaluation that should be done at the instructor's destination is prioritized. If the instructor is at home, the evaluation that should be done at home is prioritized. In this way, highly relevant grades are prioritized by evaluation that takes into account the instructor's geographical location information.

[0071] The evaluation unit can analyze the social media activity of the faculty member and evaluate the related grades when evaluating grades. The evaluation unit, for example, analyzes the social media activity of the faculty member. For example, the evaluation unit can analyze the social media activity of the faculty member based on the content of posts and the frequency of activities. The evaluation unit can also evaluate the related grades. For example, the evaluation unit can evaluate the related grades based on the information the faculty member shared on social media. The evaluation unit can analyze the content of the faculty member's social media activity and suggest related grades. The evaluation unit can evaluate the related grades based on the activity of the faculty member's friends on social media. In this way, the evaluation unit can evaluate the related grades based on the faculty member's social media activity.

[0072] The evaluation unit can customize the evaluation method by reflecting the teacher's past feedback when evaluating grades. The evaluation unit, for example, reflects the teacher's past feedback. For example, it can reflect past feedback based on the teacher's opinions and evaluation results. The evaluation unit can also customize the evaluation method. For example, it can propose the optimal evaluation method based on feedback provided by the teacher in the past. It can analyze the teacher's past feedback and improve the evaluation procedure. It can customize the evaluation interface by reflecting the teacher's feedback. As a result, the evaluation method that reflects the teacher's past feedback makes grade evaluation more efficient.

[0073] When controlling access, the security department can refer to the faculty member's past access history to select the optimal control method. The security department, for example, refers to the faculty member's past access history. For example, it can refer to past access history based on the date and time of access and the resources accessed. The security department can also select the optimal control method. For example, it can automatically display data that the faculty member has frequently accessed in the past as a candidate. It can prioritize and suggest access control methods that the faculty member has used in the past. It can predict and suggest access control to be performed during specific time periods based on the faculty member's past access history. This makes access control more efficient by selecting the optimal control method based on past access history.

[0074] When controlling access, the security department can determine the priority of control by taking into account the faculty member's current work content. The security department can, for example, take into account the faculty member's current work content. For example, the security department can take into account the faculty member's current work content based on the type of work and the progress of the work. The security department can also determine the priority of control. For example, the security department can analyze the faculty member's current work content in real time and prioritize control of access to important data. When the faculty member is busy, the security department can prioritize control of data that is easily accessible. If the faculty member's work content changes, the security department can automatically adjust access control to related data. As a result, access priorities are appropriately determined through control that takes into account the faculty member's work content.

[0075] When controlling access, the security department can select the optimal control means according to the teacher's input method. The security department takes into consideration, for example, the teacher's input method. For example, the security department can select the optimal control means based on the input method, such as voice input, text input, or image input. For example, if the teacher uses voice input, access control is performed using voice recognition technology. If the teacher uses text input, access control is performed using text analysis technology. If the teacher uses image input, access control is performed using image recognition technology. This makes access control more efficient by selecting a control means according to the teacher's input method.

[0076] When controlling access, the security department can prioritize highly relevant access by taking into account the geographical location information of the teacher. The security department, for example, takes into account the geographical location information of the teacher. For example, the current location of the teacher can be determined using GPS data or location information services. The security department can also prioritize control of highly relevant access. For example, if the teacher is in a specific location, access control that should be performed at that location is prioritized. If the teacher is traveling, access control that should be performed at the teacher's destination is prioritized. If the teacher is at home, access control that should be performed at home is prioritized. In this way, highly relevant access is prioritized by control that takes into account the geographical location information of the teacher.

[0077] When controlling access, the security department can analyze the social media activity of faculty members and control related access. The security department, for example, analyzes the social media activity of faculty members. For example, the security department can analyze the social media activity of faculty members based on the content of posts and the frequency of activity. The security department can also control related access. For example, the security department can control related access based on information shared by faculty members on social media. The security department can analyze the content of the faculty members' social media activities and suggest related access. The security department can control related access based on the activities of the faculty members' friends on social media. In this way, related access is controlled based on the social media activity of the faculty members.

[0078] When controlling access, the security department can customize the control method by reflecting past feedback from teachers. For example, the security department reflects past feedback from teachers. For example, it can reflect past feedback based on opinions and evaluation results from teachers. The security department can also customize the control method. For example, it can propose the optimal control method based on feedback provided by teachers in the past. It can analyze past feedback from teachers and improve control procedures. It can customize the control interface by reflecting teacher feedback. As a result, access control becomes more efficient using a control method that reflects past feedback from teachers.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The teaching material providing unit can provide highly relevant teaching materials preferentially, taking into account the geographical location information of the teacher. For example, if the teacher is in a specific location, teaching materials that should be used at that location are provided preferentially. If the teacher is traveling, teaching materials that should be used at the teacher's destination are provided preferentially. If the teacher is at home, teaching materials that should be used at home are provided preferentially. In this way, highly relevant teaching materials are provided preferentially by taking into account the geographical location information of the teacher.

[0081] When evaluating grades, the evaluation department can refer to the teacher's past evaluation history to select the optimal evaluation method. For example, evaluation methods that the teacher has frequently used in the past can be automatically displayed as candidates. Evaluation criteria that the teacher has used in the past can be prioritized. The evaluation method to be used at a specific time period can be predicted and proposed based on the teacher's past evaluation history. This makes grade evaluation more efficient by selecting the optimal evaluation method based on past evaluation history.

[0082] The administration department can determine management priorities by taking into account the current workload of faculty members. For example, it can analyze faculty members' current workloads in real time and prioritize important tasks when the workload is low. When faculty members are busy, it can prioritize easy tasks to reduce the workload. When faculty members' workloads are high, it can automate management and minimize manual input. This allows for appropriate prioritization of tasks through management that takes into account the workload of faculty members.

[0083] The teaching material provision unit can select the optimal delivery method by referencing the teacher's past teaching material usage history. For example, teaching materials that the teacher has frequently used in the past are automatically displayed as candidates. Delivery methods (audio, text, etc.) that the teacher has used in the past are given priority. Based on the teacher's past teaching material usage history, the teaching materials that will be used at specific times are predicted and proposed. This makes the delivery of teaching materials more efficient by selecting the optimal delivery method based on the teacher's past teaching material usage history.

[0084] The suggestion unit can determine the priority of suggestions by taking into account the teacher's current lesson content. For example, it can analyze the teacher's current lesson content in real time and prioritize suggesting highly relevant teaching materials. When the teacher is busy, it can prioritize suggesting easily accessible teaching materials. When the teacher's lesson content changes, it can automatically suggest related teaching materials. As a result, highly relevant teaching materials are prioritized by suggestions that take into account the teacher's lesson content.

[0085] The processing flow of the first embodiment will be briefly explained below.

[0086] Step 1: The registration department lists the teacher's tasks and registers them on the platform. For example, tasks such as lesson preparation, grading, and teaching material creation can be registered. Step 2: The management unit manages the progress of the work registered by the registration unit, for example, managing the degree of completion of the work and the compliance with deadlines in real time. Step 3: The update section updates the progress of the work managed by the management section. For example, a teacher can report the progress of the work and share it with other teachers and administrators. Step 4: The teaching materials provider provides the teaching materials necessary for lesson preparation. For example, the teaching materials can be searched and downloaded. Step 5: The suggestion unit uses AI to suggest the learning materials needed for lesson preparation. For example, the AI ​​suggests the most suitable learning materials based on the lesson content. Step 6: The evaluation department automates the evaluation of grades. For example, AI automatically evaluates grades. Step 7: The security department controls access to the platform, including managing access rights for teachers and administrators, and providing data encryption and backup functions.

[0087] (Example 2) A system according to an embodiment of the present invention is a work management system designed to eliminate long working hours for public school teachers. This system creates a detailed list of tasks performed by teachers, registers them on a platform, and manages their progress in real time. Teachers can update their work progress on the platform and share it with other teachers and administrators. Furthermore, the system provides support functions to reduce teachers' workloads. These functions include searching for and downloading teaching materials needed for lesson preparation, suggesting teaching materials using AI, automating grade evaluations, and providing templates for parental support. The system also enables meeting schedule management and automatic generation of meeting minutes. This allows the system to efficiently manage teachers' work and eliminate long working hours. For example, teachers can efficiently manage their own work and share information with other teachers and administrators. This reduces teachers' long working hours and improves work efficiency.

[0088] The work management system according to the embodiment includes a registration unit, a management unit, an update unit, a teaching material provision unit, a suggestion unit, an evaluation unit, and a security unit. The registration unit lists the tasks of teachers and registers them on the platform. For example, tasks such as lesson preparation, grade evaluation, and teaching material creation can be registered. The management unit manages the progress of the tasks registered by the registration unit. For example, it manages the degree of completion of tasks and compliance with deadlines in real time. The update unit updates the progress of tasks managed by the management unit. For example, teachers can report the progress of their tasks and share it with other teachers and administrators. The teaching material provision unit provides teaching materials necessary for lesson preparation. For example, it can search for and download teaching materials. The suggestion unit uses AI to suggest teaching materials necessary for lesson preparation. For example, AI suggests optimal teaching materials based on the lesson content. The evaluation unit automates grade evaluation. For example, AI automatically evaluates grades. The security unit controls access to the platform. For example, it manages the access rights of teachers and administrators and has data encryption and backup functions. As a result, the work management system according to the embodiment can efficiently manage the work of teachers and eliminate long working hours.

[0089] The teaching material providing unit can search for or download teaching materials. The teaching material providing unit, for example, searches for teaching materials. For example, a search algorithm can be used to quickly find the teaching materials needed by the teacher. The teaching material providing unit can also download teaching materials. For example, teaching materials can be downloaded according to the download format or file size. This diversifies the methods for providing teaching materials and makes lesson preparations by teachers more efficient.

[0090] The suggestion unit can use AI to suggest teaching materials necessary for lesson preparation. The suggestion unit, for example, uses AI to suggest teaching materials necessary for lesson preparation. For example, AI suggests optimal teaching materials based on the content of the lesson. AI can suggest teaching materials needed by teachers using machine learning algorithms and natural language processing technology. As a result, the use of AI improves the accuracy of teaching material suggestions.

[0091] The evaluation unit can automatically evaluate grades using AI. The evaluation unit, for example, automatically evaluates grades using AI. For example, AI uses a grading algorithm to automatically evaluate grades. AI can evaluate grades based on evaluation items and evaluation criteria. This reduces the burden on teachers by automating grade evaluation.

[0092] The security department can manage the access rights of teachers and administrators. The security department manages, for example, the access rights of teachers and administrators. For example, the security department can set access rights based on the authority level and the method of granting the authority. This improves the security of the system by managing the access rights.

[0093] The security unit may have a data encryption or backup function. The security unit, for example, performs data encryption. For example, the security unit may encrypt data using an encryption algorithm. The security unit may also have a data backup function. For example, the security unit may back up data depending on the backup frequency and storage location. This improves data security by encrypting or backing up data.

[0094] The management unit can manage meeting schedules and automatically generate minutes. The management unit, for example, manages meeting schedules. For example, it can manage meeting schedules based on how the schedules are entered and shared. The management unit can also automatically generate minutes. For example, it can automatically generate minutes using voice recognition technology. This makes the work of faculty more efficient by managing meeting schedules and automatically generating minutes.

[0095] The update unit allows faculty members to update the progress of their work and share it with other faculty members and administrators. The update unit allows, for example, faculty members to update the progress of their work. For example, the progress of their work can be updated based on the progress reporting method and update frequency. The update unit can also share the progress of their work with other faculty members and administrators. For example, the progress of their work can be shared based on the scope of information to be shared and the sharing method. This improves collaboration between faculty members by sharing the progress of their work.

[0096] The registration unit can estimate the teacher's emotions and adjust the work registration method based on the estimated teacher's emotions. The registration unit, for example, estimates the teacher's emotions. For example, the teacher's emotions can be estimated using an emotion recognition algorithm. The registration unit can also adjust the work registration method based on the estimated teacher's emotions. For example, if the teacher is feeling stressed, a simple interface can be provided to minimize the registration procedure. If the teacher is relaxed, detailed registration options can be provided and a customizable registration method can be suggested. If the teacher is in a hurry, voice input can be prioritized to enable work to be registered quickly. As a result, the registration method can be adjusted according to the teacher's emotions, allowing work to be registered smoothly.

[0097] The registration unit can analyze the teacher's past work history and select the optimal registration method. The registration unit, for example, analyzes the teacher's past work history. For example, it can analyze the past work history based on the type of work and the date and time the work was completed. The registration unit can also select the optimal registration method. For example, it can automatically display tasks that the teacher has frequently performed in the past as candidates. It can preferentially suggest registration methods (voice, text, etc.) that the teacher has used in the past. It can predict and suggest work to be performed in a specific time period based on the teacher's past work history. This makes work registration more efficient by selecting the optimal registration method based on the past work history.

[0098] When registering work, the registration unit can determine the priority of registration taking into account the faculty member's current workload. The registration unit, for example, considers the faculty member's current workload. For example, it can evaluate the faculty member's workload based on the working time and workload. The registration unit can also determine the priority of registration. For example, it can analyze the faculty member's current workload in real time and prioritize registering important work when the workload is low. When the faculty member is busy, it prioritizes registering easy work to reduce the burden. When the faculty member's workload is high, it automates registration and minimizes manual input. In this way, work priorities are appropriately determined by registration taking into account the faculty member's workload.

[0099] When registering work, the registration unit can select the optimal registration means according to the teacher's input method. The registration unit takes into account, for example, the teacher's input method. For example, the registration unit can select the optimal registration means based on the input method, such as voice input, text input, or image input. For example, if the teacher uses voice input, the work is registered using voice recognition technology. If the teacher uses text input, the work is registered using text analysis technology. If the teacher uses image input, the work is registered using image recognition technology. This makes work registration more efficient by selecting the registration means according to the teacher's input method.

[0100] The registration unit can estimate the teacher's emotions and determine the priority of tasks to be registered based on the estimated teacher's emotions. The registration unit, for example, estimates the teacher's emotions. For example, the registration unit can estimate the teacher's emotions using an emotion recognition algorithm. The registration unit can also determine the priority of tasks to be registered based on the estimated teacher's emotions. For example, if the teacher is feeling stressed, tasks of low importance are registered with priority. If the teacher is relaxed, tasks of high importance are registered with priority. If the teacher is in a hurry, tasks that can be completed quickly are registered with priority. In this way, task registration can be performed smoothly by determining the priority of tasks according to the teacher's emotions.

[0101] When registering tasks, the registration unit can prioritize registering highly relevant tasks by taking into account the geographical location information of the teacher. The registration unit, for example, considers the geographical location information of the teacher. For example, the teacher's current location can be determined using GPS data or location information services. The registration unit can also prioritize registering highly relevant tasks. For example, if the teacher is in a specific location, the registration unit prioritizes registering tasks that should be done at that location. If the teacher is traveling, the registration unit prioritizes registering tasks that should be done at the teacher's destination. If the teacher is at home, the registration unit prioritizes registering tasks that should be done at home. In this way, highly relevant tasks are registered with a high priority by taking into account the geographical location information of the teacher.

[0102] The registration unit can analyze the social media activity of the teacher when registering a task and register related tasks. The registration unit, for example, analyzes the social media activity of the teacher. For example, it can analyze the social media activity of the teacher based on the content of posts and the frequency of activity. The registration unit can also register related tasks. For example, it can register related tasks based on information shared by the teacher on social media. It can analyze the content of the teacher's social media activity and suggest related tasks. It can register related tasks based on the activity of the teacher's friends on social media. In this way, related tasks are registered based on the teacher's social media activity.

[0103] The registration unit can customize the registration method by reflecting the teacher's past feedback when registering work. The registration unit, for example, reflects the teacher's past feedback. For example, it can reflect past feedback based on opinions and evaluation results from the teacher. The registration unit can also customize the registration method. For example, it can suggest the optimal registration method based on feedback provided by the teacher in the past. It can analyze the teacher's past feedback and improve the registration procedure. It can customize the registration interface by reflecting the teacher's feedback. As a result, the registration method that reflects the teacher's past feedback makes work registration more efficient.

[0104] The management unit can estimate the teacher's emotions and adjust the work management method based on the estimated teacher's emotions. The management unit, for example, estimates the teacher's emotions. For example, the management unit can estimate the teacher's emotions using an emotion recognition algorithm. The management unit can also adjust the work management method based on the estimated teacher's emotions. For example, if the teacher is feeling stressed, a simple management interface can be provided to minimize management procedures. If the teacher is relaxed, detailed management options can be provided and a customizable management method can be suggested. If the teacher is in a hurry, voice input can be prioritized to enable quick work management. As a result, work management can be smoother by adjusting the management method according to the teacher's emotions.

[0105] When managing work, the management unit can refer to the teacher's past work history to select the optimal management method. The management unit, for example, refers to the teacher's past work history. For example, it can refer to past work history based on the type of work or the date and time the work was completed. The management unit can also select the optimal management method. For example, it can automatically display work that the teacher has frequently performed in the past as candidates. It can prioritize and suggest management methods (audio, text, etc.) that the teacher has used in the past. It can predict and suggest work to be performed during specific time periods based on the teacher's past work history. This makes work management more efficient by selecting the optimal management method based on past work history.

[0106] When managing work, the management department can determine management priorities by taking into account the faculty member's current workload. The management department, for example, considers the faculty member's current workload. For example, it can evaluate the faculty member's workload based on the working hours and workload. The management department can also determine management priorities. For example, it can analyze the faculty member's current workload in real time and prioritize management of important work when the workload is low. When the faculty member is busy, it can prioritize management of easy work to reduce the burden. When the faculty member's workload is high, it automates management and minimizes manual input. In this way, work priorities are appropriately determined through management that takes into account the faculty member's workload.

[0107] When managing work, the management unit can select the optimal management means according to the teacher's input method. The management unit takes into consideration, for example, the teacher's input method. For example, the management unit can select the optimal management means based on the input method, such as voice input, text input, or image input. For example, if the teacher uses voice input, the work is managed using voice recognition technology. If the teacher uses text input, the work is managed using text analysis technology. If the teacher uses image input, the work is managed using image recognition technology. This makes work management more efficient by selecting a management means according to the teacher's input method.

[0108] The management unit can estimate the teacher's emotions and determine the priority of tasks to be managed based on the estimated teacher's emotions. The management unit, for example, estimates the teacher's emotions. For example, the management unit can estimate the teacher's emotions using an emotion recognition algorithm. The management unit can also determine the priority of tasks to be managed based on the estimated teacher's emotions. For example, if the teacher is feeling stressed, it prioritizes tasks with low importance. If the teacher is relaxed, it prioritizes tasks with high importance. If the teacher is in a hurry, it prioritizes tasks that can be completed quickly. In this way, task prioritization according to the teacher's emotions allows for smooth task management.

[0109] When managing work, the management department can prioritize highly relevant work by taking into account the geographical location information of the faculty member. The management department, for example, takes into account the geographical location information of the faculty member. For example, the current location of the faculty member can be determined using GPS data or location information services. The management department can also prioritize managing highly relevant work. For example, if the faculty member is in a specific location, the management department prioritizes managing work that should be done at that location. If the faculty member is traveling, the management department prioritizes managing work that should be done at the travel destination. If the faculty member is at home, the management department prioritizes managing work that should be done at home. In this way, highly relevant work is managed with priority by management that takes into account the geographical location information of the faculty member.

[0110] The management unit can analyze the social media activity of faculty members and manage related tasks when managing tasks. The management unit, for example, analyzes the social media activity of faculty members. For example, it can analyze the social media activity of faculty members based on the content of posts and the frequency of activity. The management unit can also manage related tasks. For example, it can manage related tasks based on information shared by faculty members on social media. It can analyze the content of the faculty members' social media activities and suggest related tasks. It can manage related tasks based on the activities of the faculty members' friends on social media. In this way, related tasks are managed based on the social media activity of the faculty members.

[0111] When managing work, the management unit can customize the management method by reflecting past feedback from teachers. The management unit, for example, reflects past feedback from teachers. For example, it can reflect past feedback based on opinions and evaluation results from teachers. The management unit can also customize the management method. For example, it can propose an optimal management method based on feedback provided by teachers in the past. It can analyze past feedback from teachers and improve management procedures. It can customize the management interface by reflecting teacher feedback. As a result, work management becomes more efficient through a management method that reflects past feedback from teachers.

[0112] The update unit can estimate the teacher's emotions and adjust the work update method based on the estimated teacher's emotions. The update unit, for example, estimates the teacher's emotions. For example, the teacher's emotions can be estimated using an emotion recognition algorithm. The update unit can also adjust the work update method based on the estimated teacher's emotions. For example, if the teacher is feeling stressed, a simple update interface is provided to minimize the update procedure. If the teacher is relaxed, detailed update options are provided and a customizable update method is suggested. If the teacher is in a hurry, voice input is prioritized to enable quick work update. As a result, work updates can be smoothly performed by adjusting the update method according to the teacher's emotions.

[0113] When updating work, the update unit can refer to the teacher's past work history and select the optimal update method. The update unit, for example, refers to the teacher's past work history. For example, it can refer to past work history based on the type of work or the date and time the work was completed. The update unit can also select the optimal update method. For example, it can automatically display work that the teacher has frequently performed in the past as candidates. It can preferentially suggest update methods (audio, text, etc.) that the teacher has used in the past. It can predict and suggest work to be performed in a specific time period based on the teacher's past work history. This makes work updates more efficient by selecting the optimal update method based on the past work history.

[0114] When updating work, the update unit can determine the priority of updates by taking into account the faculty member's current workload. The update unit, for example, considers the faculty member's current workload. For example, it can evaluate the faculty member's workload based on the work time and workload. The update unit can also determine the priority of updates. For example, it can analyze the faculty member's current workload in real time and prioritize updating important work when the workload is low. When the faculty member is busy, it prioritizes updating simple work to reduce the burden. When the faculty member's workload is high, it automates updates and minimizes manual input. In this way, work priorities are appropriately determined by updates that take into account the faculty member's workload.

[0115] When updating work, the update unit can select the optimal update means according to the teacher's input method. The update unit takes into account, for example, the teacher's input method. For example, the update unit can select the optimal update means based on the input method, such as voice input, text input, or image input. For example, if the teacher uses voice input, the work is updated using voice recognition technology. If the teacher uses text input, the work is updated using text analysis technology. If the teacher uses image input, the work is updated using image recognition technology. This makes work updates more efficient by selecting the update means according to the teacher's input method.

[0116] The updating unit can estimate the teacher's emotions and determine the priority of tasks to be updated based on the estimated teacher's emotions. The updating unit, for example, estimates the teacher's emotions. For example, the teacher's emotions can be estimated using an emotion recognition algorithm. The updating unit can also determine the priority of tasks to be updated based on the estimated teacher's emotions. For example, if the teacher is feeling stressed, tasks with low importance are updated with priority. If the teacher is relaxed, tasks with high importance are updated with priority. If the teacher is in a hurry, tasks that can be completed quickly are updated with priority. In this way, task updating can be performed smoothly by determining the priority of tasks according to the teacher's emotions.

[0117] When updating tasks, the update unit can prioritize updating highly relevant tasks by taking into account the geographical location information of the teacher. The update unit, for example, considers the geographical location information of the teacher. For example, the teacher's current location can be determined using GPS data or location information services. The update unit can also prioritize updating highly relevant tasks. For example, if the teacher is in a specific location, it prioritizes updating tasks that should be done at that location. If the teacher is traveling, it prioritizes updating tasks that should be done at the teacher's destination. If the teacher is at home, it prioritizes updating tasks that should be done at home. In this way, highly relevant tasks are prioritized by updates that take into account the geographical location information of the teacher.

[0118] The update unit can analyze the social media activity of the teacher and update related tasks when updating tasks. The update unit, for example, analyzes the social media activity of the teacher. For example, it can analyze the social media activity of the teacher based on the content of posts and the frequency of activity. The update unit can also update related tasks. For example, it can update related tasks based on information shared by the teacher on social media. It can analyze the content of the teacher's social media activity and suggest related tasks. It can update related tasks based on the activity of the teacher's friends on social media. In this way, related tasks are updated based on the teacher's social media activity.

[0119] The update unit can customize the update method by reflecting the teacher's past feedback when updating work. The update unit, for example, reflects the teacher's past feedback. For example, it can reflect past feedback based on opinions and evaluation results from the teacher. The update unit can also customize the update method. For example, it can propose an optimal update method based on feedback provided by the teacher in the past. It can analyze the teacher's past feedback and improve the update procedure. It can customize the update interface by reflecting the teacher's feedback. As a result, work updates are made more efficient using an update method that reflects the teacher's past feedback.

[0120] The teaching material provision unit can estimate the teacher's emotions and adjust the method of providing teaching materials based on the estimated teacher's emotions. The teaching material provision unit, for example, estimates the teacher's emotions. For example, the teacher's emotions can be estimated using an emotion recognition algorithm. The teaching material provision unit can also adjust the method of providing teaching materials based on the estimated teacher's emotions. For example, if the teacher is feeling stressed, a simple interface can be provided to minimize the search steps for teaching materials. If the teacher is relaxed, detailed search options can be provided and a customizable delivery method can be suggested. If the teacher is in a hurry, voice input can be prioritized to quickly provide teaching materials. This allows the delivery of teaching materials to be smoother by adjusting the delivery method according to the teacher's emotions.

[0121] When providing teaching materials, the teaching material provision unit can select the optimal delivery method by referring to the teacher's past teaching material use history. The teaching material provision unit, for example, refers to the teacher's past teaching material use history. For example, the past teaching material use history can be referenced based on the type of teaching material used and the date and time of use. The teaching material provision unit can also select the optimal delivery method. For example, teaching materials that the teacher has frequently used in the past can be automatically displayed as candidates. Delivery methods (audio, text, etc.) that the teacher has used in the past can be preferentially suggested. Teaching materials that will be used in a specific time period can be predicted and suggested based on the teacher's past teaching material use history. In this way, the delivery of teaching materials can be made more efficient by selecting the optimal delivery method based on the teacher's past teaching material use history.

[0122] When providing teaching materials, the teaching material providing unit can determine the priority of provision by taking into consideration the teacher's current lesson content. The teaching material providing unit, for example, considers the teacher's current lesson content. For example, the teacher's current lesson content can be considered based on the lesson theme and the progress of the lesson. The teaching material providing unit can also determine the priority of provision. For example, the teaching material providing unit can analyze the teacher's current lesson content in real time and provide highly relevant teaching materials preferentially. When the teacher is busy, teaching materials that are easily available can be provided preferentially. When the teacher's lesson content changes, related teaching materials are automatically provided. In this way, highly relevant teaching materials are provided preferentially by providing teaching materials that take into consideration the teacher's lesson content.

[0123] When providing teaching materials, the teaching material providing unit can select the optimal providing means according to the teacher's input method. The teaching material providing unit takes into account, for example, the teacher's input method. For example, the optimal providing means can be selected based on the input method, such as voice input, text input, or image input. For example, if the teacher uses voice input, the teaching material is provided using voice recognition technology. If the teacher uses text input, the teaching material is provided using text analysis technology. If the teacher uses image input, the teaching material is provided using image recognition technology. This makes the provision of teaching materials more efficient by selecting a providing means according to the teacher's input method.

[0124] The teaching material providing unit can estimate the teacher's emotions and determine the priority of teaching materials to be provided based on the estimated teacher's emotions. The teaching material providing unit, for example, estimates the teacher's emotions. For example, the teacher's emotions can be estimated using an emotion recognition algorithm. The teaching material providing unit can also determine the priority of teaching materials to be provided based on the estimated teacher's emotions. For example, if the teacher is feeling stressed, teaching materials with low importance are provided preferentially. If the teacher is relaxed, teaching materials with high importance are provided preferentially. If the teacher is in a hurry, teaching materials that can be used quickly are provided preferentially. In this way, the teaching materials can be provided smoothly by determining the priority of teaching materials according to the teacher's emotions.

[0125] When providing teaching materials, the teaching material providing unit can provide highly relevant teaching materials preferentially by taking into account the geographical location information of the teacher. The teaching material providing unit, for example, takes into account the geographical location information of the teacher. For example, the teacher's current location can be determined using GPS data or location information services. The teaching material providing unit can also provide highly relevant teaching materials preferentially. For example, if the teacher is in a specific location, teaching materials that should be used in that location are provided preferentially. If the teacher is traveling, teaching materials that should be used at the teacher's destination are provided preferentially. If the teacher is at home, teaching materials that should be used at home are provided preferentially. In this way, highly relevant teaching materials are provided preferentially by providing teaching materials that take into account the geographical location information of the teacher.

[0126] The teaching material providing unit can analyze the social media activity of the teacher and provide related teaching materials when providing teaching materials. The teaching material providing unit, for example, analyzes the social media activity of the teacher. For example, the teaching material providing unit can analyze the social media activity of the teacher based on the content of posts and the frequency of activity. The teaching material providing unit can also provide related teaching materials. For example, the teaching material providing unit can provide related teaching materials based on information shared by the teacher on social media. The teaching material providing unit can analyze the content of the teacher's social media activity and suggest related teaching materials. The teaching material providing unit can provide related teaching materials based on the activity of the teacher's friends on social media. In this way, related teaching materials are provided based on the teacher's social media activity.

[0127] When providing teaching materials, the teaching material providing unit can customize the method of providing teaching materials by reflecting the teacher's past feedback. The teaching material providing unit, for example, reflects the teacher's past feedback. For example, it can reflect past feedback based on the teacher's opinions and evaluation results. The teaching material providing unit can also customize the method of providing teaching materials. For example, it can propose the optimal method of providing teaching materials based on feedback provided by the teacher in the past. It can analyze the teacher's past feedback and improve the provision procedure. It can customize the provision interface by reflecting the teacher's feedback. As a result, the provision of teaching materials becomes more efficient through a method of providing teaching materials that reflects the teacher's past feedback.

[0128] The suggestion unit can estimate the teacher's emotions and adjust the method of suggesting teaching materials based on the estimated teacher's emotions. The suggestion unit, for example, estimates the teacher's emotions. For example, the teacher's emotions can be estimated using an emotion recognition algorithm. The suggestion unit can also adjust the method of suggesting teaching materials based on the estimated teacher's emotions. For example, if the teacher is feeling stressed, a simple interface is provided and the suggestion steps are minimized. If the teacher is relaxed, detailed suggestion options are provided and a customizable suggestion method is proposed. If the teacher is in a hurry, voice input is prioritized so that teaching materials can be suggested quickly. As a result, the suggestion method can be adjusted according to the teacher's emotions, allowing teaching materials to be suggested smoothly.

[0129] When proposing teaching materials, the suggestion unit can select the optimal suggestion method by referring to the teacher's past teaching material usage history. The suggestion unit, for example, refers to the teacher's past teaching material usage history. For example, the past teaching material usage history can be referenced based on the type of teaching material used and the date and time of use. The suggestion unit can also select the optimal suggestion method. For example, teaching materials that the teacher has frequently used in the past can be automatically displayed as candidates. The suggestion unit gives priority to suggesting proposal methods (audio, text, etc.) that the teacher has used in the past. The teaching materials that will be used in a specific time period can be predicted and suggested based on the teacher's past teaching material usage history. In this way, the optimal suggestion method can be selected based on the teacher's past teaching material usage history, making teaching material suggestions more efficient.

[0130] When suggesting teaching materials, the suggestion unit can determine the priority of suggestions by taking into account the teacher's current lesson content. The suggestion unit, for example, considers the teacher's current lesson content. For example, the suggestion unit can consider the teacher's current lesson content based on the lesson theme and the progress of the lesson. The suggestion unit can also determine the priority of suggestions. For example, the suggestion unit can analyze the teacher's current lesson content in real time and prioritize suggesting highly relevant teaching materials. When the teacher is busy, the suggestion unit can prioritize suggesting easily accessible teaching materials. When the teacher's lesson content changes, the suggestion unit can automatically suggest related teaching materials. In this way, highly relevant teaching materials are prioritized by suggestions that take into account the teacher's lesson content.

[0131] When suggesting teaching materials, the suggestion unit can select the optimal suggestion means according to the teacher's input method. The suggestion unit takes into account, for example, the teacher's input method. For example, the suggestion unit can select the optimal suggestion means based on the input method, such as voice input, text input, or image input. For example, if the teacher uses voice input, teaching materials are suggested using voice recognition technology. If the teacher uses text input, teaching materials are suggested using text analysis technology. If the teacher uses image input, teaching materials are suggested using image recognition technology. This makes it possible to efficiently suggest teaching materials by selecting a suggestion means according to the teacher's input method.

[0132] The suggestion unit can estimate the teacher's emotions and determine the priority of the teaching materials to be suggested based on the estimated teacher's emotions. The suggestion unit, for example, estimates the teacher's emotions. For example, the teacher's emotions can be estimated using an emotion recognition algorithm. The suggestion unit can also determine the priority of the teaching materials to be suggested based on the estimated teacher's emotions. For example, if the teacher is feeling stressed, teaching materials with low importance are suggested preferentially. If the teacher is relaxed, teaching materials with high importance are suggested preferentially. If the teacher is in a hurry, teaching materials that can be used quickly are suggested preferentially. In this way, the priority of teaching materials can be decided according to the teacher's emotions, allowing for smooth suggestion of teaching materials.

[0133] When suggesting teaching materials, the suggestion unit can prioritize suggesting highly relevant teaching materials by taking into account the geographical location information of the teacher. The suggestion unit, for example, considers the geographical location information of the teacher. For example, the teacher's current location can be determined using GPS data or location information services. The suggestion unit can also prioritize suggesting highly relevant teaching materials. For example, if the teacher is in a specific location, the suggestion unit prioritizes suggesting teaching materials that should be used in that location. If the teacher is traveling, the suggestion unit prioritizes suggesting teaching materials that should be used at the teacher's destination. If the teacher is at home, the suggestion unit prioritizes suggesting teaching materials that should be used at home. In this way, highly relevant teaching materials are prioritized by suggestions that take into account the geographical location information of the teacher.

[0134] When suggesting teaching materials, the suggestion unit can analyze the social media activity of the teacher and suggest related teaching materials. The suggestion unit, for example, analyzes the social media activity of the teacher. For example, the suggestion unit can analyze the social media activity of the teacher based on the content of posts and the frequency of activity. The suggestion unit can also suggest related teaching materials. For example, the suggestion unit can suggest related teaching materials based on information shared by the teacher on social media. The suggestion unit can analyze the content of the teacher's social media activity and suggest related teaching materials. The suggestion unit can suggest related teaching materials based on the activity of the teacher's friends on social media. In this way, related teaching materials are suggested based on the teacher's social media activity.

[0135] When proposing teaching materials, the suggestion unit can customize the suggestion method by reflecting the teacher's past feedback. The suggestion unit, for example, reflects the teacher's past feedback. For example, it can reflect past feedback based on opinions and evaluation results from the teacher. The suggestion unit can also customize the suggestion method. For example, it can propose an optimal suggestion method based on feedback provided by the teacher in the past. It can analyze the teacher's past feedback and improve the suggestion procedure. It can customize the suggestion interface by reflecting the teacher's feedback. As a result, the suggestion method that reflects the teacher's past feedback makes teaching material suggestions more efficient.

[0136] The evaluation unit can estimate the teacher's emotions and adjust the grading method based on the estimated teacher's emotions. The evaluation unit, for example, estimates the teacher's emotions. For example, the evaluation unit can estimate the teacher's emotions using an emotion recognition algorithm. The evaluation unit can also adjust the grading method based on the estimated teacher's emotions. For example, if the teacher is feeling stressed, a simple evaluation interface can be provided to minimize the evaluation procedure. If the teacher is relaxed, detailed evaluation options can be provided and a customizable evaluation method can be suggested. If the teacher is in a hurry, voice input can be prioritized to enable quick grade evaluation. As a result, grading can be carried out smoothly by adjusting the evaluation method according to the teacher's emotions.

[0137] When evaluating grades, the evaluation unit can refer to the teacher's past evaluation history to select the optimal evaluation method. The evaluation unit, for example, refers to the teacher's past evaluation history. For example, it can refer to past evaluation history based on the type of grade evaluated and the evaluation date and time. The evaluation unit can also select the optimal evaluation method. For example, it can automatically display evaluation methods that the teacher has frequently used in the past as candidates. It can prioritize and suggest evaluation criteria that the teacher has used in the past. It can predict and suggest the evaluation method to be used at a specific time period based on the teacher's past evaluation history. This makes grade evaluation more efficient by selecting the optimal evaluation method based on past evaluation history.

[0138] The evaluation department can determine the priority of evaluation by taking into account the faculty member's current workload when evaluating grades. The evaluation department, for example, considers the faculty member's current workload. For example, it can evaluate the faculty member's workload based on the working hours and workload. The evaluation department can also determine the priority of evaluation. For example, it can analyze the faculty member's current workload in real time and prioritize important evaluations when the workload is low. When the faculty member is busy, it can prioritize simple evaluations to reduce the burden. When the faculty member's workload is high, it can automate evaluations and minimize manual input. In this way, the priority of grades can be appropriately determined through evaluations that take the faculty member's workload into account.

[0139] When evaluating grades, the evaluation unit can select the optimal evaluation means according to the teacher's input method. The evaluation unit takes into account, for example, the teacher's input method. For example, the evaluation unit can select the optimal evaluation means based on the input method, such as voice input, text input, or image input. For example, if the teacher uses voice input, the grades are evaluated using voice recognition technology. If the teacher uses text input, the grades are evaluated using text analysis technology. If the teacher uses image input, the grades are evaluated using image recognition technology. This makes grade evaluation more efficient by selecting an evaluation means according to the teacher's input method.

[0140] The evaluation unit can estimate the teacher's emotions and determine the priority of grades to be evaluated based on the estimated teacher's emotions. The evaluation unit, for example, estimates the teacher's emotions. For example, the teacher's emotions can be estimated using an emotion recognition algorithm. The evaluation unit can also determine the priority of grades to be evaluated based on the estimated teacher's emotions. For example, if the teacher is stressed, the evaluation unit prioritizes less important grades. If the teacher is relaxed, the evaluation unit prioritizes more important grades. If the teacher is in a hurry, the evaluation unit prioritizes grades that can be completed quickly. In this way, the prioritization of grades according to the teacher's emotions allows for smooth grade evaluation.

[0141] When evaluating grades, the evaluation unit can prioritize highly relevant grades by taking into account the geographical location information of the instructor. The evaluation unit, for example, considers the geographical location information of the instructor. For example, the instructor's current location can be determined using GPS data or location information services. The evaluation unit can also prioritize highly relevant grades. For example, if the instructor is in a specific location, the evaluation that should be done at that location is prioritized. If the instructor is traveling, the evaluation that should be done at the instructor's destination is prioritized. If the instructor is at home, the evaluation that should be done at home is prioritized. In this way, highly relevant grades are prioritized by evaluation that takes into account the instructor's geographical location information.

[0142] The evaluation unit can analyze the social media activity of the faculty member and evaluate the related grades when evaluating grades. The evaluation unit, for example, analyzes the social media activity of the faculty member. For example, the evaluation unit can analyze the social media activity of the faculty member based on the content of posts and the frequency of activities. The evaluation unit can also evaluate the related grades. For example, the evaluation unit can evaluate the related grades based on the information the faculty member shared on social media. The evaluation unit can analyze the content of the faculty member's social media activity and suggest related grades. The evaluation unit can evaluate the related grades based on the activity of the faculty member's friends on social media. In this way, the evaluation unit can evaluate the related grades based on the faculty member's social media activity.

[0143] The evaluation unit can customize the evaluation method by reflecting the teacher's past feedback when evaluating grades. The evaluation unit, for example, reflects the teacher's past feedback. For example, it can reflect past feedback based on the teacher's opinions and evaluation results. The evaluation unit can also customize the evaluation method. For example, it can propose the optimal evaluation method based on feedback provided by the teacher in the past. It can analyze the teacher's past feedback and improve the evaluation procedure. It can customize the evaluation interface by reflecting the teacher's feedback. As a result, the evaluation method that reflects the teacher's past feedback makes grade evaluation more efficient.

[0144] The security department can estimate the emotions of the faculty member and adjust the access control method based on the estimated emotions of the faculty member. The security department, for example, estimates the emotions of the faculty member. For example, the security department can estimate the emotions of the faculty member using an emotion recognition algorithm. The security department can also adjust the access control method based on the estimated emotions of the faculty member. For example, if the faculty member is feeling stressed, a simple access control interface can be provided to minimize the steps. If the faculty member is relaxed, detailed access control options can be provided and a customizable control method can be suggested. If the faculty member is in a hurry, voice input can be prioritized to enable quick access control. As a result, access control can be performed smoothly by adjusting the access control method according to the faculty member's emotions.

[0145] When controlling access, the security department can refer to the faculty member's past access history to select the optimal control method. The security department, for example, refers to the faculty member's past access history. For example, it can refer to past access history based on the date and time of access and the resources accessed. The security department can also select the optimal control method. For example, it can automatically display data that the faculty member has frequently accessed in the past as a candidate. It can prioritize and suggest access control methods that the faculty member has used in the past. It can predict and suggest access control to be performed during specific time periods based on the faculty member's past access history. This makes access control more efficient by selecting the optimal control method based on past access history.

[0146] When controlling access, the security department can determine the priority of control by taking into account the faculty member's current work content. The security department can, for example, take into account the faculty member's current work content. For example, the security department can take into account the faculty member's current work content based on the type of work and the progress of the work. The security department can also determine the priority of control. For example, the security department can analyze the faculty member's current work content in real time and prioritize control of access to important data. When the faculty member is busy, the security department can prioritize control of data that is easily accessible. If the faculty member's work content changes, the security department can automatically adjust access control to related data. As a result, access priorities are appropriately determined through control that takes into account the faculty member's work content.

[0147] When controlling access, the security department can select the optimal control means according to the teacher's input method. The security department takes into consideration, for example, the teacher's input method. For example, the security department can select the optimal control means based on the input method, such as voice input, text input, or image input. For example, if the teacher uses voice input, access control is performed using voice recognition technology. If the teacher uses text input, access control is performed using text analysis technology. If the teacher uses image input, access control is performed using image recognition technology. This makes access control more efficient by selecting a control means according to the teacher's input method.

[0148] The security unit can estimate the teacher's emotions and determine the priority of access control based on the estimated teacher's emotions. The security unit, for example, estimates the teacher's emotions. For example, the security unit can estimate the teacher's emotions using an emotion recognition algorithm. The security unit can also determine the priority of access control based on the estimated teacher's emotions. For example, if the teacher is feeling stressed, access to less important data is controlled preferentially. If the teacher is relaxed, access to more important data is controlled preferentially. If the teacher is in a hurry, data that can be accessed quickly is controlled preferentially. In this way, access control can be performed smoothly by determining the access priority according to the teacher's emotions.

[0149] When controlling access, the security department can prioritize highly relevant access by taking into account the geographical location information of the teacher. The security department, for example, takes into account the geographical location information of the teacher. For example, the current location of the teacher can be determined using GPS data or location information services. The security department can also prioritize control of highly relevant access. For example, if the teacher is in a specific location, access control that should be performed at that location is prioritized. If the teacher is traveling, access control that should be performed at the teacher's destination is prioritized. If the teacher is at home, access control that should be performed at home is prioritized. In this way, highly relevant access is prioritized by control that takes into account the geographical location information of the teacher.

[0150] When controlling access, the security department can analyze the social media activity of faculty members and control related access. The security department, for example, analyzes the social media activity of faculty members. For example, the security department can analyze the social media activity of faculty members based on the content of posts and the frequency of activity. The security department can also control related access. For example, the security department can control related access based on information shared by faculty members on social media. The security department can analyze the content of the faculty members' social media activities and suggest related access. The security department can control related access based on the activities of the faculty members' friends on social media. In this way, related access is controlled based on the social media activity of the faculty members.

[0151] When controlling access, the security department can customize the control method by reflecting past feedback from teachers. For example, the security department reflects past feedback from teachers. For example, it can reflect past feedback based on opinions and evaluation results from teachers. The security department can also customize the control method. For example, it can propose the optimal control method based on feedback provided by teachers in the past. It can analyze past feedback from teachers and improve control procedures. It can customize the control interface by reflecting teacher feedback. As a result, access control becomes more efficient using a control method that reflects past feedback from teachers. === Hard Collateral 1-1 === Each of the multiple elements, including the registration unit, management unit, update unit, teaching material provision unit, suggestion unit, evaluation unit, and security unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the registration unit is implemented by the control unit 46A of the smart device 14, which lists the teacher's tasks and registers them on the platform. The management unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which manages the progress of the registered tasks in real time. The update unit is implemented, for example, by the control unit 46A of the smart device 14, which allows the teacher to report the progress of their tasks and share it with other teachers and administrators. The teaching material provision unit is implemented, for example, by the control unit 46A of the smart device 14, which provides teaching materials necessary for lesson preparation. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which uses AI to suggest teaching materials necessary for lesson preparation. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which automates grade evaluation. The security unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and performs access control of the platform. === Hard Collateral 1-2 === Each of the multiple elements, including the registration unit, management unit, update unit, teaching material provision unit, suggestion unit, evaluation unit, and security unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart glasses 214, which lists the teacher's tasks and registers them on the platform. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, which manages the progress of the registered tasks in real time. The update unit is realized, for example, by the control unit 46A of the smart glasses 214, which allows the teacher to report the progress of their tasks and share it with other teachers and administrators. The teaching material provision unit is realized, for example, by the control unit 46A of the smart glasses 214, which provides teaching materials necessary for lesson preparation. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, which uses AI to suggest teaching materials necessary for lesson preparation. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, which automates grade evaluation. The security unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and performs access control of the platform. === Hard Collateral 1-3 === Each of the multiple elements, including the registration unit, management unit, update unit, teaching material provision unit, suggestion unit, evaluation unit, and security unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the registration unit is implemented by the control unit 46A of the headset terminal 314, which lists the teacher's tasks and registers them on the platform. The management unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which manages the progress of the registered tasks in real time. The update unit is implemented, for example, by the control unit 46A of the headset terminal 314, which allows the teacher to report the progress of their tasks and share it with other teachers and administrators. The teaching material provision unit is implemented, for example, by the control unit 46A of the headset terminal 314, which provides teaching materials necessary for lesson preparation. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which uses AI to suggest teaching materials necessary for lesson preparation. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which automates grade evaluation. The security unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and performs access control of the platform. === Hard Collateral 1-4 === Each of the multiple elements, including the registration unit, management unit, update unit, teaching material provision unit, suggestion unit, evaluation unit, and security unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the registration unit is implemented by the control unit 46A of the robot 414 and lists the teacher's tasks and registers them on the platform. The management unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and manages the progress of the registered tasks in real time. The update unit is implemented, for example, by the control unit 46A of the robot 414 and allows the teacher to report the progress of their tasks and share it with other teachers and administrators. The teaching material provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides teaching materials necessary for lesson preparation. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and suggests teaching materials necessary for lesson preparation using AI. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and automates grade evaluation. The security unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and performs access control of the platform.

[0152] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0153] The management department can estimate the emotions of faculty members and adjust the methods for managing meeting schedules and automatically generating meeting minutes based on the estimated emotions. For example, if a faculty member is feeling stressed, a simple schedule management interface can be provided to minimize the number of steps. If a faculty member is relaxed, detailed schedule management options can be provided and customizable management methods can be suggested. If a faculty member is in a hurry, voice input can be prioritized to enable quick schedule management. This allows for smooth meeting schedule management by adjusting the schedule management method according to the faculty member's emotions.

[0154] The teaching material providing unit can provide highly relevant teaching materials preferentially, taking into account the geographical location information of the teacher. For example, if the teacher is in a specific location, teaching materials that should be used at that location are provided preferentially. If the teacher is traveling, teaching materials that should be used at the teacher's destination are provided preferentially. If the teacher is at home, teaching materials that should be used at home are provided preferentially. In this way, highly relevant teaching materials are provided preferentially by taking into account the geographical location information of the teacher.

[0155] The suggestion unit can estimate the teacher's emotions and adjust the method of suggesting learning materials based on the estimated teacher's emotions. For example, if the teacher is feeling stressed, a simple interface is provided and the suggestion steps are minimized. If the teacher is relaxed, detailed suggestion options are provided and a customizable suggestion method is proposed. If the teacher is in a hurry, voice input is prioritized so that learning materials can be suggested quickly. This allows the suggestion method to be adjusted according to the teacher's emotions, making it possible to suggest learning materials smoothly.

[0156] When evaluating grades, the evaluation department can refer to the teacher's past evaluation history to select the optimal evaluation method. For example, evaluation methods that the teacher has frequently used in the past can be automatically displayed as candidates. Evaluation criteria that the teacher has used in the past can be prioritized. The evaluation method to be used at a specific time period can be predicted and proposed based on the teacher's past evaluation history. This makes grade evaluation more efficient by selecting the optimal evaluation method based on past evaluation history.

[0157] The security department can estimate the emotions of teachers and adjust the access control method based on the estimated emotions of the teacher. For example, if a teacher is feeling stressed, a simple access control interface can be provided to minimize the steps. If a teacher is relaxed, detailed access control options can be provided and a customizable control method can be suggested. If a teacher is in a hurry, voice input can be prioritized to enable quick access control. This allows access control to be performed smoothly by adjusting the access control method according to the teacher's emotions.

[0158] The administration department can determine management priorities by taking into account the current workload of faculty members. For example, it can analyze faculty members' current workloads in real time and prioritize important tasks when the workload is low. When faculty members are busy, it can prioritize easy tasks to reduce the workload. When faculty members' workloads are high, it can automate management and minimize manual input. This allows for appropriate prioritization of tasks through management that takes into account the workload of faculty members.

[0159] The update unit can estimate the teacher's emotions and adjust the work update method based on the estimated teacher's emotions. For example, if the teacher is feeling stressed, it provides a simple update interface and minimizes the update procedure. If the teacher is relaxed, it provides detailed update options and suggests a customizable update method. If the teacher is in a hurry, it prioritizes voice input so that the work can be updated quickly. In this way, the update method can be adjusted according to the teacher's emotions, allowing the work to be updated smoothly.

[0160] The teaching material provision unit can select the optimal delivery method by referencing the teacher's past teaching material usage history. For example, teaching materials that the teacher has frequently used in the past are automatically displayed as candidates. Delivery methods (audio, text, etc.) that the teacher has used in the past are given priority. Based on the teacher's past teaching material usage history, the teaching materials that will be used at specific times are predicted and proposed. This makes the delivery of teaching materials more efficient by selecting the optimal delivery method based on the teacher's past teaching material usage history.

[0161] The suggestion unit can determine the priority of suggestions by taking into account the teacher's current lesson content. For example, it can analyze the teacher's current lesson content in real time and prioritize suggesting highly relevant teaching materials. When the teacher is busy, it can prioritize suggesting easily accessible teaching materials. When the teacher's lesson content changes, it can automatically suggest related teaching materials. As a result, highly relevant teaching materials are prioritized by suggestions that take into account the teacher's lesson content.

[0162] The evaluation department can estimate the teacher's emotions and adjust the grading method based on the estimated teacher's emotions. For example, if the teacher is stressed, it provides a simple evaluation interface and minimizes the evaluation procedure. If the teacher is relaxed, it provides detailed evaluation options and suggests a customizable evaluation method. If the teacher is in a hurry, it prioritizes voice input and allows for quick grade evaluation. This allows for smooth grading by adjusting the evaluation method according to the teacher's emotions.

[0163] The processing flow of the second embodiment will be briefly explained below.

[0164] Step 1: The registration department lists the teacher's tasks and registers them on the platform. For example, tasks such as lesson preparation, grading, and teaching material creation can be registered. Step 2: The management unit manages the progress of the work registered by the registration unit, for example, managing the degree of completion of the work and the compliance with deadlines in real time. Step 3: The update section updates the progress of the work managed by the management section. For example, a teacher can report the progress of the work and share it with other teachers and administrators. Step 4: The teaching materials provider provides the teaching materials necessary for lesson preparation. For example, the teaching materials can be searched and downloaded. Step 5: The suggestion unit uses AI to suggest the learning materials needed for lesson preparation. For example, the AI ​​suggests the most suitable learning materials based on the lesson content. Step 6: The evaluation department automates the evaluation of grades. For example, AI automatically evaluates grades. Step 7: The security department controls access to the platform, including managing access rights for teachers and administrators, and providing data encryption and backup functions.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0170] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0185] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0201] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0202] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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).

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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).

[0222] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0223] 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."

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] [Explanation of symbols]

[0237] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The system comprises a registration unit that lists teachers' work and registers it on the platform, a management unit that manages the progress of the work registered by the registration unit, an update unit that updates the progress of the work managed by the management unit, a teaching material provision unit that provides teaching materials for lesson preparation, a suggestion unit that uses AI to suggest teaching materials based on the teaching materials provided by the teaching material provision unit, an evaluation unit that automates grade evaluation, and a security unit that controls access to the platform.

2. 2. The system according to claim 1, wherein the educational material providing unit searches for or downloads educational materials.

3. The proposal unit Using AI to suggest teaching materials needed for lesson preparation 2. The system of claim 1.

4. The evaluation unit Using AI to automatically evaluate grades 2. The system of claim 1.

5. The security unit Manage access privileges for teachers and administrators 2. The system of claim 1.

6. The system according to claim 1 , wherein the security unit has a function of encrypting or backing up data.

7. The management unit Manage meeting schedules and automatically generate meeting minutes 2. The system of claim 1.

8. The update unit Instructors update and share work progress with other instructors and administrators 2. The system of claim 1.

9. The registration unit Estimate teacher sentiment and adjust work registration methods based on the estimated teacher sentiment 2. The system of claim 1.

10. The registration unit Analyze the teacher's past work history and select the optimal registration method 2. The system of claim 1.

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