system

Abstract

The system according to the embodiment aims to reduce the burden on teaching staff and to create teaching plans efficiently. [Solution] A system according to an embodiment includes a receiving unit, a generating unit, and a confirming unit. The receiving unit inputs basic information about a teaching plan. The generating unit analyzes the information input by the receiving unit and creates a draft of the teaching plan. The confirming unit confirms the draft of the teaching plan created by the generating unit and modifies it as necessary.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of placing a significant burden on teachers and staff when creating individual lesson plans.

[0005] The system according to the embodiment aims to reduce the burden on teaching staff and to create teaching plans efficiently. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, and a confirming unit. The receiving unit inputs basic information about the teaching plan. The generating unit analyzes the information input by the receiving unit and creates a draft of the teaching plan. The confirming unit confirms the draft of the teaching plan created by the generating unit and modifies it as necessary. [Effects of the Invention]

[0007] The system according to the embodiment reduces the burden on teaching staff and enables efficient creation of teaching plans. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The teaching plan creation assistance system according to an embodiment of the present invention uses a generation AI to assist teachers in creating individual teaching plans, thereby reducing the burden on them. Using this system, teachers can delegate some of the task of creating teaching plans to the generation AI, thereby reducing the burden on teachers. First, teachers input basic information about the teaching plan, such as the target student's grade, learning goals, and any special support needs. This information is then input into the generation AI. The generation AI then analyzes the input information and creates a draft teaching plan. The generation AI then proposes an optimal teaching plan based on past teaching plan data and best practices in educational instruction. For example, the generation AI suggests what teaching methods would be effective for a specific grade or learning goals. The generated draft teaching plan is then reviewed by teachers and revised as necessary. Based on the teaching plan proposed by the generation AI, teachers then adjust the specific teaching content and schedule. This reduces the burden on teachers and allows them to create teaching plans more efficiently. This system allows teachers to leave part of the lesson plan creation to the generative AI, thereby reducing the burden on teachers. Furthermore, because the generative AI proposes lesson plans based on past data and best practices, the quality of the lesson plans is also improved. For example, by proposing effective teaching methods for specific grade levels and learning goals, it is expected that student learning outcomes will improve. This allows the lesson plan creation assistance system to reduce the burden on teachers and create lesson plans efficiently.

[0029] The teaching plan creation assistance system according to the embodiment includes a reception unit, a generation unit, and a confirmation unit. The reception unit receives basic information about a teaching plan from a teacher. The basic information includes, but is not limited to, the grade level and learning goals of the target students and any special support needs of the students. The reception unit stores the basic information received from the teacher in a database and provides it to the generation unit. The reception unit can also analyze the received basic information and provide it to the generation unit in an appropriate format. The generation unit uses a generation AI to analyze the basic information provided by the reception unit and create a draft teaching plan. The generation AI proposes an optimal teaching plan based on, for example, past teaching plan data and best practices in educational instruction. The generation AI can propose effective teaching methods for, for example, specific grade levels and learning goals. The generation unit provides the draft teaching plan proposed by the generation AI to the confirmation unit. The confirmation unit allows teachers to review the draft teaching plan proposed by the generation AI and revise it as necessary. The teachers can adjust the specific teaching content and schedule based on the teaching plan proposed by the generation AI. For example, teachers can review the draft lesson plan proposed by the generation AI and adjust the teaching content and schedule for specific students. This reduces the burden on teachers in creating lesson plans and allows them to create lesson plans more efficiently. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can create a draft lesson plan using an algorithm that proposes an optimal lesson plan based on past lesson plan data. This allows the lesson plan creation assistance system according to the embodiment to reduce the burden on teachers and create lesson plans efficiently.

[0030] The reception unit can input basic information about the target student, including the student's grade, learning goals, and special support needs. The reception unit, for example, stores the basic information input by faculty and staff in a database and provides it to the generation unit. The reception unit can also analyze the input basic information and provide it to the generation unit in an appropriate format. For example, the reception unit stores the basic information input by faculty and staff, such as the student's grade, learning goals, and special support needs, in a database and provides it to the generation unit. This allows the reception unit to accurately input basic information about the student. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the basic information input by faculty and staff into AI, which then analyzes the basic information and provides it to the generation unit.

[0031] The generation unit can propose an effective lesson plan based on past lesson plan data and best practices in educational instruction. The generation unit uses a generation AI to analyze the basic information provided by the reception unit and create a draft lesson plan. The generation AI proposes an optimal lesson plan based on, for example, past lesson plan data and best practices in educational instruction. The generation AI can propose an effective teaching method for, for example, a specific grade or learning goal. The generation unit provides the draft lesson plan proposed by the generation AI to the confirmation unit. This allows the generation unit to propose an effective lesson plan by utilizing past data and best practices. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can create a draft lesson plan using an algorithm that proposes an optimal lesson plan based on past lesson plan data.

[0032] The confirmation unit allows teachers and staff to review the draft lesson plan proposed by the generation AI and adjust the specific teaching content and schedule. The confirmation unit allows teachers and staff to review the draft lesson plan proposed by the generation AI and revise it as necessary. Teachers and staff can adjust the specific teaching content and schedule based on the lesson plan proposed by the generation AI. For example, teachers and staff can review the draft lesson plan proposed by the generation AI and adjust the teaching content and schedule for specific students. This reduces the burden on teachers and staff in creating lesson plans and enables them to create lesson plans more efficiently. Some or all of the above-mentioned processing in the confirmation unit may be performed using AI, for example, or may be performed without using AI. For example, the confirmation unit can input the draft lesson plan proposed by the generation AI into AI, which can analyze the draft and suggest revisions.

[0033] The reception unit can analyze the student's past learning history and suggest an effective method for inputting basic information. The reception unit can automatically complete the necessary basic information based on, for example, the student's past grade data. The reception unit can also suggest that particularly important information be input preferentially based on the student's learning history. Furthermore, the reception unit can analyze the student's past learning patterns and suggest an optimal input order. This allows the basic information to be input efficiently by utilizing the student's past learning history. Some or all of the above-mentioned processing in the reception unit can be performed, for example, using AI, or can be performed without using AI. For example, the reception unit can input the student's past grade data into AI, which can then automatically complete the necessary basic information.

[0034] When entering basic information, the reception unit can improve input efficiency by referring to the faculty member's past input history. For example, the reception unit can automatically display information previously entered by the faculty member as candidates. The reception unit can also prioritize displaying frequently used items from the faculty member's past input history. Furthermore, the reception unit can analyze the faculty member's past input patterns and suggest the optimal input method. This allows the faculty member's past input history to be utilized to efficiently enter basic information. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the faculty member's past input data into AI, which can then suggest the optimal input method.

[0035] When inputting basic information, the reception unit can customize input items based on the student's home environment and living situation. For example, the reception unit can add items requiring special support based on the student's home environment. The reception unit can also suggest appropriate learning goals taking into account the student's living situation. Furthermore, the reception unit can adjust the priority of input items according to the student's home environment and living situation. This allows appropriate basic information to be input according to the student's home environment and living situation. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the student's home environment data into AI, which can then customize appropriate input items.

[0036] The reception unit can analyze the social media activities of faculty and staff members when entering basic information and automatically complete the relevant information. For example, the reception unit can automatically complete relevant educational information from the faculty and staff members' social media posts. The reception unit can also analyze the social media activities of faculty and staff members and automatically enter information about specific students. Furthermore, the reception unit can automatically complete information based on the faculty and staff members' past teaching experience from the faculty and staff members' social media activities. This allows the basic information to be entered efficiently by utilizing the social media activities of faculty and staff members. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the faculty and staff members' social media data into AI, which can then automatically complete the relevant information.

[0037] The generation unit can adjust the level of detail of the lesson plan based on the student's individual learning style during generation. For example, if the student is a visual learner, the generation unit can generate a lesson plan that makes extensive use of diagrams and illustrations. Furthermore, if the student is an auditory learner, the generation unit can also generate a lesson plan that emphasizes audio and listening activities. Furthermore, if the student is an experiential learner, the generation unit can also generate a lesson plan that includes many practical activities. This allows for the generation of an optimal lesson plan according to the student's learning style. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the student's learning style data into the generation AI, which then generates an optimal lesson plan.

[0038] The generation unit can apply different generation algorithms based on specific educational tasks during generation. For example, when generating a lesson plan for improving reading comprehension, the generation unit can apply an algorithm specialized for improving reading comprehension. Furthermore, when generating a lesson plan for improving mathematical problem-solving skills, the generation unit can also apply an algorithm specialized for mathematics. Furthermore, when generating a lesson plan for promoting social development, the generation unit can also apply an algorithm specialized for social development. This makes it possible to generate an optimal lesson plan for a specific educational task. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can select an optimal generation algorithm based on a specific educational task and generate a lesson plan.

[0039] During generation, the generation unit can determine the generation priority based on past successful examples of lesson plans. For example, the generation unit preferentially generates lesson plans under similar conditions based on past successful lesson plans. The generation unit can also preferentially generate effective lesson plans for a specific grade or learning goal from past successful examples. Furthermore, the generation unit can analyze past successful examples and preferentially generate the most effective teaching methods. This makes it possible to preferentially generate effective lesson plans by utilizing past successful examples. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past lesson plan data into the generation AI, which then generates a lesson plan based on the successful examples.

[0040] The generation unit can adjust the order of generation based on the relevance of the lesson plans during generation. For example, the generation unit analyzes the relevance of the lesson plans and generates the most relevant plan preferentially. The generation unit can also adjust the order of generation based on the relevance of the lesson plans. Furthermore, the generation unit can determine the optimal generation order taking into account the relevance of the lesson plans. This makes it possible to generate lesson plans in the optimal order taking into account the relevance of the lesson plans. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input relevance data of the lesson plans into the generation AI, and the generation AI can determine the optimal generation order.

[0041] During the confirmation, the confirmation unit can suggest an effective correction method by referring to the faculty member's past correction history. For example, the confirmation unit can suggest an optimal correction method based on the faculty member's past correction history. The confirmation unit can also suggest a correction priority by referring to the corrections made by the faculty member in the past. Furthermore, the confirmation unit can analyze the faculty member's past correction patterns and suggest an optimal correction method. This allows correction work to be carried out efficiently by utilizing the faculty member's past correction history. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input the faculty member's past correction data into AI, which then suggests the optimal correction method.

[0042] The verification unit can apply different verification algorithms to each category of the lesson plan during verification. For example, when verifying a lesson plan for improving reading comprehension, the verification unit can apply a verification algorithm specialized for improving reading comprehension. Furthermore, when verifying a lesson plan for improving mathematical problem-solving skills, the verification unit can also apply a verification algorithm specialized for mathematics. Furthermore, when verifying a lesson plan for promoting social development, the verification unit can also apply a verification algorithm specialized for social development. This allows the application of an optimal verification method according to the category of the lesson plan, thereby improving the accuracy of the verification work. Some or all of the above-mentioned processing in the verification unit can be performed, for example, using AI, or can be performed without using AI. For example, the verification unit can input category data of the lesson plan into AI, which can then apply the optimal verification algorithm.

[0043] The confirmation unit can adjust the confirmation priority based on the submission time of the lesson plan during confirmation. For example, the confirmation unit prioritizes confirmation of lesson plans with an upcoming submission deadline. The confirmation unit can also adjust the confirmation priority based on the submission time. Furthermore, the confirmation unit can postpone lesson plans with a distant submission deadline and prioritize checking plans with high urgency. This allows confirmation work to be performed efficiently by adjusting the confirmation priority according to the submission time. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input data on the submission time of the lesson plan into AI, which can determine the optimal confirmation priority.

[0044] The confirmation unit can improve the accuracy of the confirmation by referring to literature related to the lesson plan during confirmation. The confirmation unit, for example, automatically refers to literature related to the lesson plan to improve the accuracy of the confirmation. The confirmation unit can also present literature related to the content of the lesson plan to make it easier for teachers and staff to confirm. Furthermore, the confirmation unit can also suggest an optimal confirmation method based on literature related to the lesson plan. In this way, by referring to related literature, the accuracy of the confirmation can be improved. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input literature data related to the lesson plan into AI, which can then suggest an optimal confirmation method.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The reception unit can automatically suggest relevant educational resources based on the basic information entered by faculty and staff. For example, it can suggest appropriate teaching materials and reference materials for a specific grade level or learning objective. The reception unit can also provide special education resources based on the special support needs entered by faculty and staff. Furthermore, the reception unit can provide information on relevant training and seminars based on the information entered by faculty and staff. This allows faculty and staff to efficiently obtain the necessary resources and improve the quality of their lesson plans.

[0047] When drafting a lesson plan, the generation department can take into account evaluation data from the faculty member's past lesson plans. For example, it can incorporate elements from lesson plans that received high evaluations in the past. The generation department can also reflect areas for improvement in the faculty member's past lesson plans. Furthermore, the generation department can suggest effective teaching methods based on successful examples of the faculty member's past lesson plans. This makes it possible to create higher-quality lesson plans by utilizing the faculty member's past experience.

[0048] The reviewing department can collect and share feedback from other faculty and staff when faculty and staff review draft lesson plans. For example, it can incorporate the opinions of faculty and staff in charge of the same grade or learning objectives. The reviewing department can also allow faculty and staff to add comments to draft lesson plans and share them with other faculty and staff. Furthermore, when faculty and staff revise draft lesson plans, the reviewing department can also refer to the revision history of other faculty and staff. This can promote cooperation between faculty and staff and improve the quality of lesson plans.

[0049] When drafting a lesson plan, the generation unit can customize the teaching method based on the individual learning style of the student. For example, if the student is a visual learner, the generation unit can suggest a teaching method that makes extensive use of diagrams and illustrations. If the student is an auditory learner, the generation unit can suggest a teaching method that emphasizes audio and listening activities. Furthermore, if the student is an experiential learner, the generation unit can suggest a teaching method that includes many hands-on activities. This makes it possible to provide the optimal teaching method according to the student's learning style.

[0050] The generation unit can apply different generation algorithms based on specific educational tasks when drafting a lesson plan. For example, when generating a lesson plan for improving reading comprehension, an algorithm specialized for improving reading comprehension can be applied. The generation unit can also apply an algorithm specialized for mathematics when generating a lesson plan for improving mathematical problem-solving skills. Furthermore, the generation unit can also apply an algorithm specialized for social development when generating a lesson plan for promoting social development. This makes it possible to generate an optimal lesson plan for a specific educational task.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The reception unit receives the basic information about the lesson plan from the faculty member. The basic information input by the faculty member includes, for example, the grade level of the target students, learning goals, and any special support needs. The reception unit stores the basic information input by the faculty member in a database and provides it to the generation unit. The reception unit can also analyze the input basic information and provide it to the generation unit in an appropriate format. Step 2: The Generation Unit uses the Generation AI to analyze the basic information provided by the Reception Unit and create a draft lesson plan. The Generation AI proposes an optimal lesson plan based on past lesson plan data and best practices in educational instruction. The Generation Unit provides the draft lesson plan proposed by the Generation AI to the Confirmation Unit. Step 3: The verification department allows faculty and staff to review the draft lesson plan proposed by the AI ​​and revise it as necessary. Based on the lesson plan proposed by the AI, faculty and staff can adjust the specific lesson content and schedule.

[0053] (Example 2) The teaching plan creation assistance system according to an embodiment of the present invention uses a generation AI to assist teachers in creating individual teaching plans, thereby reducing the burden on them. Using this system, teachers can delegate some of the task of creating teaching plans to the generation AI, thereby reducing the burden on teachers. First, teachers input basic information about the teaching plan, such as the target student's grade, learning goals, and any special support needs. This information is then input into the generation AI. The generation AI then analyzes the input information and creates a draft teaching plan. The generation AI then proposes an optimal teaching plan based on past teaching plan data and best practices in educational instruction. For example, the generation AI suggests what teaching methods would be effective for a specific grade or learning goals. The generated draft teaching plan is then reviewed by teachers and revised as necessary. Based on the teaching plan proposed by the generation AI, teachers then adjust the specific teaching content and schedule. This reduces the burden on teachers and allows them to create teaching plans more efficiently. This system allows teachers to leave part of the lesson plan creation to the generative AI, thereby reducing the burden on teachers. Furthermore, because the generative AI proposes lesson plans based on past data and best practices, the quality of the lesson plans is also improved. For example, by proposing effective teaching methods for specific grade levels and learning goals, it is expected that student learning outcomes will improve. This allows the lesson plan creation assistance system to reduce the burden on teachers and create lesson plans efficiently.

[0054] The teaching plan creation assistance system according to the embodiment includes a reception unit, a generation unit, and a confirmation unit. The reception unit receives basic information about a teaching plan from a teacher. The basic information includes, but is not limited to, the grade level and learning goals of the target students and any special support needs of the students. The reception unit stores the basic information received from the teacher in a database and provides it to the generation unit. The reception unit can also analyze the received basic information and provide it to the generation unit in an appropriate format. The generation unit uses a generation AI to analyze the basic information provided by the reception unit and create a draft teaching plan. The generation AI proposes an optimal teaching plan based on, for example, past teaching plan data and best practices in educational instruction. The generation AI can propose effective teaching methods for, for example, specific grade levels and learning goals. The generation unit provides the draft teaching plan proposed by the generation AI to the confirmation unit. The confirmation unit allows teachers to review the draft teaching plan proposed by the generation AI and revise it as necessary. The teachers can adjust the specific teaching content and schedule based on the teaching plan proposed by the generation AI. For example, teachers can review the draft lesson plan proposed by the generation AI and adjust the teaching content and schedule for specific students. This reduces the burden on teachers in creating lesson plans and allows them to create lesson plans more efficiently. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can create a draft lesson plan using an algorithm that proposes an optimal lesson plan based on past lesson plan data. This allows the lesson plan creation assistance system according to the embodiment to reduce the burden on teachers and create lesson plans efficiently.

[0055] The reception unit can input basic information about the target student, including the student's grade, learning goals, and special support needs. The reception unit, for example, stores the basic information input by faculty and staff in a database and provides it to the generation unit. The reception unit can also analyze the input basic information and provide it to the generation unit in an appropriate format. For example, the reception unit stores the basic information input by faculty and staff, such as the student's grade, learning goals, and special support needs, in a database and provides it to the generation unit. This allows the reception unit to accurately input basic information about the student. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the basic information input by faculty and staff into AI, which then analyzes the basic information and provides it to the generation unit.

[0056] The generation unit can propose an effective lesson plan based on past lesson plan data and best practices in educational instruction. The generation unit uses a generation AI to analyze the basic information provided by the reception unit and create a draft lesson plan. The generation AI proposes an optimal lesson plan based on, for example, past lesson plan data and best practices in educational instruction. The generation AI can propose an effective teaching method for, for example, a specific grade or learning goal. The generation unit provides the draft lesson plan proposed by the generation AI to the confirmation unit. This allows the generation unit to propose an effective lesson plan by utilizing past data and best practices. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can create a draft lesson plan using an algorithm that proposes an optimal lesson plan based on past lesson plan data.

[0057] The confirmation unit allows teachers and staff to review the draft lesson plan proposed by the generation AI and adjust the specific teaching content and schedule. The confirmation unit allows teachers and staff to review the draft lesson plan proposed by the generation AI and revise it as necessary. Teachers and staff can adjust the specific teaching content and schedule based on the lesson plan proposed by the generation AI. For example, teachers and staff can review the draft lesson plan proposed by the generation AI and adjust the teaching content and schedule for specific students. This reduces the burden on teachers and staff in creating lesson plans and enables them to create lesson plans more efficiently. Some or all of the above-mentioned processing in the confirmation unit may be performed using AI, for example, or may be performed without using AI. For example, the confirmation unit can input the draft lesson plan proposed by the generation AI into AI, which can analyze the draft and suggest revisions.

[0058] The reception unit can estimate the faculty member's emotions and adjust the display method of the input interface based on the estimated faculty member's emotions. For example, if the faculty member is stressed, the reception unit can provide a simple interface and minimize input steps. Alternatively, if the faculty member is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the faculty member is in a hurry, the reception unit can prioritize voice input to allow quick input of basic information. This reduces the burden of input work by adjusting the input interface according to the faculty member's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the faculty member's facial expression data into a generation AI, which can then estimate the faculty member's emotions and adjust the interface.

[0059] The reception unit can analyze the student's past learning history and suggest an effective method for inputting basic information. The reception unit can automatically complete the necessary basic information based on, for example, the student's past grade data. The reception unit can also suggest that particularly important information be input preferentially based on the student's learning history. Furthermore, the reception unit can analyze the student's past learning patterns and suggest an optimal input order. This allows the basic information to be input efficiently by utilizing the student's past learning history. Some or all of the above-mentioned processing in the reception unit can be performed, for example, using AI, or can be performed without using AI. For example, the reception unit can input the student's past grade data into AI, which can then automatically complete the necessary basic information.

[0060] When entering basic information, the reception unit can improve input efficiency by referring to the faculty member's past input history. For example, the reception unit can automatically display information previously entered by the faculty member as candidates. The reception unit can also prioritize displaying frequently used items from the faculty member's past input history. Furthermore, the reception unit can analyze the faculty member's past input patterns and suggest the optimal input method. This allows the faculty member's past input history to be utilized to efficiently enter basic information. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the faculty member's past input data into AI, which can then suggest the optimal input method.

[0061] The reception unit can estimate the faculty member's emotions and determine the priority of basic information to be entered based on the estimated faculty member's emotions. For example, if the faculty member is feeling stressed, the reception unit can prioritize the input of the most important information. The reception unit can also prompt the faculty member to enter more detailed information if the faculty member is relaxed. Furthermore, if the faculty member is in a hurry, the reception unit can prompt the faculty member to enter only the minimum necessary information. This allows for efficient information entry by adjusting the input priority of basic information according to the faculty member's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input facial expression data of the faculty member into a generation AI, which can then estimate the faculty member's emotions and determine the input priority.

[0062] When inputting basic information, the reception unit can customize input items based on the student's home environment and living situation. For example, the reception unit can add items requiring special support based on the student's home environment. The reception unit can also suggest appropriate learning goals taking into account the student's living situation. Furthermore, the reception unit can adjust the priority of input items according to the student's home environment and living situation. This allows appropriate basic information to be input according to the student's home environment and living situation. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the student's home environment data into AI, which can then customize appropriate input items.

[0063] The reception unit can analyze the social media activities of faculty and staff members when entering basic information and automatically complete the relevant information. For example, the reception unit can automatically complete relevant educational information from the faculty and staff members' social media posts. The reception unit can also analyze the social media activities of faculty and staff members and automatically enter information about specific students. Furthermore, the reception unit can automatically complete information based on the faculty and staff members' past teaching experience from the faculty and staff members' social media activities. This allows the basic information to be entered efficiently by utilizing the social media activities of faculty and staff members. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the faculty and staff members' social media data into AI, which can then automatically complete the relevant information.

[0064] The generation unit can estimate the emotions of the faculty and staff and adjust the expression method of the generated lesson plan based on the estimated emotions of the faculty and staff. For example, if the faculty and staff are relaxed, the generation unit can generate a detailed lesson plan. Furthermore, if the faculty and staff are stressed, the generation unit can generate a concise lesson plan. Furthermore, if the faculty and staff are in a hurry, the generation unit can generate a lesson plan that focuses on the main points. This allows for adjusting the expression method of the lesson plan according to the emotions of the faculty and staff, thereby generating a more appropriate lesson plan. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input facial expression data of the faculty and staff into the generation AI, which can estimate the emotions and adjust the expression method of the lesson plan.

[0065] The generation unit can adjust the level of detail of the lesson plan based on the student's individual learning style during generation. For example, if the student is a visual learner, the generation unit can generate a lesson plan that makes extensive use of diagrams and illustrations. Furthermore, if the student is an auditory learner, the generation unit can also generate a lesson plan that emphasizes audio and listening activities. Furthermore, if the student is an experiential learner, the generation unit can also generate a lesson plan that includes many practical activities. This allows for the generation of an optimal lesson plan according to the student's learning style. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the student's learning style data into the generation AI, which then generates an optimal lesson plan.

[0066] The generation unit can apply different generation algorithms based on specific educational tasks during generation. For example, when generating a lesson plan for improving reading comprehension, the generation unit can apply an algorithm specialized for improving reading comprehension. Furthermore, when generating a lesson plan for improving mathematical problem-solving skills, the generation unit can also apply an algorithm specialized for mathematics. Furthermore, when generating a lesson plan for promoting social development, the generation unit can also apply an algorithm specialized for social development. This makes it possible to generate an optimal lesson plan for a specific educational task. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can select an optimal generation algorithm based on a specific educational task and generate a lesson plan.

[0067] The generation unit can estimate the emotions of the faculty and staff and adjust the length of the lesson plan to be generated based on the estimated emotions of the faculty and staff. For example, if the faculty and staff are relaxed, the generation unit can generate a detailed lesson plan. Furthermore, if the faculty and staff are stressed, the generation unit can generate a concise lesson plan. Furthermore, if the faculty and staff are in a hurry, the generation unit can generate a short lesson plan that focuses on the main points. This allows for adjusting the length of the lesson plan according to the emotions of the faculty and staff, thereby generating a more appropriate lesson plan. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input facial expression data of the faculty and staff into the generation AI, which can then estimate the emotions and adjust the length of the lesson plan.

[0068] During generation, the generation unit can determine the generation priority based on past successful examples of lesson plans. For example, the generation unit preferentially generates lesson plans under similar conditions based on past successful lesson plans. The generation unit can also preferentially generate effective lesson plans for a specific grade or learning goal from past successful examples. Furthermore, the generation unit can analyze past successful examples and preferentially generate the most effective teaching methods. This makes it possible to preferentially generate effective lesson plans by utilizing past successful examples. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past lesson plan data into the generation AI, which then generates a lesson plan based on the successful examples.

[0069] The generation unit can adjust the order of generation based on the relevance of the lesson plans during generation. For example, the generation unit analyzes the relevance of the lesson plans and generates the most relevant plan preferentially. The generation unit can also adjust the order of generation based on the relevance of the lesson plans. Furthermore, the generation unit can determine the optimal generation order taking into account the relevance of the lesson plans. This makes it possible to generate lesson plans in the optimal order taking into account the relevance of the lesson plans. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input relevance data of the lesson plans into the generation AI, and the generation AI can determine the optimal generation order.

[0070] The confirmation unit can estimate the faculty member's emotions and adjust the display method of the confirmation interface based on the estimated faculty member's emotions. For example, if the faculty member is relaxed, the confirmation unit can provide a detailed confirmation interface. Furthermore, if the faculty member is stressed, the confirmation unit can provide a simple confirmation interface. Furthermore, if the faculty member is in a hurry, the confirmation unit can provide a confirmation interface that focuses on the main points. This reduces the burden of the confirmation work by adjusting the confirmation interface according to the faculty member's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the confirmation unit can be performed using AI, for example, or without AI. For example, the confirmation unit can input facial expression data of the faculty member into a generation AI, which can estimate the emotion and adjust the interface.

[0071] During the confirmation, the confirmation unit can suggest an effective correction method by referring to the faculty member's past correction history. For example, the confirmation unit can suggest an optimal correction method based on the faculty member's past correction history. The confirmation unit can also suggest a correction priority by referring to the corrections made by the faculty member in the past. Furthermore, the confirmation unit can analyze the faculty member's past correction patterns and suggest an optimal correction method. This allows correction work to be carried out efficiently by utilizing the faculty member's past correction history. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input the faculty member's past correction data into AI, which then suggests the optimal correction method.

[0072] The verification unit can apply different verification algorithms to each category of the lesson plan during verification. For example, when verifying a lesson plan for improving reading comprehension, the verification unit can apply a verification algorithm specialized for improving reading comprehension. Furthermore, when verifying a lesson plan for improving mathematical problem-solving skills, the verification unit can also apply a verification algorithm specialized for mathematics. Furthermore, when verifying a lesson plan for promoting social development, the verification unit can also apply a verification algorithm specialized for social development. This allows the application of an optimal verification method according to the category of the lesson plan, thereby improving the accuracy of the verification work. Some or all of the above-mentioned processing in the verification unit can be performed, for example, using AI, or can be performed without using AI. For example, the verification unit can input category data of the lesson plan into AI, which can then apply the optimal verification algorithm.

[0073] The confirmation unit can estimate the emotions of the faculty and staff and determine the priority of the lesson plans to be confirmed based on the estimated emotions of the faculty and staff. For example, if the faculty and staff are relaxed, the confirmation unit can prioritize detailed confirmation. Furthermore, if the faculty and staff are stressed, the confirmation unit can prioritize checking only important items. Furthermore, if the faculty and staff are in a hurry, the confirmation unit can prioritize checking only the main points. This allows for efficient confirmation work by adjusting the confirmation priority according to the faculty and staff's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the confirmation unit can be performed using AI, for example, or without AI. For example, the confirmation unit can input facial expression data of the faculty and staff into a generation AI, which can estimate the emotions and determine the confirmation priority.

[0074] The confirmation unit can adjust the confirmation priority based on the submission time of the lesson plan during confirmation. For example, the confirmation unit prioritizes confirmation of lesson plans with an upcoming submission deadline. The confirmation unit can also adjust the confirmation priority based on the submission time. Furthermore, the confirmation unit can postpone lesson plans with a distant submission deadline and prioritize checking plans with high urgency. This allows confirmation work to be performed efficiently by adjusting the confirmation priority according to the submission time. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input data on the submission time of the lesson plan into AI, which can determine the optimal confirmation priority.

[0075] The confirmation unit can improve the accuracy of the confirmation by referring to literature related to the lesson plan during confirmation. The confirmation unit, for example, automatically refers to literature related to the lesson plan to improve the accuracy of the confirmation. The confirmation unit can also present literature related to the content of the lesson plan to make it easier for teachers and staff to confirm. Furthermore, the confirmation unit can also suggest an optimal confirmation method based on literature related to the lesson plan. In this way, by referring to related literature, the accuracy of the confirmation can be improved. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input literature data related to the lesson plan into AI, which can then suggest an optimal confirmation method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and confirmation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and stores basic information entered by the faculty member in the database 24 and provides it to the generation unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a draft lesson plan using a generation AI. The confirmation unit is realized, for example, by the control unit 46A of the smart device 14, and allows the faculty member to confirm the draft lesson plan proposed by the generation AI and correct it as necessary. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and confirmation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and stores basic information entered by the faculty member in the database 24 and provides it to the generation unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a draft lesson plan using a generation AI. The confirmation unit is realized, for example, by the control unit 46A of the smart glasses 214, and allows the faculty member to confirm the draft lesson plan proposed by the generation AI and modify it as necessary. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and confirmation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and stores basic information entered by the faculty member in the database 24 and provides it to the generation unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a draft lesson plan using a generation AI. The confirmation unit is realized, for example, by the control unit 46A of the headset type terminal 314, and allows the faculty member to confirm the draft lesson plan proposed by the generation AI and correct it as necessary. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and confirmation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and stores basic information entered by the teaching staff in the database 24 and provides it to the generation unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a draft lesson plan using a generation AI. The confirmation unit is realized, for example, by the control unit 46A of the robot 414, and allows the teaching staff to confirm the draft lesson plan proposed by the generation AI and correct it as necessary.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The reception unit can automatically suggest relevant educational resources based on the basic information entered by faculty and staff. For example, it can suggest appropriate teaching materials and reference materials for a specific grade level or learning objective. The reception unit can also provide special education resources based on the special support needs entered by faculty and staff. Furthermore, the reception unit can provide information on relevant training and seminars based on the information entered by faculty and staff. This allows faculty and staff to efficiently obtain the necessary resources and improve the quality of their lesson plans.

[0078] When drafting a lesson plan, the generation department can take into account evaluation data from the faculty member's past lesson plans. For example, it can incorporate elements from lesson plans that received high evaluations in the past. The generation department can also reflect areas for improvement in the faculty member's past lesson plans. Furthermore, the generation department can suggest effective teaching methods based on successful examples of the faculty member's past lesson plans. This makes it possible to create higher-quality lesson plans by utilizing the faculty member's past experience.

[0079] The reviewing department can collect and share feedback from other faculty and staff when faculty and staff review draft lesson plans. For example, it can incorporate the opinions of faculty and staff in charge of the same grade or learning objectives. The reviewing department can also allow faculty and staff to add comments to draft lesson plans and share them with other faculty and staff. Furthermore, when faculty and staff revise draft lesson plans, the reviewing department can also refer to the revision history of other faculty and staff. This can promote cooperation between faculty and staff and improve the quality of lesson plans.

[0080] When drafting a lesson plan, the generation unit can customize the teaching method based on the individual learning style of the student. For example, if the student is a visual learner, the generation unit can suggest a teaching method that makes extensive use of diagrams and illustrations. If the student is an auditory learner, the generation unit can suggest a teaching method that emphasizes audio and listening activities. Furthermore, if the student is an experiential learner, the generation unit can suggest a teaching method that includes many hands-on activities. This makes it possible to provide the optimal teaching method according to the student's learning style.

[0081] The reception unit can estimate the faculty member's emotions and adjust the display method of the input interface based on the estimated faculty member's emotions. For example, if the faculty member is feeling stressed, it can provide a simple interface and minimize input steps. Alternatively, if the faculty member is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the faculty member is in a hurry, the reception unit can prioritize voice input to allow them to quickly enter basic information. In this way, the burden of input work can be reduced by adjusting the input interface according to the faculty member's emotions.

[0082] When creating a draft lesson plan, the generation unit can estimate the emotions of the faculty member and adjust the way the lesson plan is expressed based on the estimated emotions of the faculty member. For example, if the faculty member is relaxed, a detailed lesson plan can be generated. If the faculty member is stressed, a concise lesson plan can be generated. Furthermore, if the faculty member is in a hurry, a lesson plan that focuses on the main points can be generated. In this way, by adjusting the way the lesson plan is expressed based on the emotions of the faculty member, a more appropriate lesson plan can be generated.

[0083] The confirmation unit can estimate the faculty member's emotions and adjust the display method of the confirmation interface based on the estimated faculty member's emotions. For example, if the faculty member is relaxed, a detailed confirmation interface can be provided. If the faculty member is stressed, a simple confirmation interface can be provided. Furthermore, if the faculty member is in a hurry, a confirmation interface that focuses on the main points can be provided. In this way, the burden of confirmation work can be reduced by adjusting the confirmation interface according to the faculty member's emotions.

[0084] The confirmation unit can estimate the emotions of faculty and staff members and determine the priority of the lesson plans to be confirmed based on the estimated emotions of the faculty and staff members. For example, if a faculty and staff member is relaxed, detailed confirmation can be prioritized. Also, if a faculty and staff member is feeling stressed, it is possible to prioritize checking only important items. Furthermore, if a faculty and staff member is in a hurry, it is possible to prioritize checking the main points. In this way, confirmation work can be carried out efficiently by adjusting the priority of confirmations according to the emotions of the faculty and staff members.

[0085] When creating a draft lesson plan, the generation unit can estimate the emotions of the faculty member and adjust the length of the lesson plan to be generated based on the estimated emotions of the faculty member. For example, if the faculty member is relaxed, a detailed lesson plan can be generated. Also, if the faculty member is stressed, a concise lesson plan can be generated. Furthermore, if the faculty member is in a hurry, a short lesson plan that focuses on the main points can be generated. In this way, by adjusting the length of the lesson plan according to the emotions of the faculty member, a more appropriate lesson plan can be generated.

[0086] The generation unit can apply different generation algorithms based on specific educational tasks when drafting a lesson plan. For example, when generating a lesson plan for improving reading comprehension, an algorithm specialized for improving reading comprehension can be applied. The generation unit can also apply an algorithm specialized for mathematics when generating a lesson plan for improving mathematical problem-solving skills. Furthermore, the generation unit can also apply an algorithm specialized for social development when generating a lesson plan for promoting social development. This makes it possible to generate an optimal lesson plan for a specific educational task.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The reception unit receives the basic information about the lesson plan from the faculty member. The basic information input by the faculty member includes, for example, the grade level of the target students, learning goals, and any special support needs. The reception unit stores the basic information input by the faculty member in a database and provides it to the generation unit. The reception unit can also analyze the input basic information and provide it to the generation unit in an appropriate format. Step 2: The Generation Unit uses the Generation AI to analyze the basic information provided by the Reception Unit and create a draft lesson plan. The Generation AI proposes an optimal lesson plan based on past lesson plan data and best practices in educational instruction. The Generation Unit provides the draft lesson plan proposed by the Generation AI to the Confirmation Unit. Step 3: The verification department allows faculty and staff to review the draft lesson plan proposed by the AI ​​and revise it as necessary. Based on the lesson plan proposed by the AI, faculty and staff can adjust the specific lesson content and schedule.

[0089] 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.

[0090] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0091] 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.

[0092] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception section where basic information about the lesson plan is entered; a generating unit that analyzes the information input by the receiving unit and creates a draft of a teaching plan; a confirmation unit that confirms the draft of the teaching plan created by the generation unit and corrects it as necessary. A system characterized by:

2. The reception unit Enter basic information about your students, including their grade level, learning goals, and any special needs.

2. The system of claim 1.

3. The generation unit Propose effective lesson plans based on past lesson plan data and best practices in educational instruction 2. The system of claim 1.

4. The confirmation unit Faculty and staff review the draft lesson plan proposed by the AI ​​and adjust the specific content and schedule.

2. The system of claim 1.

5. The reception unit Estimate the emotions of faculty and staff members and adjust the display method of the input interface based on the estimated emotions of the faculty and staff members.

2. The system of claim 1.

6. The reception unit Analyze students' past learning history and suggest effective ways to enter basic information 2. The system of claim 1.

7. The reception unit When entering basic information, improve efficiency by referring to the past input history of faculty and staff members.

2. The system of claim 1.

8. The reception unit Estimate the emotions of faculty and staff members and determine the priority of basic information to be entered based on the estimated emotions of faculty and staff members 2. The system of claim 1.

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