system

Abstract

The system according to the embodiment aims to enable dual-income parents to keep track of their children's learning progress and efficiently support their learning. [Solution] A system according to an embodiment includes a monitoring unit, a generating unit, a planning unit, a dialogue unit, and a management unit. The monitoring unit monitors learning progress. The generating unit generates commentary based on the learning progress monitored by the monitoring unit. The planning unit presents an individual learning plan based on the commentary generated by the generating unit. The dialogue unit provides learning motivation based on the learning plan presented by the planning unit. The management unit manages a schedule based on the learning motivation provided by the dialogue unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for dual-income parents to keep track of their children's learning progress and provide effective support for their children's learning.

[0005] The system according to the embodiment aims to enable dual-income parents to keep track of their children's learning progress and efficiently support their learning. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit, a generating unit, a planning unit, a dialogue unit, and a management unit. The monitoring unit monitors learning progress. The generating unit generates commentary based on the learning progress monitored by the monitoring unit. The planning unit presents an individual learning plan based on the commentary generated by the generating unit. The dialogue unit provides learning motivation based on the learning plan presented by the planning unit. The management unit manages a schedule based on the learning motivation provided by the dialogue unit. [Effects of the Invention]

[0007] The system according to the embodiment allows dual-income parents to keep track of their children's learning progress and efficiently support their children's learning. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A learning support system according to an embodiment of the present invention utilizes a generative AI to manage and support children's learning progress. This learning support system is designed to help dual-income parents understand their children's learning status and enable them to study efficiently. Specifically, it implements a learning progress management function, a learning support function, a learning efficiency maximization function, and a test schedule management function. For example, in this learning support system, a generative AI monitors children's learning progress in real time and generates learning status reports for parents, allowing parents to easily understand their children's learning status. The generative AI also generates explanations for problems that children do not understand and presents individualized learning plans, allowing children to progress at their own pace. Furthermore, the generative AI engages in dialogue to motivate students and provides easy-to-remember, familiar applications and topics, allowing children to continue learning with enjoyment. Finally, the generative AI manages cram school homework and test schedules and notifies parents and children, facilitating schedule management for both parents and children. This allows parents and children to easily manage their schedules. The learning support system thus makes it easy for dual-income parents to understand their children's learning status and enables children to progress efficiently.

[0029] A learning support system according to an embodiment includes a monitoring unit, a generating unit, a planning unit, a dialogue unit, and a management unit. The monitoring unit monitors a child's learning progress. For example, the monitoring unit collects the child's learning data and analyzes the progress. The monitoring unit can monitor the child's learning data in real time and analyze the progress using a generation AI. The generation unit generates explanations based on the learning progress monitored by the monitoring unit. For example, the generation unit identifies problems the child does not understand and generates explanations for those problems. The generation unit can identify problems the child does not understand and generate explanations for those problems using a generation AI. The planning unit presents an individual learning plan based on the explanations generated by the generation unit. For example, the planning unit creates an optimal learning plan based on the child's learning data. The planning unit can create an optimal learning plan based on the child's learning data using a generation AI. The dialogue unit motivates the child to learn based on the learning plan presented by the planning unit. For example, the dialogue unit engages in dialogue with the child to motivate the child to learn. The dialogue unit can use the generation AI to dialogue with the child and motivate them to study. The management unit manages the schedule based on the learning motivation provided by the dialogue unit. The management unit, for example, keeps track of cram school homework and test schedules and notifies parents and children. The management unit can use the generation AI to keep track of cram school homework and test schedules and notify parents and children. As a result, the learning support system according to the embodiment can efficiently monitor learning progress, generate explanations, present learning plans, motivate students to study, and manage schedules.

[0030] The monitoring unit can collect learning data of children and analyze their progress. Learning data includes, but is not limited to, test results, assignment submission status, and study time. The monitoring unit can, for example, collect learning data of children and analyze their progress. For example, the monitoring unit can analyze children's learning progress based on test results. The monitoring unit can also analyze children's learning progress based on assignment submission status. Furthermore, the monitoring unit can analyze children's learning progress based on study time. This makes it possible to collect learning data and analyze their progress. Some or all of the above-mentioned processing in the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input children's learning data into the generation AI and have the generation AI analyze their progress.

[0031] The generation unit can identify questions that a child cannot understand and generate explanations for those questions. For example, the generation unit can identify questions that a child cannot understand and generate explanations for those questions. For example, the generation unit can identify questions that a child cannot understand based on an analysis of test errors. The generation unit can also identify questions that a child cannot understand based on the frequency of questions. Furthermore, the generation unit generates explanations for questions that a child cannot understand. For example, the generation unit can provide step-by-step explanations for math questions. The generation unit can also provide explanations using experiments and observations for science questions. Furthermore, the generation unit can provide explanations using timelines and related events for history questions. In this way, explanations for questions that a child cannot understand are provided. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input questions that a child cannot understand into the generation AI and cause the generation AI to generate explanations.

[0032] The planning unit can create a study plan based on the child's learning data. The planning unit, for example, creates an optimal study plan based on the child's learning data. For example, the planning unit can create a study plan based on the child's test results. The planning unit can also create a study plan based on the child's assignment submission status. Furthermore, the planning unit can create a study plan based on the child's study time. The planning unit can create an optimal study plan based on the child's learning data using the generation AI. For example, the planning unit can input the child's learning data into the generation AI and have the generation AI create an optimal study plan. In this way, an optimal study plan is created. Some or all of the above-mentioned processing in the planning unit may be performed using the generation AI, or may be performed without using the generation AI.

[0033] The dialogue unit can dialogue with the child and motivate the child to learn. The dialogue unit, for example, dialogues with the child and motivates the child to learn. For example, the dialogue unit can discuss with the child the importance of learning and future goals. The dialogue unit can also provide the child with feedback on their learning progress and motivate them to learn. Furthermore, the dialogue unit can introduce a reward system for the child and motivate them to learn. The dialogue unit can dialogue with the child using the generation AI and motivate the child to learn. For example, the dialogue unit can input the content of the dialogue with the child into the generation AI and have the generation AI execute the motivation to learn. This motivates the child to learn. Some or all of the above-mentioned processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI.

[0034] The management unit can keep track of cram school homework and test schedules and notify parents and children. The management unit, for example, keeps track of cram school homework and test schedules and notifies parents and children. For example, the management unit can keep track of cram school homework deadlines and notify parents and children. The management unit can also keep track of test dates and notify parents and children. The management unit can also keep track of study time and notify parents and children. The management unit can use the generation AI to keep track of cram school homework and test schedules and notify parents and children. For example, the management unit can input the cram school homework and test schedules into the generation AI and have the generation AI execute notifications. This makes schedule management easier. Some or all of the above-mentioned processing in the management unit may be performed using the generation AI, or may be performed without using the generation AI.

[0035] The monitoring unit can analyze the child's past learning data and select a monitoring method. The monitoring unit, for example, analyzes the child's past learning data and selects the optimal monitoring method. For example, the monitoring unit can reduce the monitoring frequency for subjects in which the child has previously achieved high grades. The monitoring unit can also increase the monitoring frequency and collect detailed data for subjects in which the child is weak. Furthermore, the monitoring unit can analyze the child's learning patterns and set the optimal monitoring timing. This enables optimal monitoring based on past learning data. Some or all of the above-mentioned processing in the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input the child's past learning data into the generation AI and have the generation AI select the optimal monitoring method.

[0036] The monitoring unit can perform filtering based on the child's current learning environment and concentration level when monitoring learning progress. For example, the monitoring unit can perform filtering based on the child's current learning environment and concentration level when monitoring learning progress. For example, the monitoring unit can collect detailed progress data when the child is studying in a quiet environment. The monitoring unit can also perform filtering to remove noise when the child is studying in a noisy environment. Furthermore, the monitoring unit can collect detailed progress data when the child is concentrating, and perform filtering when the child's concentration level is low. This enables filtering based on the learning environment and concentration level. Some or all of the above-mentioned processing in the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input data on the child's learning environment and concentration level into the generation AI and have the generation AI perform filtering.

[0037] When monitoring the learning progress, the monitoring unit can prioritize monitoring highly relevant learning data by taking into account the child's geographical location information. For example, when monitoring the learning progress, the monitoring unit prioritizes monitoring highly relevant learning data by taking into account the child's geographical location information. For example, when the child is studying at home, the monitoring unit can prioritize monitoring data related to home study. Furthermore, when the child is studying at school, the monitoring unit can prioritize monitoring data related to the school curriculum. Furthermore, when the child is studying at a library, the monitoring unit can prioritize monitoring data related to library resources. This enables monitoring of highly relevant learning data based on geographical location information. Some or all of the above-described processing by the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input the child's geographical location information into the generation AI and cause the generation AI to monitor highly relevant learning data.

[0038] The monitoring unit can analyze the child's social media activities and monitor related learning data when monitoring learning progress. For example, the monitoring unit can analyze the child's social media activities and monitor related learning data when monitoring learning progress. For example, the monitoring unit can monitor learning content shared by the child on social media and reflect it in the progress data. The monitoring unit can also monitor information on education-related accounts that the child follows on social media. Furthermore, the monitoring unit can monitor the activities of learning groups that the child participates in on social media and collect related data. This makes it possible to monitor learning data based on social media activities. Some or all of the above-mentioned processing by the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input the child's social media activity data into the generation AI and cause the generation AI to monitor the related learning data.

[0039] The generation unit can adjust the level of detail of the explanation based on the importance of the question when generating the explanation. For example, the generation unit can provide detailed step-by-step explanations for important questions. The generation unit can also provide concise explanations for less important questions. Furthermore, the generation unit can adjust the level of detail of the explanation based on the child's grade and level of understanding. This adjusts the level of detail of the explanation based on the importance of the question. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input question importance data to the generation AI and cause the generation AI to adjust the level of detail of the explanation.

[0040] The generation unit can apply different explanation algorithms depending on the problem category when generating an explanation. For example, the generation unit can apply different explanation algorithms depending on the problem category when generating an explanation. For example, the generation unit can apply an explanation algorithm using mathematical formulas to a math problem. The generation unit can also apply an explanation algorithm using a timeline or related events to a history problem. Furthermore, the generation unit can apply an explanation algorithm using experiments or observations to a science problem. In this way, an explanation algorithm depending on the problem category is applied. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input problem category data into the generation AI and cause the generation AI to apply the explanation algorithm.

[0041] The generation unit can determine the priority of explanations based on the time of question submission when generating explanations. The generation unit, for example, determines the priority of explanations based on the time of question submission when generating explanations. For example, the generation unit can prioritize providing explanations for questions with an approaching deadline. The generation unit can also postpone explanations for questions with a distant submission deadline. Furthermore, the generation unit can provide review explanations for questions whose submission deadline has passed. In this way, the priority of explanations is determined based on the time of submission. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input question submission time data into the generation AI and have the generation AI determine the priority of explanations.

[0042] The generation unit can adjust the order of explanations based on the relevance of the questions when generating explanations. For example, the generation unit can adjust the order of explanations based on the relevance of the questions when generating explanations. For example, the generation unit can prioritize explanations for highly relevant questions. The generation unit can also postpone explanations for less relevant questions. Furthermore, the generation unit can adjust the order of explanations according to the child's learning progress. In this way, the order of explanations based on the relevance of the questions is adjusted. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input question relevance data into the generation AI and cause the generation AI to adjust the order of explanations.

[0043] When creating a study plan, the planning unit can analyze the child's past study data to create the plan. For example, when creating a study plan, the planning unit can analyze the child's past study data to create an optimal study plan. For example, the planning unit can simplify the study plan for subjects in which the child has achieved high grades in the past. The planning unit can also provide a detailed study plan for subjects in which the child is weak. Furthermore, the planning unit can analyze the child's study patterns and create an optimal study plan. In this way, an optimal study plan based on the past study data is created. Some or all of the above-mentioned processing in the planning unit may be performed using or without the generation AI. For example, the planning unit can input the child's past study data into the generation AI and cause the generation AI to create an optimal study plan.

[0044] The planning unit can customize the learning plan based on the child's current learning situation when creating the learning plan. For example, the planning unit customizes the plan based on the child's current learning situation when creating the learning plan. For example, the planning unit can customize the learning plan based on the learning content currently being done by the child. The planning unit can also adjust the learning plan according to the child's current learning situation. Furthermore, the planning unit can customize the learning plan based on the child's learning progress. In this way, the learning plan is customized based on the current learning situation. Some or all of the above-mentioned processing in the planning unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the planning unit can input data on the child's current learning situation into the generation AI and cause the generation AI to customize the learning plan.

[0045] The planning unit can create a study plan taking into account the child's geographical location information when creating the plan. For example, when creating a study plan, the planning unit creates an optimal plan taking into account the child's geographical location information. For example, if the child is studying at home, the planning unit can create a plan suitable for home study. Furthermore, if the child is studying at school, the planning unit can create a plan suitable for the school curriculum. Furthermore, if the child is studying at a library, the planning unit can create a plan that utilizes library resources. In this way, an optimal study plan based on the geographical location information is created. Some or all of the above-mentioned processing in the planning unit may be performed using or without the generation AI. For example, the planning unit can input the child's geographical location information into the generation AI and cause the generation AI to create an optimal study plan.

[0046] The planning unit can analyze the child's social media activity and propose a lesson plan when creating the lesson plan. For example, the planning unit can analyze the child's social media activity and propose a lesson plan when creating the lesson plan. For example, the planning unit can create a lesson plan based on learning content shared by the child on social media. The planning unit can also create a lesson plan based on information about education-related accounts that the child follows on social media. Furthermore, the planning unit can create a lesson plan based on the activities of learning groups that the child participates in on social media. In this way, a lesson plan based on social media activity is proposed. Some or all of the above-mentioned processing in the planning unit may be performed using or without the generation AI. For example, the planning unit can input the child's social media activity data into the generation AI and have the generation AI execute the proposed lesson plan.

[0047] The dialogue unit can provide dialogue content by referring to the child's past dialogue history during dialogue. The dialogue unit can, for example, provide optimal dialogue content by referring to the child's past dialogue history during dialogue. For example, the dialogue unit can provide dialogue content based on topics in which the child has shown interest in the past. The dialogue unit can also re-engage with the child about problems that the child was unable to understand in the past. Furthermore, the dialogue unit can analyze the child's past dialogue history and provide optimal dialogue content. This allows optimal dialogue content to be provided based on the past dialogue history. Some or all of the above-mentioned processing in the dialogue unit may be performed using or without the generation AI. For example, the dialogue unit can input the child's past dialogue history data into the generation AI and have the generation AI provide the dialogue content.

[0048] The dialogue unit can customize the dialogue content based on the child's current learning situation during dialogue. The dialogue unit customizes the dialogue content based on the child's current learning situation during dialogue, for example. For example, the dialogue unit can customize the dialogue content based on the learning content currently in progress of the child. The dialogue unit can also adjust the dialogue content according to the child's current learning situation. Furthermore, the dialogue unit can customize the dialogue content based on the child's learning progress. In this way, the dialogue content is customized based on the current learning situation. Some or all of the above-mentioned processing in the dialogue unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the dialogue unit can input data on the child's current learning situation into the generation AI and cause the generation AI to customize the dialogue content.

[0049] The dialogue unit can provide optimal dialogue content by taking into account the child's geographical location information during dialogue. For example, the dialogue unit can provide optimal dialogue content by taking into account the child's geographical location information during dialogue. For example, if the child is studying at home, the dialogue unit can provide dialogue content related to home learning. Furthermore, if the child is studying at school, the dialogue unit can provide dialogue content related to the school curriculum. Furthermore, if the child is studying at a library, the dialogue unit can provide dialogue content related to library resources. In this way, optimal dialogue content is provided based on the geographical location information. Some or all of the above-mentioned processing in the dialogue unit may be performed using or without the generation AI. For example, the dialogue unit can input the child's geographical location information into the generation AI and cause the generation AI to provide optimal dialogue content.

[0050] The dialogue unit can analyze the child's social media activity during the dialogue and suggest dialogue content. For example, the dialogue unit can analyze the child's social media activity during the dialogue and suggest dialogue content. For example, the dialogue unit can provide dialogue content based on learning content shared by the child on social media. The dialogue unit can also provide dialogue content based on information about education-related accounts the child follows on social media. Furthermore, the dialogue unit can also provide dialogue content based on the activities of learning groups the child participates in on social media. In this way, dialogue content based on social media activity is suggested. Some or all of the above-mentioned processing in the dialogue unit may be performed using or without the generation AI. For example, the dialogue unit can input the child's social media activity data into the generation AI and cause the generation AI to suggest dialogue content.

[0051] The management unit can provide a management method by referring to the child's past schedule history when managing a schedule. For example, the management unit can provide an optimal management method by referring to the child's past schedule history when managing a schedule. For example, the management unit can provide an optimal schedule based on schedules in which the child achieved high grades in the past. The management unit can also adjust the schedule to avoid schedules in which the child has previously struggled. Furthermore, the management unit can analyze the child's past schedule history and provide an optimal management method. This provides an optimal management method based on the past schedule history. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the child's past schedule history data into the generation AI and have the generation AI provide a management method.

[0052] The management unit can customize the schedule based on the child's current living situation when managing the schedule. For example, the management unit customizes the schedule based on the child's current living situation when managing the schedule. For example, the management unit can customize the schedule based on the child's current living situation. The management unit can also adjust the schedule according to the child's current living situation. Furthermore, the management unit can customize the schedule based on the child's living situation. In this way, the schedule is customized based on the current living situation. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input data on the child's current living situation into the generation AI and cause the generation AI to customize the schedule.

[0053] The management unit can provide an optimal management method by taking into account the child's geographical location information when managing a schedule. For example, the management unit can provide an optimal management method by taking into account the child's geographical location information when managing a schedule. For example, if a child is studying at home, the management unit can provide a schedule suitable for home study. Furthermore, if a child is studying at school, the management unit can provide a schedule suitable for the school curriculum. Furthermore, if a child is studying at a library, the management unit can provide a schedule that utilizes library resources. This provides an optimal management method based on the geographical location information. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the child's geographical location information into the generation AI and cause the generation AI to provide an optimal management method.

[0054] The management unit can analyze the child's social media activity and propose a schedule when managing the schedule. For example, the management unit can analyze the child's social media activity and propose a schedule when managing the schedule. For example, the management unit can create a schedule based on learning content shared by the child on social media. The management unit can also create a schedule based on information about education-related accounts that the child follows on social media. Furthermore, the management unit can create a schedule based on the activities of learning groups that the child participates in on social media. In this way, a schedule based on social media activity is proposed. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the child's social media activity data into the generation AI and have the generation AI execute schedule proposals.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The monitoring unit not only monitors children's learning progress, but can also detect changes in the learning environment and provide appropriate feedback. For example, the monitoring unit can compare progress data when a child is learning at home and when they are learning at school, and analyze which environment is more effective. Furthermore, when a child is learning online, the monitoring unit can monitor the stability of the internet connection and the performance of the device they are using to identify factors that affect learning efficiency. Furthermore, the monitoring unit can evaluate the effectiveness of the learning materials and tools used by the child while learning and suggest optimal learning resources. This allows for flexible responses to changes in the learning environment.

[0057] The generator not only identifies problems that children cannot understand, but also provides explanations tailored to their learning style. For example, the generator can provide explanations using diagrams and graphs for children who prefer visual learning. For children who prefer auditory learning, the generator can provide audio explanations or podcast-style explanations. For children who prefer tactile learning, the generator can provide explanations through interactive simulations and experiments. This allows the generator to provide the optimal explanation tailored to each child's learning style.

[0058] The planning department not only creates learning plans based on the child's learning data, but also evaluates the degree of achievement of learning goals and amends the plans as necessary. For example, the planning department can regularly evaluate a child's progress toward the learning goals they have set, and if they are having difficulty achieving the goals, it can readjust the learning plan. The planning department can also flexibly change the learning plan to accommodate subjects or areas in which a child has developed a new interest. Furthermore, the planning department can adjust the difficulty and content of the learning plan according to the child's learning pace and level of understanding. This allows for flexible responses toward achieving learning goals.

[0059] The dialogue unit not only motivates students to study by engaging in dialogue with them, but also collects feedback on the students' learning and uses this information to improve their study plans and explanation methods. For example, the dialogue unit can collect any difficulties or questions the students have while studying and use this information to improve their explanation methods. The dialogue unit can also collect the satisfaction and sense of accomplishment the students feel about their studies and use this information to help maintain their motivation for their study plans. Furthermore, the dialogue unit can collect the students' goals and hopes for their studies and customize their study plans based on this information. This makes it possible to provide learning support that utilizes students' feedback.

[0060] The management unit not only keeps track of and notifies the child of cram school homework and test schedules, but can also suggest optimal schedules based on the child's learning rhythm and lifestyle. For example, the management unit can analyze whether the child is a morning person or a night owl and suggest a study schedule accordingly. The management unit can also set efficient study times taking into account how the child spends their weekends and holidays. Furthermore, the management unit can monitor fluctuations in the child's physical condition and mood and flexibly adjust the schedule as necessary. This makes it possible to manage an optimal schedule that suits the child's lifestyle.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The monitoring unit monitors the child's learning progress. For example, the monitoring unit collects the child's learning data and analyzes the progress. The monitoring unit can use the generative AI to monitor the child's learning data in real time and analyze the progress. Step 2: The generation unit generates an explanation based on the learning progress monitored by the monitoring unit. For example, the generation unit identifies a problem that the child does not understand and generates an explanation for that problem. The generation unit can use a generation AI to identify a problem that the child does not understand and generate an explanation for that problem. Step 3: The planning unit presents an individualized learning plan based on the explanations generated by the generation unit. The planning unit creates an optimal learning plan based on, for example, the child's learning data. The planning unit can use the generation AI to create an optimal learning plan based on the child's learning data. Step 4: The dialogue unit motivates the child to learn based on the learning plan presented by the planning unit. For example, the dialogue unit may dialogue with the child to motivate them to learn. The dialogue unit may use a generative AI to dialogue with the child to motivate them to learn. Step 5: The management unit manages the schedule based on the learning motivation provided by the dialogue unit. For example, the management unit keeps track of cram school homework and test schedules and notifies parents and children. The management unit can use the generation AI to keep track of cram school homework and test schedules and notify parents and children.

[0063] (Example 2) A learning support system according to an embodiment of the present invention utilizes a generative AI to manage and support children's learning progress. This learning support system is designed to help dual-income parents understand their children's learning status and enable them to study efficiently. Specifically, it implements a learning progress management function, a learning support function, a learning efficiency maximization function, and a test schedule management function. For example, in this learning support system, a generative AI monitors children's learning progress in real time and generates learning status reports for parents, allowing parents to easily understand their children's learning status. The generative AI also generates explanations for problems that children do not understand and presents individualized learning plans, allowing children to progress at their own pace. Furthermore, the generative AI engages in dialogue to motivate students and provides easy-to-remember, familiar applications and topics, allowing children to continue learning with enjoyment. Finally, the generative AI manages cram school homework and test schedules and notifies parents and children, facilitating schedule management for both parents and children. This allows parents and children to easily manage their schedules. The learning support system thus makes it easy for dual-income parents to understand their children's learning status and enables children to progress efficiently.

[0064] A learning support system according to an embodiment includes a monitoring unit, a generating unit, a planning unit, a dialogue unit, and a management unit. The monitoring unit monitors a child's learning progress. For example, the monitoring unit collects the child's learning data and analyzes the progress. The monitoring unit can monitor the child's learning data in real time and analyze the progress using a generation AI. The generation unit generates explanations based on the learning progress monitored by the monitoring unit. For example, the generation unit identifies problems the child does not understand and generates explanations for those problems. The generation unit can identify problems the child does not understand and generate explanations for those problems using a generation AI. The planning unit presents an individual learning plan based on the explanations generated by the generation unit. For example, the planning unit creates an optimal learning plan based on the child's learning data. The planning unit can create an optimal learning plan based on the child's learning data using a generation AI. The dialogue unit motivates the child to learn based on the learning plan presented by the planning unit. For example, the dialogue unit engages in dialogue with the child to motivate the child to learn. The dialogue unit can use the generation AI to dialogue with the child and motivate them to study. The management unit manages the schedule based on the learning motivation provided by the dialogue unit. The management unit, for example, keeps track of cram school homework and test schedules and notifies parents and children. The management unit can use the generation AI to keep track of cram school homework and test schedules and notify parents and children. As a result, the learning support system according to the embodiment can efficiently monitor learning progress, generate explanations, present learning plans, motivate students to study, and manage schedules.

[0065] The monitoring unit can collect learning data of children and analyze their progress. Learning data includes, but is not limited to, test results, assignment submission status, and study time. The monitoring unit can, for example, collect learning data of children and analyze their progress. For example, the monitoring unit can analyze children's learning progress based on test results. The monitoring unit can also analyze children's learning progress based on assignment submission status. Furthermore, the monitoring unit can analyze children's learning progress based on study time. This makes it possible to collect learning data and analyze their progress. Some or all of the above-mentioned processing in the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input children's learning data into the generation AI and have the generation AI analyze their progress.

[0066] The generation unit can identify questions that a child cannot understand and generate explanations for those questions. For example, the generation unit can identify questions that a child cannot understand and generate explanations for those questions. For example, the generation unit can identify questions that a child cannot understand based on an analysis of test errors. The generation unit can also identify questions that a child cannot understand based on the frequency of questions. Furthermore, the generation unit generates explanations for questions that a child cannot understand. For example, the generation unit can provide step-by-step explanations for math questions. The generation unit can also provide explanations using experiments and observations for science questions. Furthermore, the generation unit can provide explanations using timelines and related events for history questions. In this way, explanations for questions that a child cannot understand are provided. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input questions that a child cannot understand into the generation AI and cause the generation AI to generate explanations.

[0067] The planning unit can create a study plan based on the child's learning data. The planning unit, for example, creates an optimal study plan based on the child's learning data. For example, the planning unit can create a study plan based on the child's test results. The planning unit can also create a study plan based on the child's assignment submission status. Furthermore, the planning unit can create a study plan based on the child's study time. The planning unit can create an optimal study plan based on the child's learning data using the generation AI. For example, the planning unit can input the child's learning data into the generation AI and have the generation AI create an optimal study plan. In this way, an optimal study plan is created. Some or all of the above-mentioned processing in the planning unit may be performed using the generation AI, or may be performed without using the generation AI.

[0068] The dialogue unit can dialogue with the child and motivate the child to learn. The dialogue unit, for example, dialogues with the child and motivates the child to learn. For example, the dialogue unit can discuss with the child the importance of learning and future goals. The dialogue unit can also provide the child with feedback on their learning progress and motivate them to learn. Furthermore, the dialogue unit can introduce a reward system for the child and motivate them to learn. The dialogue unit can dialogue with the child using the generation AI and motivate the child to learn. For example, the dialogue unit can input the content of the dialogue with the child into the generation AI and have the generation AI execute the motivation to learn. This motivates the child to learn. Some or all of the above-mentioned processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI.

[0069] The management unit can keep track of cram school homework and test schedules and notify parents and children. The management unit, for example, keeps track of cram school homework and test schedules and notifies parents and children. For example, the management unit can keep track of cram school homework deadlines and notify parents and children. The management unit can also keep track of test dates and notify parents and children. The management unit can also keep track of study time and notify parents and children. The management unit can use the generation AI to keep track of cram school homework and test schedules and notify parents and children. For example, the management unit can input the cram school homework and test schedules into the generation AI and have the generation AI execute notifications. This makes schedule management easier. Some or all of the above-mentioned processing in the management unit may be performed using the generation AI, or may be performed without using the generation AI.

[0070] The monitoring unit can estimate the child's emotions and adjust the frequency of monitoring the learning progress based on the estimated emotions. The monitoring unit, for example, estimates the child's emotions and adjusts the frequency of monitoring the learning progress based on the estimated emotions. For example, if the child is feeling stressed, the monitoring unit can reduce the monitoring frequency and provide a relaxing environment. Furthermore, if the child is concentrating, the monitoring unit can increase the monitoring frequency and collect detailed progress data. Furthermore, if the child is tired, the monitoring unit can reduce the monitoring frequency to a medium level and send a notification encouraging the child to take a break. This enables monitoring according to the child's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using the generation AI, or may be performed without the generation AI. For example, the monitoring unit can input the child's emotion data into the generation AI and have the generation AI adjust the monitoring frequency.

[0071] The monitoring unit can analyze the child's past learning data and select a monitoring method. The monitoring unit, for example, analyzes the child's past learning data and selects the optimal monitoring method. For example, the monitoring unit can reduce the monitoring frequency for subjects in which the child has previously achieved high grades. The monitoring unit can also increase the monitoring frequency and collect detailed data for subjects in which the child is weak. Furthermore, the monitoring unit can analyze the child's learning patterns and set the optimal monitoring timing. This enables optimal monitoring based on past learning data. Some or all of the above-mentioned processing in the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input the child's past learning data into the generation AI and have the generation AI select the optimal monitoring method.

[0072] The monitoring unit can perform filtering based on the child's current learning environment and concentration level when monitoring learning progress. For example, the monitoring unit can perform filtering based on the child's current learning environment and concentration level when monitoring learning progress. For example, the monitoring unit can collect detailed progress data when the child is studying in a quiet environment. The monitoring unit can also perform filtering to remove noise when the child is studying in a noisy environment. Furthermore, the monitoring unit can collect detailed progress data when the child is concentrating, and perform filtering when the child's concentration level is low. This enables filtering based on the learning environment and concentration level. Some or all of the above-mentioned processing in the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input data on the child's learning environment and concentration level into the generation AI and have the generation AI perform filtering.

[0073] The monitoring unit can estimate a child's emotions and determine the priority of the learning items to be monitored based on the estimated child's emotions. The monitoring unit, for example, estimates a child's emotions and determines the priority of the learning items to be monitored based on the estimated child's emotions. For example, the monitoring unit can prioritize monitoring subjects in which the child is interested. The monitoring unit can also prioritize monitoring subjects in which the child is weak and collect detailed data. Furthermore, the monitoring unit can reduce the monitoring frequency for subjects in which the child feels stressed. This allows the priority of learning items to be determined based on the child's emotions. The 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 monitoring unit may be performed using the generation AI, or may be performed without the generation AI. For example, the monitoring unit can input the child's emotion data into the generation AI and have the generation AI determine the priority of the learning items.

[0074] When monitoring the learning progress, the monitoring unit can prioritize monitoring highly relevant learning data by taking into account the child's geographical location information. For example, when monitoring the learning progress, the monitoring unit prioritizes monitoring highly relevant learning data by taking into account the child's geographical location information. For example, when the child is studying at home, the monitoring unit can prioritize monitoring data related to home study. Furthermore, when the child is studying at school, the monitoring unit can prioritize monitoring data related to the school curriculum. Furthermore, when the child is studying at a library, the monitoring unit can prioritize monitoring data related to library resources. This enables monitoring of highly relevant learning data based on geographical location information. Some or all of the above-described processing by the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input the child's geographical location information into the generation AI and cause the generation AI to monitor highly relevant learning data.

[0075] The monitoring unit can analyze the child's social media activities and monitor related learning data when monitoring learning progress. For example, the monitoring unit can analyze the child's social media activities and monitor related learning data when monitoring learning progress. For example, the monitoring unit can monitor learning content shared by the child on social media and reflect it in the progress data. The monitoring unit can also monitor information on education-related accounts that the child follows on social media. Furthermore, the monitoring unit can monitor the activities of learning groups that the child participates in on social media and collect related data. This makes it possible to monitor learning data based on social media activities. Some or all of the above-mentioned processing by the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input the child's social media activity data into the generation AI and cause the generation AI to monitor the related learning data.

[0076] The generation unit can estimate the child's emotions and adjust the manner in which the commentary is expressed based on the estimated child's emotions. The generation unit, for example, estimates the child's emotions and adjusts the manner in which the commentary is expressed based on the estimated child's emotions. For example, the generation unit can provide detailed commentary when the child is relaxed. Furthermore, the generation unit can provide concise and to-the-point commentary when the child is stressed. Furthermore, the generation unit can provide commentary with visually stimulating effects when the child is excited. This adjusts the manner in which the commentary is expressed based on the child's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the manner in which the commentary is expressed.

[0077] The generation unit can adjust the level of detail of the explanation based on the importance of the question when generating the explanation. For example, the generation unit can provide detailed step-by-step explanations for important questions. The generation unit can also provide concise explanations for less important questions. Furthermore, the generation unit can adjust the level of detail of the explanation based on the child's grade and level of understanding. This adjusts the level of detail of the explanation based on the importance of the question. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input question importance data to the generation AI and cause the generation AI to adjust the level of detail of the explanation.

[0078] The generation unit can apply different explanation algorithms depending on the problem category when generating an explanation. For example, the generation unit can apply different explanation algorithms depending on the problem category when generating an explanation. For example, the generation unit can apply an explanation algorithm using mathematical formulas to a math problem. The generation unit can also apply an explanation algorithm using a timeline or related events to a history problem. Furthermore, the generation unit can apply an explanation algorithm using experiments or observations to a science problem. In this way, an explanation algorithm depending on the problem category is applied. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input problem category data into the generation AI and cause the generation AI to apply the explanation algorithm.

[0079] The generation unit can estimate the child's emotions and adjust the length of the commentary based on the estimated child's emotions. The generation unit, for example, estimates the child's emotions and adjusts the length of the commentary based on the estimated child's emotions. For example, if the child is in a hurry, the generation unit can provide a short, concise commentary. If the child is relaxed, the generation unit can provide a detailed commentary. If the child is excited, the generation unit can provide a commentary with visually stimulating effects. This adjusts the length of the commentary according to the child's emotions. The 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 these examples. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the length of the commentary.

[0080] The generation unit can determine the priority of explanations based on the time of question submission when generating explanations. The generation unit, for example, determines the priority of explanations based on the time of question submission when generating explanations. For example, the generation unit can prioritize providing explanations for questions with an approaching deadline. The generation unit can also postpone explanations for questions with a distant submission deadline. Furthermore, the generation unit can provide review explanations for questions whose submission deadline has passed. In this way, the priority of explanations is determined based on the time of submission. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input question submission time data into the generation AI and have the generation AI determine the priority of explanations.

[0081] The generation unit can adjust the order of explanations based on the relevance of the questions when generating explanations. For example, the generation unit can adjust the order of explanations based on the relevance of the questions when generating explanations. For example, the generation unit can prioritize explanations for highly relevant questions. The generation unit can also postpone explanations for less relevant questions. Furthermore, the generation unit can adjust the order of explanations according to the child's learning progress. In this way, the order of explanations based on the relevance of the questions is adjusted. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input question relevance data into the generation AI and cause the generation AI to adjust the order of explanations.

[0082] The planning unit can estimate the child's emotions and adjust the content of the lesson plan based on the estimated child's emotions. For example, the planning unit can estimate the child's emotions and adjust the content of the lesson plan based on the estimated child's emotions. For example, if the child is relaxed, the planning unit can provide a detailed lesson plan. If the child is stressed, the planning unit can provide a concise and to-the-point lesson plan. If the child is excited, the planning unit can provide a lesson plan with visually stimulating effects. In this way, the content of the lesson plan is adjusted according to the child'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-mentioned processing in the planning unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the planning unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the content of the lesson plan.

[0083] When creating a study plan, the planning unit can analyze the child's past study data to create the plan. For example, when creating a study plan, the planning unit can analyze the child's past study data to create an optimal study plan. For example, the planning unit can simplify the study plan for subjects in which the child has achieved high grades in the past. The planning unit can also provide a detailed study plan for subjects in which the child is weak. Furthermore, the planning unit can analyze the child's study patterns and create an optimal study plan. In this way, an optimal study plan based on the past study data is created. Some or all of the above-mentioned processing in the planning unit may be performed using or without the generation AI. For example, the planning unit can input the child's past study data into the generation AI and cause the generation AI to create an optimal study plan.

[0084] The planning unit can customize the learning plan based on the child's current learning situation when creating the learning plan. For example, the planning unit customizes the plan based on the child's current learning situation when creating the learning plan. For example, the planning unit can customize the learning plan based on the learning content currently being done by the child. The planning unit can also adjust the learning plan according to the child's current learning situation. Furthermore, the planning unit can customize the learning plan based on the child's learning progress. In this way, the learning plan is customized based on the current learning situation. Some or all of the above-mentioned processing in the planning unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the planning unit can input data on the child's current learning situation into the generation AI and cause the generation AI to customize the learning plan.

[0085] The planning unit can estimate the child's emotions and determine the priority of the lesson plan based on the estimated child's emotions. The planning unit, for example, estimates the child's emotions and determines the priority of the lesson plan based on the estimated child's emotions. For example, the planning unit can prioritize subjects that the child is interested in into the lesson plan. The planning unit can also prioritize subjects that the child is weak in into the lesson plan. Furthermore, the planning unit can lower the priority of the lesson plan for subjects that the child feels stressed about. In this way, the priority of the lesson plan is determined according to the child's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the planning unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the planning unit can input the child's emotion data into the generation AI and have the generation AI determine the priority of the lesson plan.

[0086] The planning unit can create a study plan taking into account the child's geographical location information when creating the plan. For example, when creating a study plan, the planning unit creates an optimal plan taking into account the child's geographical location information. For example, if the child is studying at home, the planning unit can create a plan suitable for home study. Furthermore, if the child is studying at school, the planning unit can create a plan suitable for the school curriculum. Furthermore, if the child is studying at a library, the planning unit can create a plan that utilizes library resources. In this way, an optimal study plan based on the geographical location information is created. Some or all of the above-mentioned processing in the planning unit may be performed using or without the generation AI. For example, the planning unit can input the child's geographical location information into the generation AI and cause the generation AI to create an optimal study plan.

[0087] The planning unit can analyze the child's social media activity and propose a lesson plan when creating the lesson plan. For example, the planning unit can analyze the child's social media activity and propose a lesson plan when creating the lesson plan. For example, the planning unit can create a lesson plan based on learning content shared by the child on social media. The planning unit can also create a lesson plan based on information about education-related accounts that the child follows on social media. Furthermore, the planning unit can create a lesson plan based on the activities of learning groups that the child participates in on social media. In this way, a lesson plan based on social media activity is proposed. Some or all of the above-mentioned processing in the planning unit may be performed using or without the generation AI. For example, the planning unit can input the child's social media activity data into the generation AI and have the generation AI execute the proposed lesson plan.

[0088] The dialogue unit can estimate the child's emotions and adjust the dialogue content based on the estimated child's emotions. The dialogue unit, for example, estimates the child's emotions and adjusts the dialogue content based on the estimated child's emotions. For example, the dialogue unit can provide detailed dialogue content when the child is relaxed. Furthermore, the dialogue unit can provide concise and to-the-point dialogue content when the child is stressed. Furthermore, the dialogue unit can provide dialogue content with visually stimulating effects when the child is excited. This adjusts the dialogue content according to the child's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit can input the child's emotion data into the generation AI and have the generation AI adjust the dialogue content.

[0089] The dialogue unit can provide dialogue content by referring to the child's past dialogue history during dialogue. The dialogue unit can, for example, provide optimal dialogue content by referring to the child's past dialogue history during dialogue. For example, the dialogue unit can provide dialogue content based on topics in which the child has shown interest in the past. The dialogue unit can also re-engage with the child about problems that the child was unable to understand in the past. Furthermore, the dialogue unit can analyze the child's past dialogue history and provide optimal dialogue content. This allows optimal dialogue content to be provided based on the past dialogue history. Some or all of the above-mentioned processing in the dialogue unit may be performed using or without the generation AI. For example, the dialogue unit can input the child's past dialogue history data into the generation AI and have the generation AI provide the dialogue content.

[0090] The dialogue unit can customize the dialogue content based on the child's current learning situation during dialogue. The dialogue unit customizes the dialogue content based on the child's current learning situation during dialogue, for example. For example, the dialogue unit can customize the dialogue content based on the learning content currently in progress of the child. The dialogue unit can also adjust the dialogue content according to the child's current learning situation. Furthermore, the dialogue unit can customize the dialogue content based on the child's learning progress. In this way, the dialogue content is customized based on the current learning situation. Some or all of the above-mentioned processing in the dialogue unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the dialogue unit can input data on the child's current learning situation into the generation AI and cause the generation AI to customize the dialogue content.

[0091] The dialogue unit can estimate the child's emotions and determine the priority of dialogues based on the estimated child's emotions. The dialogue unit, for example, estimates the child's emotions and determines the priority of dialogues based on the estimated child's emotions. For example, the dialogue unit can prioritize topics that the child is interested in into the dialogue content. The dialogue unit can also prioritize topics that the child finds difficult into the dialogue content. Furthermore, the dialogue unit can lower the priority of dialogues for topics that the child finds stressful. In this way, the priority of dialogues is determined according to the child's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit can input the child's emotion data into the generation AI and have the generation AI determine the priority of dialogues.

[0092] The dialogue unit can provide optimal dialogue content by taking into account the child's geographical location information during dialogue. For example, the dialogue unit can provide optimal dialogue content by taking into account the child's geographical location information during dialogue. For example, if the child is studying at home, the dialogue unit can provide dialogue content related to home learning. Furthermore, if the child is studying at school, the dialogue unit can provide dialogue content related to the school curriculum. Furthermore, if the child is studying at a library, the dialogue unit can provide dialogue content related to library resources. In this way, optimal dialogue content is provided based on the geographical location information. Some or all of the above-mentioned processing in the dialogue unit may be performed using or without the generation AI. For example, the dialogue unit can input the child's geographical location information into the generation AI and cause the generation AI to provide optimal dialogue content.

[0093] The dialogue unit can analyze the child's social media activity during the dialogue and suggest dialogue content. For example, the dialogue unit can analyze the child's social media activity during the dialogue and suggest dialogue content. For example, the dialogue unit can provide dialogue content based on learning content shared by the child on social media. The dialogue unit can also provide dialogue content based on information about education-related accounts the child follows on social media. Furthermore, the dialogue unit can also provide dialogue content based on the activities of learning groups the child participates in on social media. In this way, dialogue content based on social media activity is suggested. Some or all of the above-mentioned processing in the dialogue unit may be performed using or without the generation AI. For example, the dialogue unit can input the child's social media activity data into the generation AI and cause the generation AI to suggest dialogue content.

[0094] The management unit can estimate the child's emotions and adjust the schedule management method based on the estimated child's emotions. For example, the management unit can estimate the child's emotions and adjust the schedule management method based on the estimated child's emotions. For example, if the child is relaxed, the management unit can provide a detailed schedule. If the child is stressed, the management unit can provide a concise and concise schedule. If the child is excited, the management unit can provide a schedule with visually stimulating effects. This adjusts the schedule management method according to the child'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 these examples. Some or all of the above-mentioned processing in the management unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the management unit can input the child's emotion data into the generation AI and have the generation AI adjust the schedule management method.

[0095] The management unit can provide a management method by referring to the child's past schedule history when managing a schedule. For example, the management unit can provide an optimal management method by referring to the child's past schedule history when managing a schedule. For example, the management unit can provide an optimal schedule based on schedules in which the child achieved high grades in the past. The management unit can also adjust the schedule to avoid schedules in which the child has previously struggled. Furthermore, the management unit can analyze the child's past schedule history and provide an optimal management method. This provides an optimal management method based on the past schedule history. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the child's past schedule history data into the generation AI and have the generation AI provide a management method.

[0096] The management unit can customize the schedule based on the child's current living situation when managing the schedule. For example, the management unit customizes the schedule based on the child's current living situation when managing the schedule. For example, the management unit can customize the schedule based on the child's current living situation. The management unit can also adjust the schedule according to the child's current living situation. Furthermore, the management unit can customize the schedule based on the child's living situation. In this way, the schedule is customized based on the current living situation. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input data on the child's current living situation into the generation AI and cause the generation AI to customize the schedule.

[0097] The management unit can estimate the child's emotions and determine schedule management priorities based on the estimated child's emotions. The management unit, for example, estimates the child's emotions and determines schedule management priorities based on the estimated child's emotions. For example, the management unit can prioritize activities that the child is interested in in the schedule. The management unit can also prioritize activities that the child is not good at in the schedule. Furthermore, the management unit can lower the schedule priority for activities that the child finds stressful. In this way, schedule management priorities are determined according to the child'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 these examples. Some or all of the above-mentioned processing in the management unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the management unit can input the child's emotion data into the generation AI and have the generation AI determine the schedule management priorities.

[0098] The management unit can provide an optimal management method by taking into account the child's geographical location information when managing a schedule. For example, the management unit can provide an optimal management method by taking into account the child's geographical location information when managing a schedule. For example, if a child is studying at home, the management unit can provide a schedule suitable for home study. Furthermore, if a child is studying at school, the management unit can provide a schedule suitable for the school curriculum. Furthermore, if a child is studying at a library, the management unit can provide a schedule that utilizes library resources. This provides an optimal management method based on the geographical location information. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the child's geographical location information into the generation AI and cause the generation AI to provide an optimal management method.

[0099] The management unit can analyze the child's social media activity and propose a schedule when managing the schedule. For example, the management unit can analyze the child's social media activity and propose a schedule when managing the schedule. For example, the management unit can create a schedule based on learning content shared by the child on social media. The management unit can also create a schedule based on information about education-related accounts that the child follows on social media. Furthermore, the management unit can create a schedule based on the activities of learning groups that the child participates in on social media. In this way, a schedule based on social media activity is proposed. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the child's social media activity data into the generation AI and have the generation AI execute schedule proposals. === Hard Collateral 1-1 === Each of the multiple elements, including the monitoring unit, generating unit, planning unit, dialogue unit, and management unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the monitoring unit collects the child's learning data using the camera 42 and microphone 38B of the smart device 14 and analyzes the child's progress using the control unit 46A. The generating unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and generates explanations for problems the child does not understand. The planning unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and creates an individual learning plan. The dialogue unit, for example, is realized by the control unit 46A of the smart device 14 and interacts with the child to motivate them to study. The management unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and keeps track of cram school homework and test schedules and notifies parents and children of the same. === Hard Collateral 1-2 === Each of the multiple elements, including the monitoring unit, generating unit, planning unit, dialogue unit, and management unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the monitoring unit collects the child's learning data using the camera 42 and microphone 238 of the smart glasses 214 and analyzes the child's progress using the control unit 46A. The generating unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and generates explanations for problems the child does not understand. The planning unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and creates an individual learning plan. The dialogue unit, for example, is realized by the control unit 46A of the smart glasses 214 and dialogues with the child to motivate them to study. The management unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and keeps track of cram school homework and test schedules and notifies parents and children of the same. === Hard Collateral 1-3 === Each of the multiple elements, including the monitoring unit, generating unit, planning unit, dialogue unit, and management unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the monitoring unit collects the child's learning data using the camera 42 and microphone 238 of the headset-type terminal 314 and analyzes the child's progress using the control unit 46A. The generating unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates explanations for problems the child does not understand. The planning unit, realized, for example, by the specific processing unit 290 of the data processing device 12, creates an individual learning plan. The dialogue unit, realized, for example, by the control unit 46A of the headset-type terminal 314, dialogues with the child to motivate them to study. The management unit, realized, for example, by the specific processing unit 290 of the data processing device 12, keeps track of cram school homework and test schedules and notifies parents and children of the same. === Hard Collateral 1-4 === Each of the multiple elements, including the monitoring unit, generating unit, planning unit, dialogue unit, and management unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the monitoring unit collects the child's learning data using the camera 42 and microphone 238 of the robot 414 and analyzes the child's progress using the control unit 46A. The generating unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates explanations for problems the child does not understand. The planning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates an individual learning plan. The dialogue unit is realized, for example, by the control unit 46A of the robot 414 and dialogues with the child to motivate them to study. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and keeps track of cram school homework and test schedules and notifies parents and children of the same.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The monitoring unit not only monitors children's learning progress, but can also detect changes in the learning environment and provide appropriate feedback. For example, the monitoring unit can compare progress data when a child is learning at home and when they are learning at school, and analyze which environment is more effective. Furthermore, when a child is learning online, the monitoring unit can monitor the stability of the internet connection and the performance of the device they are using to identify factors that affect learning efficiency. Furthermore, the monitoring unit can evaluate the effectiveness of the learning materials and tools used by the child while learning and suggest optimal learning resources. This allows for flexible responses to changes in the learning environment.

[0102] The generator not only identifies problems that children cannot understand, but also provides explanations tailored to their learning style. For example, the generator can provide explanations using diagrams and graphs for children who prefer visual learning. For children who prefer auditory learning, the generator can provide audio explanations or podcast-style explanations. For children who prefer tactile learning, the generator can provide explanations through interactive simulations and experiments. This allows the generator to provide the optimal explanation tailored to each child's learning style.

[0103] The planning department not only creates learning plans based on the child's learning data, but also evaluates the degree of achievement of learning goals and amends the plans as necessary. For example, the planning department can regularly evaluate a child's progress toward the learning goals they have set, and if they are having difficulty achieving the goals, it can readjust the learning plan. The planning department can also flexibly change the learning plan to accommodate subjects or areas in which a child has developed a new interest. Furthermore, the planning department can adjust the difficulty and content of the learning plan according to the child's learning pace and level of understanding. This allows for flexible responses toward achieving learning goals.

[0104] The dialogue unit not only motivates students to study by engaging in dialogue with them, but also collects feedback on the students' learning and uses this information to improve their study plans and explanation methods. For example, the dialogue unit can collect any difficulties or questions the students have while studying and use this information to improve their explanation methods. The dialogue unit can also collect the satisfaction and sense of accomplishment the students feel about their studies and use this information to help maintain their motivation for their study plans. Furthermore, the dialogue unit can collect the students' goals and hopes for their studies and customize their study plans based on this information. This makes it possible to provide learning support that utilizes students' feedback.

[0105] The management unit not only keeps track of and notifies the child of cram school homework and test schedules, but can also suggest optimal schedules based on the child's learning rhythm and lifestyle. For example, the management unit can analyze whether the child is a morning person or a night owl and suggest a study schedule accordingly. The management unit can also set efficient study times taking into account how the child spends their weekends and holidays. Furthermore, the management unit can monitor fluctuations in the child's physical condition and mood and flexibly adjust the schedule as necessary. This makes it possible to manage an optimal schedule that suits the child's lifestyle.

[0106] The monitoring unit can estimate a child's emotions and adjust the frequency of monitoring the child's learning progress based on the estimated emotions, as well as optimize the learning environment in response to changes in emotions. For example, if a child is feeling stressed, the monitoring unit can provide relaxing music or videos. If a child is concentrating, the monitoring unit can minimize notifications and alerts to provide an environment where the child can concentrate on their studies. Furthermore, if a child is tired, the monitoring unit can suggest a short break or an activity to refresh them. This makes it possible to optimize the learning environment in response to a child's emotions.

[0107] The generation unit not only estimates the child's emotions and adjusts the way the explanations are presented based on the estimated child's emotions, but also selects a learning module according to the emotions. For example, if the child is relaxed, the generation unit can provide a learning module including difficult questions. If the child is stressed, the generation unit can provide a learning module focusing on basic content. Furthermore, if the child is excited, the generation unit can provide an interactive game-style learning module. This makes it possible to select the optimal learning module according to the child's emotions.

[0108] The planning unit not only estimates the child's emotions and adjusts the content of the lesson plan based on the estimated child's emotions, but also sets learning goals according to the emotions. For example, if the child is relaxed, the planning unit can set long-term learning goals. Also, if the child is feeling stressed, the planning unit can set short-term learning goals. Furthermore, if the child is excited, the planning unit can set challenging learning goals. This makes it possible to set learning goals according to the child's emotions.

[0109] The dialogue unit not only estimates the child's emotions and adjusts the content of the dialogue based on the estimated child's emotions, but also adjusts the timing of the dialogue according to the emotions. For example, if the child is relaxed, the dialogue unit can provide relaxing dialogue between study sessions. Also, if the child is feeling stressed, the dialogue unit can provide refreshing dialogue after study sessions. Furthermore, if the child is excited, the dialogue unit can provide motivational dialogue before study sessions. This provides optimal dialogue timing according to the child's emotions.

[0110] The management unit not only estimates a child's emotions and adjusts the schedule management method based on the estimated child's emotions, but also sets break times according to the emotions. For example, if a child is relaxed, the management unit can set longer study sessions and provide short breaks to maintain concentration. Also, if a child is feeling stressed, the management unit can set shorter study sessions and provide longer breaks to refresh. Furthermore, if a child is excited, the management unit can provide a schedule that alternates between study and breaks. This makes it possible to set optimal break times according to a child's emotions.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The monitoring unit monitors the child's learning progress. For example, the monitoring unit collects the child's learning data and analyzes the progress. The monitoring unit can use the generative AI to monitor the child's learning data in real time and analyze the progress. Step 2: The generation unit generates an explanation based on the learning progress monitored by the monitoring unit. For example, the generation unit identifies a problem that the child does not understand and generates an explanation for that problem. The generation unit can use a generation AI to identify a problem that the child does not understand and generate an explanation for that problem. Step 3: The planning unit presents an individualized learning plan based on the explanations generated by the generation unit. The planning unit creates an optimal learning plan based on, for example, the child's learning data. The planning unit can use the generation AI to create an optimal learning plan based on the child's learning data. Step 4: The dialogue unit motivates the child to learn based on the learning plan presented by the planning unit. For example, the dialogue unit may dialogue with the child to motivate them to learn. The dialogue unit may use a generative AI to dialogue with the child to motivate them to learn. Step 5: The management unit manages the schedule based on the learning motivation provided by the dialogue unit. For example, the management unit keeps track of cram school homework and test schedules and notifies parents and children. The management unit can use the generation AI to keep track of cram school homework and test schedules and notify parents and children.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 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 monitoring unit that monitors learning progress; a generation unit that generates a commentary based on the learning progress monitored by the monitoring unit; a planning unit that presents an individual learning plan based on the commentary generated by the generation unit; a dialogue unit that motivates the learner to learn based on the learning plan presented by the planning unit; a management unit that manages a schedule based on the learning motivation provided by the dialogue unit; Equipped with A system characterized by:

2. The monitoring unit Collecting children's learning data and analyzing their progress The system of claim 1 .

3. The generation unit Identify problems that children do not understand and generate explanations for those problems The system of claim 1 .

4. The planning unit Create a learning plan based on your child's learning data The system of claim 1 .

5. The dialogue unit Dialogue with children and motivate them to learn The system of claim 1 .

6. The management unit Keep track of homework and test schedules and notify parents and children The system of claim 1 .

7. The monitoring unit Estimate the child's emotions and adjust the frequency of monitoring the learning progress based on the estimated emotions. The system of claim 1 .

8. The monitoring unit Analyze children's past learning data and select monitoring methods The system of claim 1 .

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