System

Abstract

An object of the system according to the embodiment is to propose an optimal learning plan or course based on a learning style, a personality, an interest, and an interest of a child.SOLUTION: A system according to an embodiment includes a collection unit, a generation unit, a support unit, and a suggestion unit. The collection unit collects information for grasping a learning style of the child. The generation unit generates an original learning plan based on the information collected by the collection unit. The support unit supports learning based on the learning plan created by the generation unit. The proposal part analyzes the character, interest and concern of the child and proposes an appropriate junior high school.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately propose optimal learning plans and career paths based on a child's learning style, personality, interests, and concerns, and there is room for improvement.

[0005] The system according to the embodiment aims to propose optimal learning plans and career paths based on a child's learning style, personality, interests, and concerns. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, a support unit, and a suggestion unit. The collection unit collects information to understand a child's learning style. The generation unit creates an original learning plan based on the information collected by the collection unit. The support unit supports learning based on the learning plan created by the generation unit. The suggestion unit analyzes the child's personality, interests, and concerns, and suggests an appropriate junior high school. [Effects of the Invention]

[0007] The system according to the embodiment can suggest optimal learning plans and career paths based on a child's learning style, personality, interests, and concerns. [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 is a system that enables children to study independently in preparation for junior high school entrance exams and achieve their ideal career path. The learning support system collects information to understand a child's learning style, and a generation AI creates an original study plan, supports the child's learning, and suggests the most suitable junior high school. For example, the learning support system uses online tests and questionnaires to collect information about a child's learning style, personality, and interests. The generation AI then creates an original study plan based on the collected information. The generation AI uses an algorithm to generate an optimal study plan based on past learning data and learning outcomes. The generation AI then analyzes the child's personality, interests, and concerns to suggest the most suitable junior high school. The generation AI then provides a list of junior high schools, taking into account the school's curriculum, educational policy, past admissions records, and other factors. This allows the learning support system to enable children to study independently and achieve their ideal career path. This allows the learning support system to provide effective support for children to study independently and achieve their ideal career path. For example, children can study according to a plan that suits their learning style and obtain information to select the most suitable junior high school. Parents can also understand their children's learning progress and provide appropriate support.

[0029] A learning support system according to an embodiment includes a collection unit, a generation unit, a support unit, and a suggestion unit. The collection unit collects information to understand a child's learning style. The collection unit collects information about a child's learning style, personality, and interests, for example, using online tests and questionnaires. The collection unit can also analyze a child's past learning history and select an optimal information collection method. For example, the collection unit can prioritize collecting information about a child's favorite subjects from the child's past learning history. The generation unit uses a generation AI to create an original study plan based on the information collected by the collection unit. The generation unit uses, for example, an algorithm that generates an optimal study plan based on past learning data and learning outcomes. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. The support unit supports learning based on the study plan created by the generation unit. For example, the support unit may provide a study schedule and teaching materials based on the generated study plan. The suggestion unit uses the generation AI to analyze a child's personality, interests, and concerns and suggest an optimal junior high school. The suggestion unit provides a list of junior high schools, taking into account, for example, the school's curriculum, educational policy, past admissions record, etc. As a result, the learning support system according to the embodiment can provide effective support for children to study independently and realize their ideal career path. For example, children can proceed with their studies according to a plan that suits their learning style and obtain information for selecting the most suitable junior high school. Furthermore, parents can understand their children's learning situation and provide appropriate support.

[0030] The collection unit can collect information about a child's learning style, personality, and interests using online tests or questionnaires. Examples of online tests or questionnaires include, but are not limited to, academic achievement tests and personality assessment questionnaires. For example, the collection unit can conduct online tests to understand a child's learning style. The collection unit can also conduct questionnaires to collect information about a child's personality and interests. For example, the collection unit can conduct online tests when a child is relaxed to accurately understand a child's learning style. The collection unit can also conduct questionnaires when a child is concentrating to collect detailed information about the child's interests. In this way, by using online tests or questionnaires, a child's learning style, personality, and interests can be understood in detail. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the results of online tests or questionnaires into a generation AI and have the generation AI analyze the child's learning style, personality, and interests.

[0031] The generation unit can use an algorithm that generates an appropriate lesson plan based on past learning data and learning outcomes. Examples of past learning data and learning outcomes include, but are not limited to, test results and homework submission status. For example, the generation unit can generate a lesson plan that focuses on a child's weak areas based on past test results. The generation unit can also analyze homework submission status to generate a plan that allows a child to study effectively. For example, the generation unit can generate a lesson plan that focuses on a child's strong areas based on past learning outcomes. The generation unit can also analyze past learning outcomes to generate a lesson plan that reinforces a child's weak areas. This enables effective learning support by generating an optimal lesson plan based on past learning data and learning outcomes. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input past learning data and learning outcomes into the generation AI and cause the generation AI to generate an optimal lesson plan.

[0032] The support unit can support learning based on the generated learning plan. The generated learning plan includes, for example, a learning schedule, a method for providing learning materials, etc., but is not limited to these examples. For example, the support unit provides a learning schedule based on the generated learning plan. The support unit can also provide learning materials based on the generated learning plan. For example, the support unit supports a child to study efficiently based on the learning schedule. The support unit can also provide learning materials needed by the child based on the generated learning plan. In this way, by supporting learning based on the generated learning plan, the child's learning effectiveness can be improved. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the generated learning plan into the generation AI and cause the generation AI to execute the learning support method.

[0033] The suggestion unit can suggest an appropriate junior high school by taking into consideration the school's curriculum or educational policy, past admission records, etc. Examples of the school's curriculum or educational policy include, but are not limited to, the type of subject and educational philosophy. The suggestion unit can, for example, suggest the junior high school that is best suited to a child based on the school's curriculum. The suggestion unit can also suggest a junior high school that is best suited to a child by taking into consideration the educational policy. For example, the suggestion unit can suggest a junior high school that is likely to be accepted by a child based on past admission records. The suggestion unit can also analyze the school's curriculum and educational policy to suggest the junior high school that is best suited to a child. This allows the suggestion of the junior high school that is best suited to a child by taking into consideration the school's curriculum, educational policy, past admission records, etc. Some or all of the above-mentioned processing by the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input data on the school's curriculum, educational policy, and past admission records into the generation AI and have the generation AI suggest the optimal junior high school.

[0034] The suggestion unit can provide a list of suggested junior high schools. The list of junior high schools includes, for example, school names, locations, and characteristics, but is not limited to these examples. The suggestion unit, for example, provides a list of suggested junior high schools. The suggestion unit can also make it easier for parents and children to compare and consider options based on the list of junior high schools. For example, the suggestion unit provides a list of junior high schools and explains the characteristics of each school. The suggestion unit can also provide a list of junior high schools so that parents and children can compare and consider options. In this way, providing a list of suggested junior high schools makes it easier for parents and children to compare and consider options. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the list of junior high schools into a generation AI and cause the generation AI to generate the list.

[0035] The collection unit can analyze the child's past learning history and select the optimal information collection method. The past learning history includes, for example, study time, learning content, grades, etc., but is not limited to these examples. For example, the collection unit prioritizes collecting information about subjects in which the child is good at from the past learning history. The collection unit can also collect detailed information about subjects in which the child is weak based on the past learning history. For example, the collection unit analyzes the past learning history and collects information during times when the child can study most effectively. In this way, by analyzing the past learning history, the optimal information collection method for the child can be selected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past learning history into a generation AI and cause the generation AI to select the optimal information collection method.

[0036] When collecting information, the collection unit can filter the information based on the child's current learning situation and areas of interest. Examples of the current learning situation and areas of interest include, but are not limited to, learning progress and subjects of interest. For example, the collection unit can prioritize collecting information about subjects in which progress is lagging based on the child's current learning situation. The collection unit can also collect interesting learning materials and resources based on the child's areas of interest. For example, the collection unit can analyze the current learning situation and filter and provide necessary supplemental information. This allows for efficient collection of necessary information by filtering based on the current learning situation and areas of interest. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the child's current learning situation and areas of interest into the generation AI and have the generation AI perform filtering.

[0037] When collecting information, the collection unit can select the optimal collection means depending on the child's input method. Examples of the child's input method include, but are not limited to, voice input, text input, and image input. For example, if the child prefers voice input, the collection unit provides a questionnaire that can be answered by voice. Furthermore, if the child prefers text input, the collection unit can also administer a text-based online test. For example, if the child prefers image input, the collection unit can perform an image-based learning style diagnosis. This improves the efficiency of information collection by selecting the optimal collection means depending on the child's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the child's input data into a generation AI and have the generation AI select the optimal collection means.

[0038] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the child's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, the collection unit prioritizes collecting educational information in the area where the child lives. The collection unit can also prioritize collecting safety information related to the child's route to school. For example, the collection unit prioritizes collecting information about learning events and workshops in the child's area. This allows for efficient collection of highly relevant information by taking the geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0039] When collecting information, the collection unit can analyze the child's social media activities and collect related information. Social media activities include, but are not limited to, the content of posts, the number of likes, and the number of followers. For example, the collection unit can collect information on topics in which the child is interested on social media. The collection unit can also collect related learning information by referring to the activities of the child's friends on social media. For example, the collection unit can analyze the content of the child's social media posts and collect information that piques their interest. In this way, information related to the child can be efficiently collected by analyzing social media activities. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data on social media activities into the generation AI and cause the generation AI to collect related information.

[0040] When collecting information, the collection unit can customize the collection method by reflecting the child's past feedback. Past feedback includes, but is not limited to, survey results, comments, etc. For example, the collection unit preferentially uses a collection method that the child has previously preferred. The collection unit can also avoid a collection method that the child has previously expressed dissatisfaction with. For example, the collection unit improves and optimizes the collection method based on the child's past feedback. In this way, the collection method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data of past feedback into the generation AI and have the generation AI customize the collection method.

[0041] When generating a study plan, the generation unit can adjust the level of detail of the plan based on the importance of the study content. Examples of the importance of the study content include, but are not limited to, test scope and study goals. For example, the generation unit generates a plan that includes detailed explanations and practice questions for important study content. The generation unit can also generate a plan that includes concise explanations and basic practice questions for less important study content. For example, the generation unit generates a plan that adjusts the allocation of study time according to the importance of the study content. This allows for an effective study plan to be provided by adjusting the level of detail of the plan based on the importance of the study content. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input data on the importance of the study content into the generation AI and have the generation AI adjust the level of detail of the plan.

[0042] When generating a study plan, the generation unit can apply different generation algorithms depending on the study subject. Examples of different generation algorithms depending on the study subject include, but are not limited to, a mathematics algorithm and a language algorithm. For example, the generation unit can apply a problem-solving algorithm to a mathematics study plan. The generation unit can also apply a vocabulary enhancement algorithm to an English study plan. For example, the generation unit can apply an experiment simulation algorithm to a science study plan. By applying different generation algorithms depending on the study subject, it is possible to provide an optimal study plan for each subject. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input data on the study subject into the generation AI and cause the generation AI to apply different generation algorithms.

[0043] When generating a learning plan, the generation unit can improve the accuracy of the plan by referring to the child's past learning results. Past learning results include, but are not limited to, test scores and homework submission status. For example, the generation unit can generate a plan that focuses on areas in which the child is weak based on the past learning results. The generation unit can also analyze past learning results and generate a plan that incorporates effective learning methods. For example, the generation unit can generate a plan based on the child's learning progress by referring to the past learning results. In this way, the accuracy of the plan can be improved by referring to the past learning results. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input data on past learning results into the generation AI and have the generation AI improve the accuracy of the plan.

[0044] When generating a study plan, the generation unit can determine the priority of the plan based on the student's learning progress. The learning progress includes, but is not limited to, the level of achievement and the number of uncompleted tasks. For example, the generation unit generates a plan that prioritizes learning of subjects for which the student's learning progress is lagging behind. The generation unit can also generate a plan that focuses on review for subjects for which the student's learning progress is steady. For example, the generation unit generates a plan that adjusts the allocation of study time according to the student's learning progress. This enables effective study support by determining the priority of plans based on the student's learning progress. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input data on the student's learning progress into the generation AI and have the generation AI determine the priority of the plans.

[0045] When generating a study plan, the generation unit can adjust the order of the plan based on the relevance of the study content. Examples of the relevance of the study content include, but are not limited to, the presence or absence of prerequisite knowledge and the relevance between subjects. For example, the generation unit generates a plan in which highly relevant study content is studied consecutively. The generation unit can also generate a plan in which less relevant study content is studied at intervals. For example, the generation unit generates a plan that determines an efficient study order based on the relevance of the study content. This enables efficient study by adjusting the order of the plan based on the relevance of the study content. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input relevance data of the study content into the generation AI and have the generation AI adjust the order of the plan.

[0046] When generating a learning plan, the generation unit can adjust the content of the plan according to the child's grade and academic level. Examples of the child's grade and academic level include, but are not limited to, grade-specific curricula and standardized test results. The generation unit, for example, generates a learning plan with a difficulty level according to the child's grade. The generation unit can also generate learning plans ranging from basic to advanced according to the child's academic level. For example, the generation unit generates a plan including appropriate teaching materials and resources based on the child's grade and academic level. This allows the content of the plan to be adjusted according to the child's grade and academic level, thereby providing an appropriate learning plan. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the child's grade and academic level into the generation AI and have the generation AI adjust the content of the plan.

[0047] When providing learning support, the support unit can analyze the child's past learning behavior and select the optimal support method. Past learning behavior includes, but is not limited to, for example, study time, learning content, and learning outcomes. For example, the support unit selects a support method that will enable the child to study effectively based on the child's past learning behavior. The support unit can also analyze the child's past learning behavior and select a support method that focuses on areas in which the child is weak. For example, the support unit selects a support method that matches the child's preferred learning style with reference to the child's past learning behavior. In this way, the analysis of the child's past learning behavior can select the optimal support method for the child. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input data on the child's past learning behavior into the generation AI and have the generation AI select the optimal support method.

[0048] When providing learning support, the support unit can customize the support means based on the child's current learning situation. Examples of the current learning situation include, but are not limited to, progress and level of understanding. For example, the support unit can provide support means that focus on subjects where progress is lagging based on the current learning situation. The support unit can also analyze the current learning situation and customize the support means to provide necessary supplemental information. For example, the support unit can provide support means that adjusts the allocation of study time according to the current learning situation. This enables effective learning support by customizing the support means based on the current learning situation. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input data on the current learning situation into a generation AI and have the generation AI customize the support means.

[0049] The support unit can improve the support method by reflecting the child's feedback during learning support. Support methods include, but are not limited to, teaching methods and methods of providing learning materials. For example, the support unit can adjust the support method based on the child's feedback to provide more effective learning support. The support unit can also analyze the child's feedback and identify and implement improvements to the support method. For example, the support unit can customize the support method by reflecting the child's feedback and strengthen individual support. In this way, the support method can be optimized by reflecting the feedback. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the child's feedback data into a generation AI and have the generation AI improve the support method.

[0050] When providing learning support, the support unit can select the optimal support method by taking into account the child's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, the support unit selects a support method that utilizes educational resources in the area where the child lives. The support unit can also select a support method that takes into account safety information about the child's route to school. For example, the support unit selects a support method that utilizes information about learning events and workshops in the child's area. In this way, the optimal support method can be selected by taking into account the geographical location information. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input geographical location information into the generation AI and cause the generation AI to select the optimal support method.

[0051] When providing learning support, the support unit can analyze the child's social media activity and suggest support methods. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the support unit can suggest support methods related to topics in which the child has shown interest on social media. The support unit can also suggest related learning support methods based on the activity of the child's friends on social media. For example, the support unit can analyze the content of the child's social media posts and suggest support methods that interest them. In this way, analysis of social media activity can efficiently suggest support methods relevant to the child. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input social media activity data into a generation AI and have the generation AI suggest support methods.

[0052] When providing learning support, the support unit can customize the support method by reflecting the child's past feedback. Past feedback includes, but is not limited to, survey results, comments, etc. For example, the support unit may preferentially use support methods that the child has previously preferred. The support unit may also avoid support methods that the child has previously expressed dissatisfaction with. For example, the support unit improves and optimizes the support method based on the child's past feedback. In this way, the support method can be optimized by reflecting past feedback. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit may input data of past feedback into a generation AI and have the generation AI customize the support method.

[0053] When proposing a junior high school, the suggestion unit can analyze the child's past learning achievements and select the optimal suggestion method. Past learning achievements include, but are not limited to, test scores, homework submission status, etc. For example, the suggestion unit can suggest a junior high school that is strong in the child's areas of strength based on the child's past learning achievements. The suggestion unit can also analyze the child's past learning achievements and suggest a junior high school that can strengthen the child's weak areas. For example, the suggestion unit can suggest a junior high school that the child will be most suited to based on the child's past learning achievements. In this way, the analysis of the child's past learning achievements can suggest the optimal junior high school for the child. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the suggestion unit can input data on the child's past learning achievements into the generation AI and have the generation AI select the optimal suggestion method.

[0054] When proposing a junior high school, the suggestion unit can analyze the child's current learning situation and interests and suggest the most suitable junior high school. Examples of the current learning situation and interests include, but are not limited to, the child's learning progress and subjects of interest. For example, the suggestion unit can suggest a junior high school that is strong in subjects in which the child is lagging behind based on the child's current learning situation. The suggestion unit can also analyze the child's interests and suggest a junior high school with an appealing curriculum. For example, the suggestion unit can analyze the child's current learning situation and suggest a junior high school where the child can learn most effectively. By analyzing the child's current learning situation and interests, the most suitable junior high school for the child can be suggested. Some or all of the above-described processing in the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input data on the child's current learning situation and interests into the generation AI and have the generation AI suggest the most suitable junior high school.

[0055] The suggestion unit can improve the suggestion method by reflecting the child's feedback when proposing a junior high school. Examples of feedback include, but are not limited to, survey results and comments. For example, the suggestion unit can adjust the suggestion method based on the child's feedback to make more effective suggestions for junior high schools. The suggestion unit can also analyze the child's feedback to identify and implement improvements to the suggestion method. For example, the suggestion unit can customize the suggestion method by reflecting the child's feedback and strengthen individual responses. This allows the suggestion method to be optimized by reflecting the feedback. Some or all of the above-described processing in the suggestion unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the suggestion unit can input the child's feedback data into the generation AI and have the generation AI improve the suggestion method.

[0056] When proposing a junior high school, the suggestion unit can suggest the most suitable junior high school by taking into consideration the child's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, the suggestion unit may preferentially suggest junior high schools in the area where the child lives. The suggestion unit may also suggest junior high schools by taking into consideration safety information about the child's route to school. For example, the suggestion unit may suggest junior high schools by taking into consideration learning events and workshop information in the child's area. In this way, the most suitable junior high school for the child can be suggested by taking into consideration the geographical location information. Some or all of the above-described processing in the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit may input geographical location information into the generation AI and cause the generation AI to suggest the most suitable junior high school.

[0057] When suggesting junior high schools, the suggestion unit can analyze the child's social media activity to suggest relevant junior high schools. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the suggestion unit can suggest junior high schools related to topics the child is interested in on social media. The suggestion unit can also suggest relevant junior high schools based on the activity of the child's friends on social media. For example, the suggestion unit can analyze the content of the child's social media posts to suggest junior high schools that interest the child. In this way, by analyzing social media activity, relevant junior high schools can be efficiently suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input social media activity data into the generation AI and cause the generation AI to suggest relevant junior high schools.

[0058] When making a proposal to a junior high school, the suggestion unit can customize the proposal method by reflecting the child's past feedback. Past feedback includes, but is not limited to, survey results, comments, etc. For example, the suggestion unit can prioritize the use of proposal methods that the child has previously preferred. The suggestion unit can also avoid proposal methods that the child has previously expressed dissatisfaction with. For example, the suggestion unit improves and optimizes the proposal method based on the child's past feedback. This allows the proposal method to be optimized by reflecting past feedback. Some or all of the above-described processing in the suggestion unit can be performed, for example, using a generation AI or without using a generation AI. For example, the suggestion unit can input past feedback data into the generation AI and have the generation AI customize the proposal method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The collection unit not only collects information to understand a child's learning style, but can also collect information on the child's health and lifestyle. For example, the collection unit records the child's sleep time, diet, exercise habits, etc., and analyzes the correlation with learning style. The collection unit can also monitor the child's stress level and mood fluctuations, and use this information to help adjust the learning plan. This makes it possible to provide learning support that takes into account the child's overall health.

[0061] The generator can take into account a child's future career goals and interests when generating a learning plan. For example, if a child wants to be a doctor in the future, the generator can generate a learning plan that focuses on science and biology. Alternatively, if a child wants to be an artist, the generator can provide a learning plan related to art and design. Furthermore, the generator can provide information on relevant junior high and high schools based on the child's career goals. This makes it possible to provide learning support that is tailored to the child's future goals.

[0062] The support department not only provides learning support based on the generated learning plan, but can also monitor a child's learning progress in real time and adjust the plan as needed. For example, if a child is struggling with a particular subject, the support department can provide additional learning materials and support for that subject. Also, if a child is progressing faster than expected, the support department can provide a new plan to help them move on to the next step. This allows for flexible support based on a child's learning situation.

[0063] When suggesting the optimal junior high school, the proposal unit can take into account not only the child's learning style and personality, but also the family's financial situation and commute time. For example, the proposal unit can suggest junior high schools with affordable tuition fees based on the family's financial situation. It can also prioritize junior high schools close to the child's home area to shorten commute times. This makes it possible to suggest realistic junior high schools that suit the family's situation.

[0064] When generating a lesson plan, the generator can combine different learning media according to the child's learning style. For example, it can provide video materials for visual learners and audio materials for auditory learners. It can also provide interactive simulations for tactile learners and text-based materials for reading learners. This allows the generator to provide an optimal lesson plan according to the child's learning style.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects information to understand the child's learning style. For example, the collection unit uses online tests and questionnaires to collect information about the child's learning style, personality, and interests. The collection unit can also analyze the child's past learning history and select the most appropriate information collection method. For example, the collection unit can prioritize collecting information about the child's best subjects from the child's past learning history. Step 2: The generator uses a generation AI to create an original study plan based on the information collected by the collection unit. The generator uses, for example, an algorithm to generate an optimal study plan based on past learning data and learning outcomes. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The support unit supports learning based on the learning plan created by the generation unit. The support unit can provide, for example, a learning schedule and learning materials based on the created learning plan. Step 4: The suggestion unit uses generative AI to analyze the child's personality, interests, and concerns, and proposes the most suitable junior high school. The suggestion unit provides a list of junior high schools, taking into consideration, for example, the school's curriculum, educational policy, and past admissions record.

[0067] (Example 2) A learning support system according to an embodiment of the present invention is a system that enables children to study independently in preparation for junior high school entrance exams and achieve their ideal career path. The learning support system collects information to understand a child's learning style, and a generation AI creates an original study plan, supports the child's learning, and suggests the most suitable junior high school. For example, the learning support system uses online tests and questionnaires to collect information about a child's learning style, personality, and interests. The generation AI then creates an original study plan based on the collected information. The generation AI uses an algorithm to generate an optimal study plan based on past learning data and learning outcomes. The generation AI then analyzes the child's personality, interests, and concerns to suggest the most suitable junior high school. The generation AI then provides a list of junior high schools, taking into account the school's curriculum, educational policy, past admissions records, and other factors. This allows the learning support system to enable children to study independently and achieve their ideal career path. This allows the learning support system to provide effective support for children to study independently and achieve their ideal career path. For example, children can study according to a plan that suits their learning style and obtain information to select the most suitable junior high school. Parents can also understand their children's learning progress and provide appropriate support.

[0068] A learning support system according to an embodiment includes a collection unit, a generation unit, a support unit, and a suggestion unit. The collection unit collects information to understand a child's learning style. The collection unit collects information about a child's learning style, personality, and interests, for example, using online tests and questionnaires. The collection unit can also analyze a child's past learning history and select an optimal information collection method. For example, the collection unit can prioritize collecting information about a child's favorite subjects from the child's past learning history. The generation unit uses a generation AI to create an original study plan based on the information collected by the collection unit. The generation unit uses, for example, an algorithm that generates an optimal study plan based on past learning data and learning outcomes. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. The support unit supports learning based on the study plan created by the generation unit. For example, the support unit may provide a study schedule and teaching materials based on the generated study plan. The suggestion unit uses the generation AI to analyze a child's personality, interests, and concerns and suggest an optimal junior high school. The suggestion unit provides a list of junior high schools, taking into account, for example, the school's curriculum, educational policy, past admissions record, etc. As a result, the learning support system according to the embodiment can provide effective support for children to study independently and realize their ideal career path. For example, children can proceed with their studies according to a plan that suits their learning style and obtain information for selecting the most suitable junior high school. Furthermore, parents can understand their children's learning situation and provide appropriate support.

[0069] The collection unit can collect information about a child's learning style, personality, and interests using online tests or questionnaires. Examples of online tests or questionnaires include, but are not limited to, academic achievement tests and personality assessment questionnaires. For example, the collection unit can conduct online tests to understand a child's learning style. The collection unit can also conduct questionnaires to collect information about a child's personality and interests. For example, the collection unit can conduct online tests when a child is relaxed to accurately understand a child's learning style. The collection unit can also conduct questionnaires when a child is concentrating to collect detailed information about the child's interests. In this way, by using online tests or questionnaires, a child's learning style, personality, and interests can be understood in detail. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the results of online tests or questionnaires into a generation AI and have the generation AI analyze the child's learning style, personality, and interests.

[0070] The generation unit can use an algorithm that generates an appropriate lesson plan based on past learning data and learning outcomes. Examples of past learning data and learning outcomes include, but are not limited to, test results and homework submission status. For example, the generation unit can generate a lesson plan that focuses on a child's weak areas based on past test results. The generation unit can also analyze homework submission status to generate a plan that allows a child to study effectively. For example, the generation unit can generate a lesson plan that focuses on a child's strong areas based on past learning outcomes. The generation unit can also analyze past learning outcomes to generate a lesson plan that reinforces a child's weak areas. This enables effective learning support by generating an optimal lesson plan based on past learning data and learning outcomes. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input past learning data and learning outcomes into the generation AI and cause the generation AI to generate an optimal lesson plan.

[0071] The support unit can support learning based on the generated learning plan. The generated learning plan includes, for example, a learning schedule, a method for providing learning materials, etc., but is not limited to these examples. For example, the support unit provides a learning schedule based on the generated learning plan. The support unit can also provide learning materials based on the generated learning plan. For example, the support unit supports a child to study efficiently based on the learning schedule. The support unit can also provide learning materials needed by the child based on the generated learning plan. In this way, by supporting learning based on the generated learning plan, the child's learning effectiveness can be improved. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the generated learning plan into the generation AI and cause the generation AI to execute the learning support method.

[0072] The suggestion unit can suggest an appropriate junior high school by taking into consideration the school's curriculum or educational policy, past admission records, etc. Examples of the school's curriculum or educational policy include, but are not limited to, the type of subject and educational philosophy. The suggestion unit can, for example, suggest the junior high school that is best suited to a child based on the school's curriculum. The suggestion unit can also suggest a junior high school that is best suited to a child by taking into consideration the educational policy. For example, the suggestion unit can suggest a junior high school that is likely to be accepted by a child based on past admission records. The suggestion unit can also analyze the school's curriculum and educational policy to suggest the junior high school that is best suited to a child. This allows the suggestion of the junior high school that is best suited to a child by taking into consideration the school's curriculum, educational policy, past admission records, etc. Some or all of the above-mentioned processing by the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input data on the school's curriculum, educational policy, and past admission records into the generation AI and have the generation AI suggest the optimal junior high school.

[0073] The suggestion unit can provide a list of suggested junior high schools. The list of junior high schools includes, for example, school names, locations, and characteristics, but is not limited to these examples. The suggestion unit, for example, provides a list of suggested junior high schools. The suggestion unit can also make it easier for parents and children to compare and consider options based on the list of junior high schools. For example, the suggestion unit provides a list of junior high schools and explains the characteristics of each school. The suggestion unit can also provide a list of junior high schools so that parents and children can compare and consider options. In this way, providing a list of suggested junior high schools makes it easier for parents and children to compare and consider options. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the list of junior high schools into a generation AI and cause the generation AI to generate the list.

[0074] The collection unit can estimate a child's emotions and adjust the timing of information collection based on the child's emotions. Examples of the child's emotions include, but are not limited to, relaxation, concentration, fatigue, etc. The collection unit can, for example, administer an online test when the child is relaxed to accurately grasp the child's learning style. The collection unit can also administer a questionnaire when the child is concentrating to collect detailed information about the child's interests. For example, the collection unit can temporarily suspend information collection when the child is tired and resume it after a break. This allows for more accurate information collection by adjusting the timing of information collection 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, for example, 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 collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the timing of information collection.

[0075] The collection unit can analyze the child's past learning history and select the optimal information collection method. The past learning history includes, for example, study time, learning content, grades, etc., but is not limited to these examples. For example, the collection unit prioritizes collecting information about subjects in which the child is good at from the past learning history. The collection unit can also collect detailed information about subjects in which the child is weak based on the past learning history. For example, the collection unit analyzes the past learning history and collects information during times when the child can study most effectively. In this way, by analyzing the past learning history, the optimal information collection method for the child can be selected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past learning history into a generation AI and cause the generation AI to select the optimal information collection method.

[0076] When collecting information, the collection unit can filter the information based on the child's current learning situation and areas of interest. Examples of the current learning situation and areas of interest include, but are not limited to, learning progress and subjects of interest. For example, the collection unit can prioritize collecting information about subjects in which progress is lagging based on the child's current learning situation. The collection unit can also collect interesting learning materials and resources based on the child's areas of interest. For example, the collection unit can analyze the current learning situation and filter and provide necessary supplemental information. This allows for efficient collection of necessary information by filtering based on the current learning situation and areas of interest. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the child's current learning situation and areas of interest into the generation AI and have the generation AI perform filtering.

[0077] When collecting information, the collection unit can select the optimal collection means depending on the child's input method. Examples of the child's input method include, but are not limited to, voice input, text input, and image input. For example, if the child prefers voice input, the collection unit provides a questionnaire that can be answered by voice. Furthermore, if the child prefers text input, the collection unit can also administer a text-based online test. For example, if the child prefers image input, the collection unit can perform an image-based learning style diagnosis. This improves the efficiency of information collection by selecting the optimal collection means depending on the child's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the child's input data into a generation AI and have the generation AI select the optimal collection means.

[0078] The collection unit can estimate the child's emotions and determine the priority of information to be collected based on the child's emotions. Examples of the priority of information to be collected include, but are not limited to, importance and urgency. For example, if the child is excited, the collection unit can prioritize collecting interesting information. Also, if the child is calm, the collection unit can prioritize collecting detailed educational information. For example, if the child is tired, the collection unit can prioritize collecting simple information. This enables effective information collection by determining the priority of information based on 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, for example, 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 collection unit may be performed using AI, or without AI. For example, the collection unit can input the child's emotion data into the generation AI and have the generation AI determine the priority of information.

[0079] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the child's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, the collection unit prioritizes collecting educational information in the area where the child lives. The collection unit can also prioritize collecting safety information related to the child's route to school. For example, the collection unit prioritizes collecting information about learning events and workshops in the child's area. This allows for efficient collection of highly relevant information by taking the geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0080] When collecting information, the collection unit can analyze the child's social media activities and collect related information. Social media activities include, but are not limited to, the content of posts, the number of likes, and the number of followers. For example, the collection unit can collect information on topics in which the child is interested on social media. The collection unit can also collect related learning information by referring to the activities of the child's friends on social media. For example, the collection unit can analyze the content of the child's social media posts and collect information that piques their interest. In this way, information related to the child can be efficiently collected by analyzing social media activities. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data on social media activities into the generation AI and cause the generation AI to collect related information.

[0081] When collecting information, the collection unit can customize the collection method by reflecting the child's past feedback. Past feedback includes, but is not limited to, survey results, comments, etc. For example, the collection unit preferentially uses a collection method that the child has previously preferred. The collection unit can also avoid a collection method that the child has previously expressed dissatisfaction with. For example, the collection unit improves and optimizes the collection method based on the child's past feedback. In this way, the collection method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data of past feedback into the generation AI and have the generation AI customize the collection method.

[0082] The generation unit can estimate the child's emotions and adjust the presentation of the lesson plan based on the child's emotions. Examples of presentation methods for the lesson plan include, but are not limited to, text format and graphical format. For example, if the child is relaxed, the generation unit generates a lesson plan that progresses at a leisurely pace. Furthermore, if the child is in a hurry, the generation unit can generate a plan that allows the child to study efficiently in a short amount of time. For example, if the child is excited, the generation unit generates a lesson plan that adds visually stimulating effects. This allows the presentation of the lesson plan to be adjusted based on the child's emotions, thereby providing a lesson plan that is suitable for the child. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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, for example, 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 presentation of the lesson plan.

[0083] When generating a study plan, the generation unit can adjust the level of detail of the plan based on the importance of the study content. Examples of the importance of the study content include, but are not limited to, test scope and study goals. For example, the generation unit generates a plan that includes detailed explanations and practice questions for important study content. The generation unit can also generate a plan that includes concise explanations and basic practice questions for less important study content. For example, the generation unit generates a plan that adjusts the allocation of study time according to the importance of the study content. This allows for an effective study plan to be provided by adjusting the level of detail of the plan based on the importance of the study content. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input data on the importance of the study content into the generation AI and have the generation AI adjust the level of detail of the plan.

[0084] When generating a study plan, the generation unit can apply different generation algorithms depending on the study subject. Examples of different generation algorithms depending on the study subject include, but are not limited to, a mathematics algorithm and a language algorithm. For example, the generation unit can apply a problem-solving algorithm to a mathematics study plan. The generation unit can also apply a vocabulary enhancement algorithm to an English study plan. For example, the generation unit can apply an experiment simulation algorithm to a science study plan. By applying different generation algorithms depending on the study subject, it is possible to provide an optimal study plan for each subject. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input data on the study subject into the generation AI and cause the generation AI to apply different generation algorithms.

[0085] When generating a learning plan, the generation unit can improve the accuracy of the plan by referring to the child's past learning results. Past learning results include, but are not limited to, test scores and homework submission status. For example, the generation unit can generate a plan that focuses on areas in which the child is weak based on the past learning results. The generation unit can also analyze past learning results and generate a plan that incorporates effective learning methods. For example, the generation unit can generate a plan based on the child's learning progress by referring to the past learning results. In this way, the accuracy of the plan can be improved by referring to the past learning results. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input data on past learning results into the generation AI and have the generation AI improve the accuracy of the plan.

[0086] The generation unit can estimate the child's emotions and adjust the length of the lesson plan based on the child's emotions. Examples of the length of the lesson plan include, but are not limited to, the study time and the amount of learning content. For example, if the child is relaxed, the generation unit generates a longer lesson plan. Also, if the child is in a hurry, the generation unit can generate a lesson plan that can be completed in a short time. For example, if the child is excited, the generation unit generates a lesson plan of an appropriate length that allows the child to maintain concentration. This allows the length of the lesson plan to be adjusted based on the child's emotions, thereby providing a lesson plan that is suitable for the child. The emotion estimation is achieved using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI can be, for example, 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 can be performed using, for example, 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 lesson plan.

[0087] When generating a study plan, the generation unit can determine the priority of the plan based on the student's learning progress. The learning progress includes, but is not limited to, the level of achievement and the number of uncompleted tasks. For example, the generation unit generates a plan that prioritizes learning of subjects for which the student's learning progress is lagging behind. The generation unit can also generate a plan that focuses on review for subjects for which the student's learning progress is steady. For example, the generation unit generates a plan that adjusts the allocation of study time according to the student's learning progress. This enables effective study support by determining the priority of plans based on the student's learning progress. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input data on the student's learning progress into the generation AI and have the generation AI determine the priority of the plans.

[0088] When generating a study plan, the generation unit can adjust the order of the plan based on the relevance of the study content. Examples of the relevance of the study content include, but are not limited to, the presence or absence of prerequisite knowledge and the relevance between subjects. For example, the generation unit generates a plan in which highly relevant study content is studied consecutively. The generation unit can also generate a plan in which less relevant study content is studied at intervals. For example, the generation unit generates a plan that determines an efficient study order based on the relevance of the study content. This enables efficient study by adjusting the order of the plan based on the relevance of the study content. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input relevance data of the study content into the generation AI and have the generation AI adjust the order of the plan.

[0089] When generating a learning plan, the generation unit can adjust the content of the plan according to the child's grade and academic level. Examples of the child's grade and academic level include, but are not limited to, grade-specific curricula and standardized test results. The generation unit, for example, generates a learning plan with a difficulty level according to the child's grade. The generation unit can also generate learning plans ranging from basic to advanced according to the child's academic level. For example, the generation unit generates a plan including appropriate teaching materials and resources based on the child's grade and academic level. This allows the content of the plan to be adjusted according to the child's grade and academic level, thereby providing an appropriate learning plan. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the child's grade and academic level into the generation AI and have the generation AI adjust the content of the plan.

[0090] The support unit can estimate a child's emotions and adjust the learning support method based on the child's emotions. Examples of learning support methods include, but are not limited to, individual instruction, group learning, online support, etc. For example, if a child is relaxed, the support unit can provide learning support at a leisurely pace. Furthermore, if a child is in a hurry, the support unit can provide learning support efficiently in a short amount of time. For example, if a child is excited, the support unit can provide learning support with visually stimulating effects. This enables effective learning support by adjusting the learning support method based on 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, for example, 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 support unit may be performed using AI, for example, or without AI. For example, the support unit can input the child's emotion data into the generation AI and have the generation AI adjust the learning support method.

[0091] When providing learning support, the support unit can analyze the child's past learning behavior and select the optimal support method. Past learning behavior includes, but is not limited to, for example, study time, learning content, and learning outcomes. For example, the support unit selects a support method that will enable the child to study effectively based on the child's past learning behavior. The support unit can also analyze the child's past learning behavior and select a support method that focuses on areas in which the child is weak. For example, the support unit selects a support method that matches the child's preferred learning style with reference to the child's past learning behavior. In this way, the analysis of the child's past learning behavior can select the optimal support method for the child. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input data on the child's past learning behavior into the generation AI and have the generation AI select the optimal support method.

[0092] When providing learning support, the support unit can customize the support means based on the child's current learning situation. Examples of the current learning situation include, but are not limited to, progress and level of understanding. For example, the support unit can provide support means that focus on subjects where progress is lagging based on the current learning situation. The support unit can also analyze the current learning situation and customize the support means to provide necessary supplemental information. For example, the support unit can provide support means that adjusts the allocation of study time according to the current learning situation. This enables effective learning support by customizing the support means based on the current learning situation. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input data on the current learning situation into a generation AI and have the generation AI customize the support means.

[0093] The support unit can improve the support method by reflecting the child's feedback during learning support. Support methods include, but are not limited to, teaching methods and methods of providing learning materials. For example, the support unit can adjust the support method based on the child's feedback to provide more effective learning support. The support unit can also analyze the child's feedback and identify and implement improvements to the support method. For example, the support unit can customize the support method by reflecting the child's feedback and strengthen individual support. In this way, the support method can be optimized by reflecting the feedback. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the child's feedback data into a generation AI and have the generation AI improve the support method.

[0094] The support unit can estimate a child's emotions and determine the priority of learning support based on the child's emotions. Priorities for learning support include, but are not limited to, urgency and importance. For example, if a child is excited, the support unit can prioritize support for learning content that is of interest to the child. Furthermore, if a child is calm, the support unit can prioritize support for detailed learning content. For example, if a child is tired, the support unit can prioritize support for simple learning content. This enables effective learning support by determining the priority of learning support based on 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, for example, 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 support unit may be performed using, for example, AI, or without AI. For example, the support unit can input the child's emotion data into the generation AI and have the generation AI determine the priority of learning support.

[0095] When providing learning support, the support unit can select the optimal support method by taking into account the child's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, the support unit selects a support method that utilizes educational resources in the area where the child lives. The support unit can also select a support method that takes into account safety information about the child's route to school. For example, the support unit selects a support method that utilizes information about learning events and workshops in the child's area. In this way, the optimal support method can be selected by taking into account the geographical location information. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input geographical location information into the generation AI and cause the generation AI to select the optimal support method.

[0096] When providing learning support, the support unit can analyze the child's social media activity and suggest support methods. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the support unit can suggest support methods related to topics in which the child has shown interest on social media. The support unit can also suggest related learning support methods based on the activity of the child's friends on social media. For example, the support unit can analyze the content of the child's social media posts and suggest support methods that interest them. In this way, analysis of social media activity can efficiently suggest support methods relevant to the child. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input social media activity data into a generation AI and have the generation AI suggest support methods.

[0097] When providing learning support, the support unit can customize the support method by reflecting the child's past feedback. Past feedback includes, but is not limited to, survey results, comments, etc. For example, the support unit may preferentially use support methods that the child has previously preferred. The support unit may also avoid support methods that the child has previously expressed dissatisfaction with. For example, the support unit improves and optimizes the support method based on the child's past feedback. In this way, the support method can be optimized by reflecting past feedback. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit may input data of past feedback into a generation AI and have the generation AI customize the support method.

[0098] The suggestion unit can estimate the child's emotions and adjust the junior high school suggestion method based on the child's emotions. Examples of the junior high school suggestion method include, but are not limited to, the timing of the suggestion and the format of the suggestion content. For example, if the child is relaxed, the suggestion unit can make a junior high school suggestion with detailed information. Furthermore, if the child is in a hurry, the suggestion unit can make a concise junior high school suggestion that focuses on the main points. For example, if the child is excited, the suggestion unit can make a junior high school suggestion with a visually stimulating effect. This enables effective suggestions by adjusting the junior high school suggestion method based on the child's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 suggestion unit can be performed using, for example, the generation AI. For example, the suggestion unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the junior high school suggestion method.

[0099] When proposing a junior high school, the suggestion unit can analyze the child's past learning achievements and select the optimal suggestion method. Past learning achievements include, but are not limited to, test scores, homework submission status, etc. For example, the suggestion unit can suggest a junior high school that is strong in the child's areas of strength based on the child's past learning achievements. The suggestion unit can also analyze the child's past learning achievements and suggest a junior high school that can strengthen the child's weak areas. For example, the suggestion unit can suggest a junior high school that the child will be most suited to based on the child's past learning achievements. In this way, the analysis of the child's past learning achievements can suggest the optimal junior high school for the child. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the suggestion unit can input data on the child's past learning achievements into the generation AI and have the generation AI select the optimal suggestion method.

[0100] When proposing a junior high school, the suggestion unit can analyze the child's current learning situation and interests and suggest the most suitable junior high school. Examples of the current learning situation and interests include, but are not limited to, the child's learning progress and subjects of interest. For example, the suggestion unit can suggest a junior high school that is strong in subjects in which the child is lagging behind based on the child's current learning situation. The suggestion unit can also analyze the child's interests and suggest a junior high school with an appealing curriculum. For example, the suggestion unit can analyze the child's current learning situation and suggest a junior high school where the child can learn most effectively. By analyzing the child's current learning situation and interests, the most suitable junior high school for the child can be suggested. Some or all of the above-described processing in the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input data on the child's current learning situation and interests into the generation AI and have the generation AI suggest the most suitable junior high school.

[0101] The suggestion unit can improve the suggestion method by reflecting the child's feedback when proposing a junior high school. Examples of feedback include, but are not limited to, survey results and comments. For example, the suggestion unit can adjust the suggestion method based on the child's feedback to make more effective suggestions for junior high schools. The suggestion unit can also analyze the child's feedback to identify and implement improvements to the suggestion method. For example, the suggestion unit can customize the suggestion method by reflecting the child's feedback and strengthen individual responses. This allows the suggestion method to be optimized by reflecting the feedback. Some or all of the above-described processing in the suggestion unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the suggestion unit can input the child's feedback data into the generation AI and have the generation AI improve the suggestion method.

[0102] The suggestion unit can estimate the child's emotions and prioritize the junior high school suggestions based on the child's emotions. Examples of the prioritization of junior high school suggestions include, but are not limited to, urgency and importance. For example, if the child is excited, the suggestion unit can prioritize suggesting junior high schools that interest the child. Furthermore, if the child is calm, the suggestion unit can prioritize suggesting junior high schools that include detailed information. For example, if the child is tired, the suggestion unit can prioritize suggesting junior high schools that include simple information. This enables effective suggestions by prioritizing suggestions 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, for example, 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 suggestion unit can be performed using, for example, the generation AI. For example, the suggestion unit can input the child's emotion data into the generation AI and have the generation AI determine the priority of the junior high school suggestions.

[0103] When proposing a junior high school, the suggestion unit can suggest the most suitable junior high school by taking into consideration the child's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, the suggestion unit may preferentially suggest junior high schools in the area where the child lives. The suggestion unit may also suggest junior high schools by taking into consideration safety information about the child's route to school. For example, the suggestion unit may suggest junior high schools by taking into consideration learning events and workshop information in the child's area. In this way, the most suitable junior high school for the child can be suggested by taking into consideration the geographical location information. Some or all of the above-described processing in the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit may input geographical location information into the generation AI and cause the generation AI to suggest the most suitable junior high school.

[0104] When suggesting junior high schools, the suggestion unit can analyze the child's social media activity to suggest relevant junior high schools. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the suggestion unit can suggest junior high schools related to topics the child is interested in on social media. The suggestion unit can also suggest relevant junior high schools based on the activity of the child's friends on social media. For example, the suggestion unit can analyze the content of the child's social media posts to suggest junior high schools that interest the child. In this way, by analyzing social media activity, relevant junior high schools can be efficiently suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input social media activity data into the generation AI and cause the generation AI to suggest relevant junior high schools.

[0105] When making a proposal to a junior high school, the suggestion unit can customize the proposal method by reflecting the child's past feedback. Past feedback includes, but is not limited to, survey results, comments, etc. For example, the suggestion unit can prioritize the use of proposal methods that the child has previously preferred. The suggestion unit can also avoid proposal methods that the child has previously expressed dissatisfaction with. For example, the suggestion unit improves and optimizes the proposal method based on the child's past feedback. This allows the proposal method to be optimized by reflecting past feedback. Some or all of the above-described processing in the suggestion unit can be performed, for example, using a generation AI or without using a generation AI. For example, the suggestion unit can input past feedback data into the generation AI and have the generation AI customize the proposal method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, generation unit, support unit, and suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information about a child's learning style, personality, and interests using the camera 42 and microphone 38B of the smart device 14, and processes the information using the control unit 46A. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, creates an original study plan based on the collected information. The support unit, realized, for example, by the control unit 46A of the smart device 14, provides a study schedule and teaching materials based on the generated study plan. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes a child's personality, interests, and concerns, and suggests the most suitable junior high school. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, generation unit, support unit, and suggestion 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 collection unit collects information about a child's learning style, personality, and interests using the camera 42 and microphone 238 of the smart glasses 214, and processes the information using the control unit 46A. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, creates an original study plan based on the collected information. The support unit, realized, for example, by the control unit 46A of the smart glasses 214, provides a study schedule and teaching materials based on the generated study plan. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes a child's personality, interests, and concerns, and suggests the most suitable junior high school. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, generation unit, support unit, and suggestion 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 collection unit collects information about a child's learning style, personality, and interests using the camera 42 and microphone 238 of the headset-type terminal 314, and processes the information using the control unit 46A. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, creates an original study plan based on the collected information. The support unit, realized, for example, by the control unit 46A of the headset-type terminal 314, provides a study schedule and teaching materials based on the generated study plan. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes a child's personality, interests, and concerns, and suggests the most suitable junior high school. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, generation unit, support unit, and suggestion unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to collect information about the child's learning style, personality, and interests, and processes the information using the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates an original study plan based on the collected information. The support unit is realized, for example, by the control unit 46A of the robot 414 and provides a study schedule and teaching materials based on the generated study plan. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the child's personality, interests, and concerns, and suggests the most suitable junior high school.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The collection unit not only collects information to understand a child's learning style, but can also collect information on the child's health and lifestyle. For example, the collection unit records the child's sleep time, diet, exercise habits, etc., and analyzes the correlation with learning style. The collection unit can also monitor the child's stress level and mood fluctuations, and use this information to help adjust the learning plan. This makes it possible to provide learning support that takes into account the child's overall health.

[0108] The generator can take into account a child's future career goals and interests when generating a learning plan. For example, if a child wants to be a doctor in the future, the generator can generate a learning plan that focuses on science and biology. Alternatively, if a child wants to be an artist, the generator can provide a learning plan related to art and design. Furthermore, the generator can provide information on relevant junior high and high schools based on the child's career goals. This makes it possible to provide learning support that is tailored to the child's future goals.

[0109] The support department not only provides learning support based on the generated learning plan, but can also monitor a child's learning progress in real time and adjust the plan as needed. For example, if a child is struggling with a particular subject, the support department can provide additional learning materials and support for that subject. Also, if a child is progressing faster than expected, the support department can provide a new plan to help them move on to the next step. This allows for flexible support based on a child's learning situation.

[0110] When suggesting the optimal junior high school, the proposal unit can take into account not only the child's learning style and personality, but also the family's financial situation and commute time. For example, the proposal unit can suggest junior high schools with affordable tuition fees based on the family's financial situation. It can also prioritize junior high schools close to the child's home area to shorten commute times. This makes it possible to suggest realistic junior high schools that suit the family's situation.

[0111] The collection unit can estimate the child's emotions and adjust the information collection method based on the child's emotions. For example, when the child is relaxed, a detailed questionnaire can be conducted, and when the child is tired, simple questions can be asked. Also, when the child is excited, a game-style test can be conducted to collect information while having fun. This makes it possible to collect information flexibly according to the child's emotions.

[0112] The generation unit can estimate the child's emotions and adjust the difficulty of the lesson plan based on the child's emotions. For example, if the child is relaxed, it can provide a more difficult task, and if the child is tired, it can provide an easier task. Also, if the child is excited, it can provide a more challenging task to increase motivation. This makes it possible to provide an appropriate lesson plan according to the child's emotions.

[0113] The support unit can estimate a child's emotions and adjust the timing of learning support based on the child's emotions. For example, when a child is relaxed, it can help them concentrate on their studies, and when a child is tired, it can encourage them to take a break. Also, when a child is excited, it can provide fun learning activities to increase their motivation to learn. This makes it possible to provide effective learning support that is tailored to the child's emotions.

[0114] The suggestion unit can estimate the child's emotions and adjust the content of junior high school suggestions based on the child's emotions. For example, if the child is relaxed, detailed information can be provided, and if the child is in a hurry, concise information that focuses on the main points can be provided. Also, if the child is excited, a visually appealing presentation can be made to attract the child's interest. This makes it possible to make effective junior high school suggestions based on the child's emotions.

[0115] The collection unit can estimate the child's emotions and determine the priority of information to be collected based on the child's emotions. For example, if the child is excited, it can prioritize collecting interesting information, and if the child is calm, it can prioritize collecting detailed educational information. Also, if the child is tired, it can prioritize collecting simple information. In this way, by determining the priority of information based on the child's emotions, it is possible to collect information effectively.

[0116] When generating a lesson plan, the generator can combine different learning media according to the child's learning style. For example, it can provide video materials for visual learners and audio materials for auditory learners. It can also provide interactive simulations for tactile learners and text-based materials for reading learners. This allows the generator to provide an optimal lesson plan according to the child's learning style.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection unit collects information to understand the child's learning style. For example, the collection unit uses online tests and questionnaires to collect information about the child's learning style, personality, and interests. The collection unit can also analyze the child's past learning history and select the most appropriate information collection method. For example, the collection unit can prioritize collecting information about the child's best subjects from the child's past learning history. Step 2: The generator uses a generation AI to create an original study plan based on the information collected by the collection unit. The generator uses, for example, an algorithm to generate an optimal study plan based on past learning data and learning outcomes. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The support unit supports learning based on the learning plan created by the generation unit. The support unit can provide, for example, a learning schedule and learning materials based on the created learning plan. Step 4: The suggestion unit uses generative AI to analyze the child's personality, interests, and concerns, and proposes the most suitable junior high school. The suggestion unit provides a list of junior high schools, taking into consideration, for example, the school's curriculum, educational policy, and past admissions record.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 collection department that collects information to understand children's learning styles; a generation unit that creates an original learning plan based on the information collected by the collection unit; a support unit that supports learning based on the learning plan created by the generation unit; A proposal department that analyzes children's personalities, interests, and concerns and suggests appropriate junior high schools. A system characterized by:

2. The collecting unit Use online tests or surveys to gather information about your child's learning style, personality, and interests 2. The system of claim 1.

3. The generation unit Use an algorithm to generate an appropriate learning plan based on past learning data and learning outcomes 2. The system of claim 1.

4. The support unit Support learning based on generated learning plans 2. The system of claim 1.

5. The proposal unit We will propose suitable junior high schools based on the school's curriculum, educational policy, and past admissions records.

2. The system of claim 1.

6. The proposal unit Provide a list of suggested secondary schools 2. The system of claim 1.

7. The collecting unit Inferring the child's emotions and adjusting the timing of information gathering based on the child's emotions 2. The system of claim 1.

8. The collecting unit Analyze the child's past learning history and select the most appropriate method of collecting information 2. The system of claim 1.

9. The collecting unit When collecting information, filter it based on the child's current learning status and areas of interest.

2. The system of claim 1.

10. The collecting unit When collecting information, choose the most appropriate collection method depending on the child's input method.

2. The system of claim 1.

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