system

Abstract

The system according to the embodiment aims to provide individual learning support based on the level of understanding and strengths and weaknesses of each learner. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a planning unit, a question generation unit, and a provision unit. The collection unit collects learning data. The analysis unit analyzes the data collected by the collection unit and identifies each individual's level of understanding or strengths and weaknesses. The planning unit creates an individual learning plan based on the analysis results obtained by the analysis unit. The question generation unit automatically generates questions based on the learning plan created by the planning unit. The provision unit provides the questions and learning plan created by the question generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide individual learning support based on each learner's level of understanding and areas of strength and weakness, and there is room for improvement.

[0005] The system according to the embodiment aims to provide individual learning support based on the level of understanding and strengths and weaknesses of each learner. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a planning unit, a question generation unit, and a provision unit. The collection unit collects learning data. The analysis unit analyzes the data collected by the collection unit and identifies each individual's understanding level or strengths and weaknesses. The planning unit creates an individual learning plan based on the analysis results obtained by the analysis unit. The question generation unit automatically generates questions based on the learning plan created by the planning unit. The provision unit provides the questions and learning plan created by the question generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide individual learning support based on the understanding level and strengths and weaknesses of each learner. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A learning support system according to an embodiment of the present invention uses a cloud platform to collect data on children's learning, which is then analyzed by AI. This system supports learning by setting learning goals in advance, using AI to create individual learning plans, and automatically generating questions. This tool can improve the work efficiency of teachers who are understaffed or overworked. For example, the learning support system collects data on children's learning, such as learning progress, test results, and answer trends. This data is securely stored using a cloud platform. AI then analyzes the collected data. AI identifies each individual's level of understanding and strengths and weaknesses. For example, it evaluates the child's level of understanding of a specific subject or question format and determines which areas the child is strong and weak in. Furthermore, learning goals are set. Teachers or parents set learning goals for their children, and AI creates individual learning plans based on those goals. For example, if the child's goal is to understand a specific unit in one week, AI creates a learning plan to achieve that goal. AI then automatically generates questions based on the individual learning plan. For example, for areas where students have low comprehension, more practice questions are provided, and for areas where students have a high level of comprehension, application questions are provided. This allows children to study effectively at their own pace. Finally, this tool will improve teacher work efficiency. For example, by having AI create learning plans, teachers can reduce the time they spend on individual instruction and focus on other educational activities. Furthermore, by having AI automatically generate questions, teachers are relieved of the burden of creating questions. In this way, by using a cloud platform to collect data on children's studies and having AI analyze it, individual learning support can be realized and teachers' work can be improved. As a result, the learning support system collects children's learning data and has AI analyze it, allowing individual learning support to be realized and teachers' work to be improved.

[0029] A learning assistance system according to an embodiment includes a collection unit, an analysis unit, a planning unit, a question generation unit, and a provision unit. The collection unit collects learning data. The learning data includes, for example, learning progress, test results, and answer trends, but is not limited to these examples. For example, the collection unit collects data such as study time and achievement level to record learning progress. The collection unit can also record test results and collect data such as scores and correct answer rates. The collection unit can also record answer trends and collect data such as correct answer patterns and incorrect answer patterns. For example, the collection unit measures study time and evaluates achievement level to record learning progress. The collection unit can also record test results and evaluate scores and correct answer rates. The collection unit can also record answer trends and evaluate correct answer patterns and incorrect answer patterns. The analysis unit analyzes the data collected by the collection unit to identify each individual's understanding level and strengths and weaknesses. The analysis is performed using, for example, a machine learning algorithm, but is not limited to these examples. For example, the analysis unit uses a machine learning algorithm to analyze the collected data and evaluate the understanding level of each individual. The analysis unit can also use a machine learning algorithm to analyze the collected data and identify strengths and weaknesses. The analysis unit can also use a machine learning algorithm to analyze the collected data and evaluate the level of understanding. For example, the analysis unit uses a machine learning algorithm to analyze the collected data and evaluate the understanding level of each individual. The analysis unit can also use a machine learning algorithm to analyze the collected data and identify strengths and weaknesses. The analysis unit can also use a machine learning algorithm to analyze the collected data and evaluate the level of understanding. The planning unit creates an individual learning plan based on the analysis results obtained by the analysis unit. The learning plan can be created based on, for example, learning goals set by a teacher or a parent, but is not limited to such examples. For example, the planning unit creates an individual learning plan based on the learning goals set by a teacher or a parent. The planning unit can also create a learning schedule based on the learning goals. The planning unit can also select learning materials based on the learning goals.For example, the planning unit creates an individual learning plan based on learning goals set by a teacher or a parent. The planning unit can also create a learning schedule based on the learning goals. The planning unit can also select learning materials based on the learning goals. The question generation unit automatically generates questions based on the learning plan created by the planning unit. For example, the questions may provide more practice questions for areas with low comprehension and more application questions for areas with high comprehension, but are not limited to such examples. For example, the question generation unit provides more practice questions for areas with low comprehension. The question generation unit can also provide application questions for areas with high comprehension. The question generation unit can also provide questions of a moderate level of difficulty for areas with medium comprehension. For example, the question generation unit provides more practice questions for areas with low comprehension. The question generation unit can also provide application questions for areas with high comprehension. The question generation unit can also provide questions of a moderate level of difficulty for areas with medium comprehension. The providing unit provides the questions and learning plan generated by the question generation unit. The provision may be performed, for example, through a web application or a mobile application, but is not limited to such examples. For example, the providing unit provides the generated questions and study plans through a web application. The providing unit can also provide the generated questions and study plans through a mobile application. Furthermore, the providing unit can provide the generated questions and study plans by email. For example, the providing unit provides the generated questions and study plans through a web application. The providing unit can also provide the generated questions and study plans through a mobile application. Furthermore, the providing unit can provide the generated questions and study plans by email. As a result, the learning support system according to the embodiment collects children's learning data and has AI analyze it, thereby realizing individual learning support and improving the work efficiency of teachers.

[0030] The collection unit can collect data such as learning progress, test results, and answer trends. For example, the collection unit collects data such as study time and achievement level to record learning progress. For example, the collection unit measures study time and evaluates achievement level to record learning progress. The collection unit can also record test results and collect scores and correct answer rates as data. For example, the collection unit can record test results and evaluate scores and correct answer rates. Furthermore, the collection unit can record answer trends and collect correct answer patterns and incorrect answer patterns as data. For example, the collection unit can record answer trends and evaluate correct answer patterns and incorrect answer patterns. In this way, detailed learning data can be obtained by collecting data such as learning progress, test results, and answer trends. 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 collect data using an AI model that measures study time and evaluates achievement level to record learning progress.

[0031] The analysis unit can analyze the collected data using a machine learning algorithm to identify each individual's understanding level and strengths and weaknesses. The analysis unit can, for example, use a machine learning algorithm to analyze the collected data and evaluate each individual's understanding level. For example, the analysis unit can analyze the collected data using a machine learning algorithm to evaluate each individual's understanding level. The analysis unit can also analyze the collected data using a machine learning algorithm to identify each individual's strengths and weaknesses. For example, the analysis unit can analyze the collected data using a machine learning algorithm to identify each individual's strengths and weaknesses. The analysis unit can also analyze the collected data using a machine learning algorithm to evaluate each individual's understanding level. The analysis unit can also analyze the collected data using a machine learning algorithm to evaluate each individual's understanding level. In this way, the use of a machine learning algorithm can accurately identify each individual's understanding level and strengths and weaknesses. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the collected data using a machine learning algorithm to analyze the data using an AI model that identifies each individual's understanding level and strengths and weaknesses.

[0032] The planning unit can create an individual learning plan based on learning goals set by a teacher or a parent. The planning unit, for example, creates an individual learning plan based on learning goals set by a teacher or a parent. For example, the planning unit creates an individual learning plan based on learning goals set by a teacher or a parent. The planning unit can also create a learning schedule based on the learning goals. For example, the planning unit can create a learning schedule based on the learning goals. Furthermore, the planning unit can select learning materials based on the learning goals. For example, the planning unit can select learning materials based on the learning goals. In this way, individual learning support is realized by creating an individual learning plan based on the learning goals set by a teacher or a parent. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can create a learning plan using an AI model that creates an individual learning plan based on learning goals set by a teacher or a parent.

[0033] The question generation unit can provide more practice questions for areas with low levels of understanding and provide application questions for areas with high levels of understanding. The question generation unit, for example, provides more practice questions for areas with low levels of understanding. For example, the question generation unit provides more practice questions for areas with low levels of understanding. The question generation unit can also provide application questions for areas with high levels of understanding. For example, the question generation unit can also provide application questions for areas with high levels of understanding. Furthermore, the question generation unit can provide questions of a moderate level of difficulty for areas with medium levels of understanding. For example, the question generation unit can also provide questions of a moderate level of difficulty for areas with medium levels of understanding. This enables effective learning by providing questions according to the level of understanding. Some or all of the above-mentioned processing in the question generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the question generation unit can generate questions using an AI model that provides more practice questions for areas with low levels of understanding.

[0034] The providing unit can provide the generated questions and study plans to the user. The providing unit, for example, provides the generated questions and study plans through a web application. For example, the providing unit can provide the generated questions and study plans through a web application. The providing unit can also provide the generated questions and study plans through a mobile application. For example, the providing unit can also provide the generated questions and study plans through a mobile application. Furthermore, the providing unit can provide the generated questions and study plans by email. For example, the providing unit can also provide the generated questions and study plans by email. In this way, by providing the generated questions and study plans to the user, progress in learning is supported. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that provides the generated questions and study plans.

[0035] When collecting learning data, the collection unit can analyze the user's past learning history and select the optimal collection method. The collection unit, for example, collects detailed data on subjects in which the user has achieved high grades in the past. For example, the collection unit collects detailed data on subjects in which the user has achieved high grades in the past. The collection unit can also focus on collecting data on subjects in which the user has struggled in the past. For example, the collection unit can also focus on collecting data on subjects in which the user has struggled in the past. Furthermore, the collection unit can identify the most effective learning method from the user's past learning history and collect data based on that method. For example, the collection unit can identify the most effective learning method from the user's past learning history and collect data based on that method. In this way, the optimal collection method can be selected by analyzing the user's past learning history. 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 collect data using an AI model that analyzes the user's past learning history and selects the optimal collection method.

[0036] When collecting learning data, the collection unit can filter the learning data based on the user's current learning environment and areas of interest. For example, if the user is studying in a quiet environment, the collection unit collects data appropriate for that environment. For example, if the user is studying in a quiet environment, the collection unit collects data appropriate for that environment. Furthermore, if the user is interested in a particular field, the collection unit can prioritize collecting data related to that field. For example, if the user is interested in a particular field, the collection unit can prioritize collecting data related to that field. Furthermore, if the user is studying in a group, the collection unit can collect data for the entire group and compare it with individual data. For example, if the user is studying in a group, the collection unit can collect data for the entire group and compare it with individual data. In this way, by filtering data based on the user's current learning environment and areas of interest, more relevant data can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model that filters data based on the user's current learning environment and areas of interest.

[0037] When collecting learning data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user lives in a specific area, the collection unit prioritizes collecting data related to that area. For example, if the user lives in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, if the user is traveling, the collection unit can prioritize collecting data related to the travel destination. For example, if the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, if the user is at school, the collection unit can prioritize collecting data related to the school curriculum. For example, if the user is at school, the collection unit can prioritize collecting data related to the school curriculum. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can collect data using an AI model that prioritizes collecting highly relevant data by taking into account the user's geographical location information.

[0038] The collection unit can analyze the user's social media activities and collect related data when collecting learning data. The collection unit can collect related data, for example, based on learning content shared by the user on social media. For example, the collection unit can collect related data based on learning content shared by the user on social media. The collection unit can also collect information on education-related accounts followed by the user on social media. For example, the collection unit can collect information on education-related accounts followed by the user on social media. The collection unit can also collect activity content of learning groups in which the user participates on social media. For example, the collection unit can collect activity content of learning groups in which the user participates on social media. In this way, related data can be collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit can be performed, for example, using AI, or can be performed without using AI. For example, the collection unit can collect data using an AI model that analyzes the user's social media activities and collects related data.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the training data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a brief analysis on data with low importance. For example, the analysis unit can also perform a brief analysis on data with low importance. Furthermore, the analysis unit can perform an analysis with a moderate level of detail on data with medium importance. For example, the analysis unit can also perform an analysis with a moderate level of detail on data with medium importance. This enables effective analysis by adjusting the level of detail of the analysis based on the importance of the training data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze data using an AI model that adjusts the level of detail of the analysis based on the importance of the training data.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the training data. For example, the analysis unit applies a numerical analysis algorithm to mathematical data. For example, the analysis unit applies a numerical analysis algorithm to mathematical data. The analysis unit can also apply a natural language processing algorithm to linguistic data. For example, the analysis unit can apply a natural language processing algorithm to linguistic data. Furthermore, the analysis unit can apply an experimental data analysis algorithm to scientific data. For example, the analysis unit can apply an experimental data analysis algorithm to scientific data. This enables more appropriate analysis by applying different analysis algorithms depending on the category of the training data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze data using an AI model that applies different analysis algorithms depending on the category of the training data.

[0041] During analysis, the analysis unit can determine the priority of the analysis based on the time when the learning data was collected. For example, the analysis unit prioritizes the analysis of recently collected data. For example, the analysis unit prioritizes the analysis of recently collected data. The analysis unit can also analyze current data while referring to data collected in the past. For example, the analysis unit can analyze current data while referring to data collected in the past. Furthermore, the analysis unit can focus on analyzing data collected during a specific period. For example, the analysis unit can focus on analyzing data collected during a specific period. This enables effective analysis by determining the priority of the analysis based on the time when the learning data was collected. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze data using an AI model that determines the priority of the analysis based on the time when the learning data was collected.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the training data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of low-relevance data. For example, the analysis unit can also postpone analysis of low-relevance data. Furthermore, the analysis unit can moderately analyze data with medium relevance. For example, the analysis unit can moderately analyze data with medium relevance. This enables effective analysis by adjusting the order of analysis based on the relevance of the training data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze data using an AI model that adjusts the order of analysis based on the relevance of the training data.

[0043] When creating a learning plan, the planning unit can adjust the level of detail of the plan based on the importance of the learning objective. For example, the planning unit creates a detailed plan for a learning objective with high importance. For example, the planning unit creates a detailed plan for a learning objective with high importance. The planning unit can also create a concise plan for a learning objective with low importance. For example, the planning unit can create a concise plan for a learning objective with low importance. Furthermore, the planning unit can create a moderately detailed plan for a learning objective with medium importance. For example, the planning unit can create a moderately detailed plan for a learning objective with medium importance. This enables an effective learning plan to be created by adjusting the level of detail of the plan based on the importance of the learning objective. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can create a learning plan using an AI model that adjusts the level of detail of the plan based on the importance of the learning objective.

[0044] When formulating a learning plan, the planner can apply different planning algorithms depending on the category of the learning objective. For example, the planner can apply a numerical analysis algorithm to a mathematics learning objective. For example, the planner can apply a numerical analysis algorithm to a mathematics learning objective. The planner can also apply a natural language processing algorithm to a language learning objective. For example, the planner can apply a natural language processing algorithm to a language learning objective. Furthermore, the planner can apply an experimental data analysis algorithm to a science learning objective. For example, the planner can apply an experimental data analysis algorithm to a science learning objective. This enables a more appropriate learning plan by applying different planning algorithms depending on the category of the learning objective. Some or all of the above-described processing in the planner can be performed using, for example, AI, or without AI. For example, the planner can create a learning plan using an AI model that applies different planning algorithms depending on the category of the learning objective.

[0045] When creating a learning plan, the planning unit can determine the priority of the plan based on when the learning goals were set. For example, the planning unit prioritizes recently set learning goals into the plan. For example, the planning unit prioritizes recently set learning goals into the plan. The planning unit can also create a current plan while referring to previously set learning goals. For example, the planning unit can create a current plan while referring to previously set learning goals. Furthermore, the planning unit can prioritize learning goals set during a specific period into the plan. For example, the planning unit can prioritize learning goals set during a specific period into the plan. This enables effective learning plans by determining the priority of the plan based on when the learning goals were set. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can create a learning plan using an AI model that prioritizes plans based on when the learning goals were set.

[0046] When creating a learning plan, the planner can adjust the order of the plan based on the relevance of the learning goals. For example, the planner can prioritize incorporating highly relevant learning goals into the plan. For example, the planner can prioritize incorporating highly relevant learning goals into the plan. The planner can also postpone low-relevance learning goals. For example, the planner can postpone low-relevance learning goals. Furthermore, the planner can moderately incorporate learning goals with medium relevance into the plan. For example, the planner can moderately incorporate learning goals with medium relevance into the plan. This enables an effective learning plan by adjusting the order of the plan based on the relevance of the learning goals. Some or all of the above-described processing in the planner can be performed using, for example, AI, or without AI. For example, the planner can create a learning plan using an AI model that adjusts the order of the plan based on the relevance of the learning goals.

[0047] The question generation unit can adjust the level of detail of questions based on the importance of the training data when generating questions. For example, the question generation unit generates detailed questions for data with high importance. For example, the question generation unit generates detailed questions for data with high importance. The question generation unit can also generate concise questions for data with low importance. For example, the question generation unit can also generate concise questions for data with low importance. Furthermore, the question generation unit can generate questions with a moderate level of detail for data with medium importance. For example, the question generation unit can also generate questions with a moderate level of detail for data with medium importance. This enables effective question generation by adjusting the level of detail of questions based on the importance of the training data. Some or all of the above-described processing in the question generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the question generation unit can generate questions using an AI model that adjusts the level of detail of questions based on the importance of the training data.

[0048] When generating questions, the question generation unit can apply different question generation algorithms depending on the category of the training data. For example, the question generation unit generates questions by applying a numerical analysis algorithm to mathematical data. For example, the question generation unit generates questions by applying a numerical analysis algorithm to mathematical data. The question generation unit can also generate questions by applying a natural language processing algorithm to linguistic data. For example, the question generation unit can generate questions by applying a natural language processing algorithm to linguistic data. Furthermore, the question generation unit can also generate questions by applying an experimental data analysis algorithm to scientific data. For example, the question generation unit can also generate questions by applying an experimental data analysis algorithm to scientific data. This enables more appropriate question generation by applying different question generation algorithms depending on the category of the training data. Some or all of the above-mentioned processing in the question generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the question generation unit can generate questions using an AI model that applies different question generation algorithms depending on the category of the training data.

[0049] When generating questions, the question generation unit can determine the priority of questions based on the time when the learning data was collected. The question generation unit, for example, generates questions based on recently collected data. For example, the question generation unit generates questions based on recently collected data. The question generation unit can also generate questions based on current data while referring to data collected in the past. For example, the question generation unit can generate questions based on current data while referring to data collected in the past. Furthermore, the question generation unit can also focus on reflecting data collected during a specific period in the questions. For example, the question generation unit can also focus on reflecting data collected during a specific period in the questions. This enables effective question generation by determining the priority of questions based on the time when the learning data was collected. Some or all of the above-described processing in the question generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the question generation unit can generate questions using an AI model that determines the priority of questions based on the time when the learning data was collected.

[0050] When generating questions, the question generation unit can adjust the order of questions based on the relevance of the training data. The question generation unit, for example, generates questions based on highly relevant data. For example, the question generation unit generates questions based on highly relevant data. The question generation unit can also generate questions by delaying the generation of questions based on less relevant data. For example, the question generation unit can also generate questions by delaying the generation of questions based on less relevant data. Furthermore, the question generation unit can generate questions by appropriately reflecting data with a medium degree of relevance. For example, the question generation unit can also generate questions by appropriately reflecting data with a medium degree of relevance. This enables effective question generation by adjusting the order of questions based on the relevance of the training data. Some or all of the above-described processing in the question generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the question generation unit can generate questions using an AI model that adjusts the order of questions based on the relevance of the training data.

[0051] The providing unit can adjust the level of detail of the provided information based on the importance of the training data when providing the information. For example, the providing unit provides detailed information for data with high importance. For example, the providing unit provides detailed information for data with high importance. The providing unit can also provide concise information for data with low importance. For example, the providing unit can also provide concise information for data with low importance. Furthermore, the providing unit can provide information with an appropriate level of detail for data with medium importance. For example, the providing unit can also provide information with an appropriate level of detail for data with medium importance. This enables effective information provision by adjusting the level of detail of the provided information based on the importance of the training data. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that adjusts the level of detail of the provided information based on the importance of the training data.

[0052] The providing unit can apply different providing algorithms depending on the category of the training data when providing the data. For example, the providing unit provides information by applying a numerical analysis algorithm to mathematical data. For example, the providing unit provides information by applying a numerical analysis algorithm to mathematical data. The providing unit can also provide information by applying a natural language processing algorithm to linguistic data. For example, the providing unit can provide information by applying a natural language processing algorithm to linguistic data. Furthermore, the providing unit can provide information by applying an experimental data analysis algorithm to scientific data. For example, the providing unit can provide information by applying an experimental data analysis algorithm to scientific data. This enables more appropriate information to be provided by applying different providing algorithms depending on the category of the training data. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that applies different providing algorithms depending on the category of the training data.

[0053] The providing unit can determine the priority of the information to be provided based on the time when the learning data was collected at the time of providing the information. The providing unit, for example, provides information based on recently collected data. For example, the providing unit provides information based on recently collected data. The providing unit can also provide information based on current data while referring to data collected in the past. For example, the providing unit can provide information based on current data while referring to data collected in the past. Furthermore, the providing unit can also reflect data collected during a specific period in the information with a focus. For example, the providing unit can also reflect data collected during a specific period in the information with a focus. This enables effective information provision by determining the priority of the information to be provided based on the time when the learning data was collected. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that determines the priority of the information to be provided based on the time when the learning data was collected.

[0054] The providing unit can adjust the order of the information to be provided based on the relevance of the training data when providing the information. The providing unit, for example, provides information based on highly relevant data. For example, the providing unit provides information based on highly relevant data. The providing unit can also provide information after providing data with low relevance. For example, the providing unit can also provide information after providing data with low relevance. Furthermore, the providing unit can provide information by appropriately reflecting data with a medium degree of relevance. For example, the providing unit can provide information by appropriately reflecting data with a medium degree of relevance. This enables effective information provision by adjusting the order of the information to be provided based on the relevance of the training data. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that adjusts the order of the information to be provided based on the relevance of the training data.

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

[0056] The learning support system can further include a collaborative learning unit. The collaborative learning unit provides a function that allows multiple users to study together. For example, users can create groups and solve problems together. The collaborative learning unit can also provide a chat function that allows users to communicate with each other in real time. Furthermore, the collaborative learning unit can also provide a function that supports the allocation of roles within a group and progress management. This allows users to study together with other users, thereby improving the effectiveness of their learning.

[0057] The learning assistance system can further include a reminder unit. The reminder unit sends learning reminders based on the user's learning schedule. For example, the reminder unit can urge the user to start studying by sending a notification before the study time set by the user. The reminder unit can also send a notification urging the user to resume studying if the user interrupts the study. Furthermore, the reminder unit can also suggest the next content to study based on the user's learning progress. This allows the user to proceed with their studies in a planned manner.

[0058] The learning assistance system may further include a customization unit. The customization unit customizes the learning content and interface according to the user's learning style and preferences. For example, if the user prefers visual learning, content that makes extensive use of visual aids and infographics may be provided. If the user prefers auditory learning, content in audio commentary or podcast format may be provided. Furthermore, the customization unit may adjust the difficulty level and format of the learning content based on user feedback. This allows the user to study in a learning environment that is optimal for them.

[0059] The learning support system can further include a data visualization unit. The data visualization unit provides a function for visually displaying the user's learning data. For example, the user's learning progress can be displayed in graphs or charts. The data visualization unit can also display the user's level of understanding and areas of strength and weakness using different colors. Furthermore, the data visualization unit can display the user's learning history in a timeline format, allowing the user to check past learning results at a glance. This allows the user to intuitively understand their own learning situation.

[0060] The learning assistance system can further include an adaptive testing unit. The adaptive testing unit provides a function to adjust the difficulty of the test according to the user's level of understanding. For example, if the user correctly answers easy questions in succession, the difficulty of the next question can be increased. Also, if the user stumbles on a difficult question, the difficulty of the next question can be decreased. Furthermore, the adaptive testing unit can evaluate the user's overall level of understanding by presenting questions that balance the user's areas of strength and weakness. This allows the user to take a test that is appropriate for their level, maximizing the effectiveness of their learning.

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

[0062] Step 1: The collection unit collects learning data. Learning data includes learning progress, test results, and answer trends. For example, the collection unit records study time and achievement level, and collects test scores, accuracy rates, and correct and incorrect answer patterns as data. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies each individual's level of understanding and areas of strength and weakness. The analysis is carried out using machine learning algorithms, and the collected data is analyzed to evaluate each individual's level of understanding and areas of strength and weakness. Step 3: The planning department creates individual learning plans based on the analysis results obtained by the analysis department. The learning plans are created based on the learning goals set by teachers and parents, and also include the selection of learning schedules and teaching materials. Step 4: The problem generator automatically generates problems based on the learning plan created by the planner. It provides many practice problems for areas with low comprehension, application problems for areas with high comprehension, and problems of moderate difficulty for areas with medium comprehension. Step 5: The provider provides the questions and study plans generated by the question generator via a web application, a mobile application, or email.

[0063] (Example 2) A learning support system according to an embodiment of the present invention uses a cloud platform to collect data on children's learning, which is then analyzed by AI. This system supports learning by setting learning goals in advance, using AI to create individual learning plans, and automatically generating questions. This tool can improve the work efficiency of teachers who are understaffed or overworked. For example, the learning support system collects data on children's learning, such as learning progress, test results, and answer trends. This data is securely stored using a cloud platform. AI then analyzes the collected data. AI identifies each individual's level of understanding and strengths and weaknesses. For example, it evaluates the child's level of understanding of a specific subject or question format and determines which areas the child is strong and weak in. Furthermore, learning goals are set. Teachers or parents set learning goals for their children, and AI creates individual learning plans based on those goals. For example, if the child's goal is to understand a specific unit in one week, AI creates a learning plan to achieve that goal. AI then automatically generates questions based on the individual learning plan. For example, for areas where students have low comprehension, more practice questions are provided, and for areas where students have a high level of comprehension, application questions are provided. This allows children to study effectively at their own pace. Finally, this tool will improve teacher work efficiency. For example, by having AI create learning plans, teachers can reduce the time they spend on individual instruction and focus on other educational activities. Furthermore, by having AI automatically generate questions, teachers are relieved of the burden of creating questions. In this way, by using a cloud platform to collect data on children's studies and having AI analyze it, individual learning support can be realized and teachers' work can be improved. As a result, the learning support system collects children's learning data and has AI analyze it, allowing individual learning support to be realized and teachers' work to be improved.

[0064] A learning assistance system according to an embodiment includes a collection unit, an analysis unit, a planning unit, a question generation unit, and a provision unit. The collection unit collects learning data. The learning data includes, for example, learning progress, test results, and answer trends, but is not limited to these examples. For example, the collection unit collects data such as study time and achievement level to record learning progress. The collection unit can also record test results and collect data such as scores and correct answer rates. The collection unit can also record answer trends and collect data such as correct answer patterns and incorrect answer patterns. For example, the collection unit measures study time and evaluates achievement level to record learning progress. The collection unit can also record test results and evaluate scores and correct answer rates. The collection unit can also record answer trends and evaluate correct answer patterns and incorrect answer patterns. The analysis unit analyzes the data collected by the collection unit to identify each individual's understanding level and strengths and weaknesses. The analysis is performed using, for example, a machine learning algorithm, but is not limited to these examples. For example, the analysis unit uses a machine learning algorithm to analyze the collected data and evaluate the understanding level of each individual. The analysis unit can also use a machine learning algorithm to analyze the collected data and identify strengths and weaknesses. The analysis unit can also use a machine learning algorithm to analyze the collected data and evaluate the level of understanding. For example, the analysis unit uses a machine learning algorithm to analyze the collected data and evaluate the understanding level of each individual. The analysis unit can also use a machine learning algorithm to analyze the collected data and identify strengths and weaknesses. The analysis unit can also use a machine learning algorithm to analyze the collected data and evaluate the level of understanding. The planning unit creates an individual learning plan based on the analysis results obtained by the analysis unit. The learning plan can be created based on, for example, learning goals set by a teacher or a parent, but is not limited to such examples. For example, the planning unit creates an individual learning plan based on the learning goals set by a teacher or a parent. The planning unit can also create a learning schedule based on the learning goals. The planning unit can also select learning materials based on the learning goals.For example, the planning unit creates an individual learning plan based on learning goals set by a teacher or a parent. The planning unit can also create a learning schedule based on the learning goals. The planning unit can also select learning materials based on the learning goals. The question generation unit automatically generates questions based on the learning plan created by the planning unit. For example, the questions may provide more practice questions for areas with low comprehension and more application questions for areas with high comprehension, but are not limited to such examples. For example, the question generation unit provides more practice questions for areas with low comprehension. The question generation unit can also provide application questions for areas with high comprehension. The question generation unit can also provide questions of a moderate level of difficulty for areas with medium comprehension. For example, the question generation unit provides more practice questions for areas with low comprehension. The question generation unit can also provide application questions for areas with high comprehension. The question generation unit can also provide questions of a moderate level of difficulty for areas with medium comprehension. The providing unit provides the questions and learning plan generated by the question generation unit. The provision may be performed, for example, through a web application or a mobile application, but is not limited to such examples. For example, the providing unit provides the generated questions and study plans through a web application. The providing unit can also provide the generated questions and study plans through a mobile application. Furthermore, the providing unit can provide the generated questions and study plans by email. For example, the providing unit provides the generated questions and study plans through a web application. The providing unit can also provide the generated questions and study plans through a mobile application. Furthermore, the providing unit can provide the generated questions and study plans by email. As a result, the learning support system according to the embodiment collects children's learning data and has AI analyze it, thereby realizing individual learning support and improving the work efficiency of teachers.

[0065] The collection unit can collect data such as learning progress, test results, and answer trends. For example, the collection unit collects data such as study time and achievement level to record learning progress. For example, the collection unit measures study time and evaluates achievement level to record learning progress. The collection unit can also record test results and collect scores and correct answer rates as data. For example, the collection unit can record test results and evaluate scores and correct answer rates. Furthermore, the collection unit can record answer trends and collect correct answer patterns and incorrect answer patterns as data. For example, the collection unit can record answer trends and evaluate correct answer patterns and incorrect answer patterns. In this way, detailed learning data can be obtained by collecting data such as learning progress, test results, and answer trends. 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 collect data using an AI model that measures study time and evaluates achievement level to record learning progress.

[0066] The analysis unit can analyze the collected data using a machine learning algorithm to identify each individual's understanding level and strengths and weaknesses. The analysis unit can, for example, use a machine learning algorithm to analyze the collected data and evaluate each individual's understanding level. For example, the analysis unit can analyze the collected data using a machine learning algorithm to evaluate each individual's understanding level. The analysis unit can also analyze the collected data using a machine learning algorithm to identify each individual's strengths and weaknesses. For example, the analysis unit can analyze the collected data using a machine learning algorithm to identify each individual's strengths and weaknesses. The analysis unit can also analyze the collected data using a machine learning algorithm to evaluate each individual's understanding level. The analysis unit can also analyze the collected data using a machine learning algorithm to evaluate each individual's understanding level. In this way, the use of a machine learning algorithm can accurately identify each individual's understanding level and strengths and weaknesses. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the collected data using a machine learning algorithm to analyze the data using an AI model that identifies each individual's understanding level and strengths and weaknesses.

[0067] The planning unit can create an individual learning plan based on learning goals set by a teacher or a parent. The planning unit, for example, creates an individual learning plan based on learning goals set by a teacher or a parent. For example, the planning unit creates an individual learning plan based on learning goals set by a teacher or a parent. The planning unit can also create a learning schedule based on the learning goals. For example, the planning unit can create a learning schedule based on the learning goals. Furthermore, the planning unit can select learning materials based on the learning goals. For example, the planning unit can select learning materials based on the learning goals. In this way, individual learning support is realized by creating an individual learning plan based on the learning goals set by a teacher or a parent. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can create a learning plan using an AI model that creates an individual learning plan based on learning goals set by a teacher or a parent.

[0068] The question generation unit can provide more practice questions for areas with low levels of understanding and provide application questions for areas with high levels of understanding. The question generation unit, for example, provides more practice questions for areas with low levels of understanding. For example, the question generation unit provides more practice questions for areas with low levels of understanding. The question generation unit can also provide application questions for areas with high levels of understanding. For example, the question generation unit can also provide application questions for areas with high levels of understanding. Furthermore, the question generation unit can provide questions of a moderate level of difficulty for areas with medium levels of understanding. For example, the question generation unit can also provide questions of a moderate level of difficulty for areas with medium levels of understanding. This enables effective learning by providing questions according to the level of understanding. Some or all of the above-mentioned processing in the question generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the question generation unit can generate questions using an AI model that provides more practice questions for areas with low levels of understanding.

[0069] The providing unit can provide the generated questions and study plans to the user. The providing unit, for example, provides the generated questions and study plans through a web application. For example, the providing unit can provide the generated questions and study plans through a web application. The providing unit can also provide the generated questions and study plans through a mobile application. For example, the providing unit can also provide the generated questions and study plans through a mobile application. Furthermore, the providing unit can provide the generated questions and study plans by email. For example, the providing unit can also provide the generated questions and study plans by email. In this way, by providing the generated questions and study plans to the user, progress in learning is supported. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that provides the generated questions and study plans.

[0070] The collection unit can estimate the user's emotions and adjust the timing of collecting training data based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit temporarily stops collecting training data and resumes it when the user is relaxed. For example, if the user is feeling stressed, the collection unit temporarily stops collecting training data and resumes it when the user is relaxed. The collection unit can also intensively collect training data when the user is concentrating. For example, if the user is concentrating, the collection unit can also intensively collect training data when the user is concentrating. Furthermore, if the user is tired, the collection unit can refrain from collecting training data and resume it after a break. For example, if the user is tired, the collection unit can refrain from collecting training data and resume it after a break. This enables more appropriate data collection by adjusting the timing of collecting training data according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may collect data using an AI model that estimates a user's emotions and adjusts the timing of collecting training data based on the estimated emotions.

[0071] When collecting learning data, the collection unit can analyze the user's past learning history and select the optimal collection method. The collection unit, for example, collects detailed data on subjects in which the user has achieved high grades in the past. For example, the collection unit collects detailed data on subjects in which the user has achieved high grades in the past. The collection unit can also focus on collecting data on subjects in which the user has struggled in the past. For example, the collection unit can also focus on collecting data on subjects in which the user has struggled in the past. Furthermore, the collection unit can identify the most effective learning method from the user's past learning history and collect data based on that method. For example, the collection unit can identify the most effective learning method from the user's past learning history and collect data based on that method. In this way, the optimal collection method can be selected by analyzing the user's past learning history. 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 collect data using an AI model that analyzes the user's past learning history and selects the optimal collection method.

[0072] When collecting learning data, the collection unit can filter the learning data based on the user's current learning environment and areas of interest. For example, if the user is studying in a quiet environment, the collection unit collects data appropriate for that environment. For example, if the user is studying in a quiet environment, the collection unit collects data appropriate for that environment. Furthermore, if the user is interested in a particular field, the collection unit can prioritize collecting data related to that field. For example, if the user is interested in a particular field, the collection unit can prioritize collecting data related to that field. Furthermore, if the user is studying in a group, the collection unit can collect data for the entire group and compare it with individual data. For example, if the user is studying in a group, the collection unit can collect data for the entire group and compare it with individual data. In this way, by filtering data based on the user's current learning environment and areas of interest, more relevant data can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model that filters data based on the user's current learning environment and areas of interest.

[0073] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. For example, when the user is relaxed, the collection unit prioritizes collecting detailed data. For example, when the user is relaxed, the collection unit prioritizes collecting detailed data. Furthermore, when the user is feeling stressed, the collection unit can prioritize collecting basic data. For example, when the user is feeling stressed, the collection unit can prioritize collecting basic data. Furthermore, when the user is concentrating, the collection unit can prioritize collecting data related to a specific subject or field. For example, when the user is concentrating, the collection unit can prioritize collecting data related to a specific subject or field. This enables more effective data collection by determining the priority of data based on the user'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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can collect data using an AI model that estimates a user's emotions and prioritizes the data to collect based on the estimated emotions.

[0074] When collecting learning data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user lives in a specific area, the collection unit prioritizes collecting data related to that area. For example, if the user lives in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, if the user is traveling, the collection unit can prioritize collecting data related to the travel destination. For example, if the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, if the user is at school, the collection unit can prioritize collecting data related to the school curriculum. For example, if the user is at school, the collection unit can prioritize collecting data related to the school curriculum. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can collect data using an AI model that prioritizes collecting highly relevant data by taking into account the user's geographical location information.

[0075] The collection unit can analyze the user's social media activities and collect related data when collecting learning data. The collection unit can collect related data, for example, based on learning content shared by the user on social media. For example, the collection unit can collect related data based on learning content shared by the user on social media. The collection unit can also collect information on education-related accounts followed by the user on social media. For example, the collection unit can collect information on education-related accounts followed by the user on social media. The collection unit can also collect activity content of learning groups in which the user participates on social media. For example, the collection unit can collect activity content of learning groups in which the user participates on social media. In this way, related data can be collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit can be performed, for example, using AI, or can be performed without using AI. For example, the collection unit can collect data using an AI model that analyzes the user's social media activities and collects related data.

[0076] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, when the user is relaxed, the analysis unit provides detailed analysis results. Furthermore, when the user is stressed, the analysis unit can provide concise analysis results. For example, when the user is stressed, the analysis unit can provide concise analysis results. Furthermore, when the user is concentrating, the analysis unit can provide analysis results focused on a specific area. For example, when the user is concentrating, the analysis unit can provide analysis results focused on a specific area. This allows for adjusting the way the analysis is presented based on the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can provide analysis results using an AI model that infers a user's emotions and adjusts the way the analysis is presented based on the inferred emotions.

[0077] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the training data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a brief analysis on data with low importance. For example, the analysis unit can also perform a brief analysis on data with low importance. Furthermore, the analysis unit can perform an analysis with a moderate level of detail on data with medium importance. For example, the analysis unit can also perform an analysis with a moderate level of detail on data with medium importance. This enables effective analysis by adjusting the level of detail of the analysis based on the importance of the training data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze data using an AI model that adjusts the level of detail of the analysis based on the importance of the training data.

[0078] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the training data. For example, the analysis unit applies a numerical analysis algorithm to mathematical data. For example, the analysis unit applies a numerical analysis algorithm to mathematical data. The analysis unit can also apply a natural language processing algorithm to linguistic data. For example, the analysis unit can apply a natural language processing algorithm to linguistic data. Furthermore, the analysis unit can apply an experimental data analysis algorithm to scientific data. For example, the analysis unit can apply an experimental data analysis algorithm to scientific data. This enables more appropriate analysis by applying different analysis algorithms depending on the category of the training data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze data using an AI model that applies different analysis algorithms depending on the category of the training data.

[0079] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit provides a detailed analysis when the user is relaxed. For example, the analysis unit provides a detailed analysis when the user is relaxed. The analysis unit can also provide a brief analysis when the user is stressed. For example, the analysis unit can provide a brief analysis when the user is stressed. Furthermore, the analysis unit can provide an analysis focused on a specific area when the user is concentrating. For example, the analysis unit can provide an analysis focused on a specific area when the user is concentrating. This allows for adjusting the length of the analysis based on the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can provide analysis results using an AI model that estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions.

[0080] During analysis, the analysis unit can determine the priority of the analysis based on the time when the learning data was collected. For example, the analysis unit prioritizes the analysis of recently collected data. For example, the analysis unit prioritizes the analysis of recently collected data. The analysis unit can also analyze current data while referring to data collected in the past. For example, the analysis unit can analyze current data while referring to data collected in the past. Furthermore, the analysis unit can focus on analyzing data collected during a specific period. For example, the analysis unit can focus on analyzing data collected during a specific period. This enables effective analysis by determining the priority of the analysis based on the time when the learning data was collected. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze data using an AI model that determines the priority of the analysis based on the time when the learning data was collected.

[0081] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the training data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of low-relevance data. For example, the analysis unit can also postpone analysis of low-relevance data. Furthermore, the analysis unit can moderately analyze data with medium relevance. For example, the analysis unit can moderately analyze data with medium relevance. This enables effective analysis by adjusting the order of analysis based on the relevance of the training data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze data using an AI model that adjusts the order of analysis based on the relevance of the training data.

[0082] The planning unit can estimate the user's emotions and adjust the presentation method of the study plan based on the estimated user's emotions. For example, when the user is relaxed, the planning unit provides a detailed study plan. For example, when the user is relaxed, the planning unit provides a detailed study plan. The planning unit can also provide a concise study plan when the user is stressed. For example, when the user is stressed, the planning unit can also provide a concise study plan. Furthermore, when the user is concentrating, the planning unit can provide a study plan that focuses on a specific subject. For example, when the user is concentrating, the planning unit can also provide a study plan that focuses on a specific subject. This allows for adjusting the presentation method of the study plan based on the user's emotions to provide a more appropriate study plan. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the planning unit can be performed using, for example, an AI, or without using an AI. For example, the planning unit can provide a study plan using an AI model that estimates the user's emotions and adjusts the way the study plan is presented based on the estimated emotions.

[0083] When creating a learning plan, the planning unit can adjust the level of detail of the plan based on the importance of the learning objective. For example, the planning unit creates a detailed plan for a learning objective with high importance. For example, the planning unit creates a detailed plan for a learning objective with high importance. The planning unit can also create a concise plan for a learning objective with low importance. For example, the planning unit can create a concise plan for a learning objective with low importance. Furthermore, the planning unit can create a moderately detailed plan for a learning objective with medium importance. For example, the planning unit can create a moderately detailed plan for a learning objective with medium importance. This enables an effective learning plan to be created by adjusting the level of detail of the plan based on the importance of the learning objective. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can create a learning plan using an AI model that adjusts the level of detail of the plan based on the importance of the learning objective.

[0084] When formulating a learning plan, the planner can apply different planning algorithms depending on the category of the learning objective. For example, the planner can apply a numerical analysis algorithm to a mathematics learning objective. For example, the planner can apply a numerical analysis algorithm to a mathematics learning objective. The planner can also apply a natural language processing algorithm to a language learning objective. For example, the planner can apply a natural language processing algorithm to a language learning objective. Furthermore, the planner can apply an experimental data analysis algorithm to a science learning objective. For example, the planner can apply an experimental data analysis algorithm to a science learning objective. This enables a more appropriate learning plan by applying different planning algorithms depending on the category of the learning objective. Some or all of the above-described processing in the planner can be performed using, for example, AI, or without AI. For example, the planner can create a learning plan using an AI model that applies different planning algorithms depending on the category of the learning objective.

[0085] The planner can estimate the user's emotions and adjust the length of the study plan based on the estimated user emotions. For example, if the user is relaxed, the planner can provide a detailed study plan. For example, if the user is relaxed, the planner can provide a detailed study plan. The planner can also provide a concise study plan if the user is stressed. For example, if the user is stressed, the planner can provide a concise study plan. Furthermore, if the user is concentrating, the planner can provide a study plan that focuses on a specific subject. For example, if the user is concentrating, the planner can provide a study plan that focuses on a specific subject. This allows for adjusting the length of the study plan based on the user's emotions, thereby providing a more appropriate study plan. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the planner can be performed using, for example, an AI, or without an AI. For example, the planner can provide a lesson plan using an AI model that estimates a user's emotions and adjusts the length of the lesson plan based on the estimated emotions.

[0086] When creating a learning plan, the planning unit can determine the priority of the plan based on when the learning goals were set. For example, the planning unit prioritizes recently set learning goals into the plan. For example, the planning unit prioritizes recently set learning goals into the plan. The planning unit can also create a current plan while referring to previously set learning goals. For example, the planning unit can create a current plan while referring to previously set learning goals. Furthermore, the planning unit can prioritize learning goals set during a specific period into the plan. For example, the planning unit can prioritize learning goals set during a specific period into the plan. This enables effective learning plans by determining the priority of the plan based on when the learning goals were set. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can create a learning plan using an AI model that prioritizes plans based on when the learning goals were set.

[0087] When creating a learning plan, the planner can adjust the order of the plan based on the relevance of the learning goals. For example, the planner can prioritize incorporating highly relevant learning goals into the plan. For example, the planner can prioritize incorporating highly relevant learning goals into the plan. The planner can also postpone low-relevance learning goals. For example, the planner can postpone low-relevance learning goals. Furthermore, the planner can moderately incorporate learning goals with medium relevance into the plan. For example, the planner can moderately incorporate learning goals with medium relevance into the plan. This enables an effective learning plan by adjusting the order of the plan based on the relevance of the learning goals. Some or all of the above-described processing in the planner can be performed using, for example, AI, or without AI. For example, the planner can create a learning plan using an AI model that adjusts the order of the plan based on the relevance of the learning goals.

[0088] The question generator can estimate the user's emotions and adjust the way questions are presented based on the estimated user emotions. For example, when the user is relaxed, the question generator provides detailed questions. For example, when the user is relaxed, the question generator provides detailed questions. The question generator can also provide concise questions when the user is stressed. For example, when the user is stressed, the question generator can also provide concise questions. Furthermore, when the user is concentrating, the question generator can provide questions that focus on a specific area. For example, when the user is concentrating, the question generator can also provide questions that focus on a specific area. This allows for adjusting the way questions are presented based on the user's emotions to provide more appropriate questions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the question generator can be performed using, for example, an AI, or without an AI. For example, the question generator can provide questions using an AI model that estimates the user's emotions and adjusts the way the questions are presented based on the estimated emotions.

[0089] The question generation unit can adjust the level of detail of questions based on the importance of the training data when generating questions. For example, the question generation unit generates detailed questions for data with high importance. For example, the question generation unit generates detailed questions for data with high importance. The question generation unit can also generate concise questions for data with low importance. For example, the question generation unit can also generate concise questions for data with low importance. Furthermore, the question generation unit can generate questions with a moderate level of detail for data with medium importance. For example, the question generation unit can also generate questions with a moderate level of detail for data with medium importance. This enables effective question generation by adjusting the level of detail of questions based on the importance of the training data. Some or all of the above-described processing in the question generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the question generation unit can generate questions using an AI model that adjusts the level of detail of questions based on the importance of the training data.

[0090] When generating questions, the question generation unit can apply different question generation algorithms depending on the category of the training data. For example, the question generation unit generates questions by applying a numerical analysis algorithm to mathematical data. For example, the question generation unit generates questions by applying a numerical analysis algorithm to mathematical data. The question generation unit can also generate questions by applying a natural language processing algorithm to linguistic data. For example, the question generation unit can generate questions by applying a natural language processing algorithm to linguistic data. Furthermore, the question generation unit can also generate questions by applying an experimental data analysis algorithm to scientific data. For example, the question generation unit can also generate questions by applying an experimental data analysis algorithm to scientific data. This enables more appropriate question generation by applying different question generation algorithms depending on the category of the training data. Some or all of the above-mentioned processing in the question generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the question generation unit can generate questions using an AI model that applies different question generation algorithms depending on the category of the training data.

[0091] The question generator can estimate the user's emotions and adjust the length of the questions based on the estimated user emotions. For example, the question generator can provide detailed questions when the user is relaxed. For example, the question generator can provide detailed questions when the user is relaxed. The question generator can also provide concise questions when the user is stressed. For example, the question generator can provide concise questions when the user is stressed. Furthermore, the question generator can provide questions that focus on a specific subject when the user is concentrating. For example, the question generator can provide questions that focus on a specific subject when the user is concentrating. This allows for adjusting the length of the questions based on the user's emotions to provide more appropriate questions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the question generator can be performed, for example, using AI, or without AI. For example, the question generator can provide questions using an AI model that estimates the user's emotions and adjusts the length of the questions based on the estimated emotions.

[0092] When generating questions, the question generation unit can determine the priority of questions based on the time when the learning data was collected. The question generation unit, for example, generates questions based on recently collected data. For example, the question generation unit generates questions based on recently collected data. The question generation unit can also generate questions based on current data while referring to data collected in the past. For example, the question generation unit can generate questions based on current data while referring to data collected in the past. Furthermore, the question generation unit can also focus on reflecting data collected during a specific period in the questions. For example, the question generation unit can also focus on reflecting data collected during a specific period in the questions. This enables effective question generation by determining the priority of questions based on the time when the learning data was collected. Some or all of the above-described processing in the question generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the question generation unit can generate questions using an AI model that determines the priority of questions based on the time when the learning data was collected.

[0093] When generating questions, the question generation unit can adjust the order of questions based on the relevance of the training data. The question generation unit, for example, generates questions based on highly relevant data. For example, the question generation unit generates questions based on highly relevant data. The question generation unit can also generate questions by delaying the generation of questions based on less relevant data. For example, the question generation unit can also generate questions by delaying the generation of questions based on less relevant data. Furthermore, the question generation unit can generate questions by appropriately reflecting data with a medium degree of relevance. For example, the question generation unit can also generate questions by appropriately reflecting data with a medium degree of relevance. This enables effective question generation by adjusting the order of questions based on the relevance of the training data. Some or all of the above-described processing in the question generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the question generation unit can generate questions using an AI model that adjusts the order of questions based on the relevance of the training data.

[0094] The providing unit can estimate the user's emotions and adjust the presentation method of the information to be provided based on the estimated user's emotions. For example, when the user is relaxed, the providing unit provides detailed information. For example, when the user is relaxed, the providing unit provides detailed information. Furthermore, when the user is feeling stressed, the providing unit can provide concise information. For example, when the user is feeling stressed, the providing unit can provide concise information. Furthermore, when the user is concentrating, the providing unit can provide information focused on a specific field. For example, when the user is concentrating, the providing unit can provide information focused on a specific field. This allows for adjusting the presentation method of the information to be provided based on the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can provide information using an AI model that estimates the user's emotions and adjusts the way the information is presented based on the estimated emotions.

[0095] The providing unit can adjust the level of detail of the provided information based on the importance of the training data when providing the information. For example, the providing unit provides detailed information for data with high importance. For example, the providing unit provides detailed information for data with high importance. The providing unit can also provide concise information for data with low importance. For example, the providing unit can also provide concise information for data with low importance. Furthermore, the providing unit can provide information with an appropriate level of detail for data with medium importance. For example, the providing unit can also provide information with an appropriate level of detail for data with medium importance. This enables effective information provision by adjusting the level of detail of the provided information based on the importance of the training data. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that adjusts the level of detail of the provided information based on the importance of the training data.

[0096] The providing unit can apply different providing algorithms depending on the category of the training data when providing the data. For example, the providing unit provides information by applying a numerical analysis algorithm to mathematical data. For example, the providing unit provides information by applying a numerical analysis algorithm to mathematical data. The providing unit can also provide information by applying a natural language processing algorithm to linguistic data. For example, the providing unit can provide information by applying a natural language processing algorithm to linguistic data. Furthermore, the providing unit can provide information by applying an experimental data analysis algorithm to scientific data. For example, the providing unit can provide information by applying an experimental data analysis algorithm to scientific data. This enables more appropriate information to be provided by applying different providing algorithms depending on the category of the training data. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that applies different providing algorithms depending on the category of the training data.

[0097] The providing unit can estimate the user's emotions and adjust the length of the information to be provided based on the estimated user's emotions. For example, when the user is relaxed, the providing unit provides detailed information. For example, when the user is relaxed, the providing unit provides detailed information. Furthermore, when the user is stressed, the providing unit can provide concise information. For example, when the user is stressed, the providing unit can provide concise information. Furthermore, when the user is concentrating, the providing unit can provide information focused on a specific field. For example, when the user is concentrating, the providing unit can provide information focused on a specific field. This allows for adjusting the length of the information to be provided based on the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using an AI, or may be performed without an AI. For example, the providing unit can provide information using an AI model that estimates the user's emotions and adjusts the length of the information to be provided based on the estimated emotions.

[0098] The providing unit can determine the priority of the information to be provided based on the time when the learning data was collected at the time of providing the information. The providing unit, for example, provides information based on recently collected data. For example, the providing unit provides information based on recently collected data. The providing unit can also provide information based on current data while referring to data collected in the past. For example, the providing unit can provide information based on current data while referring to data collected in the past. Furthermore, the providing unit can also reflect data collected during a specific period in the information with a focus. For example, the providing unit can also reflect data collected during a specific period in the information with a focus. This enables effective information provision by determining the priority of the information to be provided based on the time when the learning data was collected. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that determines the priority of the information to be provided based on the time when the learning data was collected.

[0099] The providing unit can adjust the order of the information to be provided based on the relevance of the training data when providing the information. The providing unit, for example, provides information based on highly relevant data. For example, the providing unit provides information based on highly relevant data. The providing unit can also provide information after providing data with low relevance. For example, the providing unit can also provide information after providing data with low relevance. Furthermore, the providing unit can provide information by appropriately reflecting data with a medium degree of relevance. For example, the providing unit can provide information by appropriately reflecting data with a medium degree of relevance. This enables effective information provision by adjusting the order of the information to be provided based on the relevance of the training data. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that adjusts the order of the information to be provided based on the relevance of the training data. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, planning unit, question generation unit, and provision unit, 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 learning data using the camera 42 and microphone 38B of the smart device 14 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to evaluate each individual's understanding level. The planner is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates an individual learning plan based on the analysis results. The question generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates questions based on the learning plan. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the generated questions and learning plan to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, planning unit, question generation unit, and provision 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 learning data using the camera 42 and microphone 238 of the smart glasses 214 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and evaluates each individual's understanding level. The planner, realized, for example, by the specific processing unit 290 of the data processing device 12, creates an individual learning plan based on the analysis results. The question generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, automatically generates questions based on the learning plan. The provision unit, realized, for example, by the control unit 46A of the smart glasses 214, provides the generated questions and learning plan to the user. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, planning unit, question generation unit, and provision 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 learning data using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to evaluate each individual's understanding level. The planner is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates an individual learning plan based on the analysis results. The question generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates questions based on the learning plan. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and provides the generated questions and learning plan to the user. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, planning unit, question generation unit, and provision 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 collects learning data using the camera 42 and microphone 238 of the robot 414 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to evaluate each individual's understanding level. The planner is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates an individual learning plan based on the analysis results. The question generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates questions based on the learning plan. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the generated questions and learning plan to the user.

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

[0101] The learning assistance system may further include a feedback unit. The feedback unit provides real-time feedback based on the user's learning progress and level of understanding. For example, if the user repeatedly makes mistakes on a particular problem, an explanation for that problem may be provided. Also, if the user achieves high marks in a particular area, a message of praise or encouragement may be sent. Furthermore, the feedback unit may suggest adjustments to the learning plan according to the user's learning pace. This allows the user to receive appropriate feedback according to their learning situation, thereby maintaining their motivation to learn.

[0102] The learning assistance system may further include a reward unit. The reward unit provides rewards when the user achieves the learning goal set by the user. For example, if the user achieves a learning goal for one week, a digital badge or points may be awarded. Also, if the user achieves a high score on a specific test, special content or in-game items may be provided. Furthermore, the reward unit may suggest individually customized rewards based on the user's learning history. This may increase the user's motivation to study.

[0103] The learning support system can further include a collaborative learning unit. The collaborative learning unit provides a function that allows multiple users to study together. For example, users can create groups and solve problems together. The collaborative learning unit can also provide a chat function that allows users to communicate with each other in real time. Furthermore, the collaborative learning unit can also provide a function that supports the allocation of roles within a group and progress management. This allows users to study together with other users, thereby improving the effectiveness of their learning.

[0104] The learning assistance system can further include a reminder unit. The reminder unit sends learning reminders based on the user's learning schedule. For example, the reminder unit can urge the user to start studying by sending a notification before the study time set by the user. The reminder unit can also send a notification urging the user to resume studying if the user interrupts the study. Furthermore, the reminder unit can also suggest the next content to study based on the user's learning progress. This allows the user to proceed with their studies in a planned manner.

[0105] The learning assistance system may further include a customization unit. The customization unit customizes the learning content and interface according to the user's learning style and preferences. For example, if the user prefers visual learning, content that makes extensive use of visual aids and infographics may be provided. If the user prefers auditory learning, content in audio commentary or podcast format may be provided. Furthermore, the customization unit may adjust the difficulty level and format of the learning content based on user feedback. This allows the user to study in a learning environment that is optimal for them.

[0106] The learning assistance system may further include a health management unit. The health management unit monitors the user's health status and provides advice to maximize learning efficiency. For example, if the user continues studying for a long time, it may send a notification urging the user to take a break. The health management unit may also analyze the user's sleep patterns and food records to suggest optimal study times. Furthermore, the health management unit may monitor the user's stress level and provide guidance on relaxation exercises and meditation. This allows the user to study effectively while maintaining their health.

[0107] The learning support system may further include a progress sharing unit. The progress sharing unit provides a function for sharing the user's learning progress with family and friends. For example, when the user achieves a specific goal, the user can notify family and friends of the achievement. The progress sharing unit may also provide a platform for the user to share the knowledge and skills acquired in the course of learning with other users. Furthermore, the progress sharing unit may provide a function for users to check the progress of other users and encourage each other. This makes it easier for users to maintain their motivation to learn.

[0108] The learning support system can further include a data visualization unit. The data visualization unit provides a function for visually displaying the user's learning data. For example, the user's learning progress can be displayed in graphs or charts. The data visualization unit can also display the user's level of understanding and areas of strength and weakness using different colors. Furthermore, the data visualization unit can display the user's learning history in a timeline format, allowing the user to check past learning results at a glance. This allows the user to intuitively understand their own learning situation.

[0109] The learning assistance system can further include an adaptive testing unit. The adaptive testing unit provides a function to adjust the difficulty of the test according to the user's level of understanding. For example, if the user correctly answers easy questions in succession, the difficulty of the next question can be increased. Also, if the user stumbles on a difficult question, the difficulty of the next question can be decreased. Furthermore, the adaptive testing unit can evaluate the user's overall level of understanding by presenting questions that balance the user's areas of strength and weakness. This allows the user to take a test that is appropriate for their level, maximizing the effectiveness of their learning.

[0110] The learning support system can further include an emotion analysis unit. The emotion analysis unit analyzes the user's emotions in real time and provides support according to the progress of the learning. For example, if the user is feeling stressed, it can provide relaxation advice or suggest a break. Also, if the user is concentrating, it can provide environmental settings and learning content to help the user maintain that state. Furthermore, the emotion analysis unit can accumulate the user's emotion data and analyze long-term changes in emotion to optimize the learning plan. This allows the user to receive appropriate support according to their emotional state, improving the effectiveness of their learning.

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

[0112] Step 1: The collection unit collects learning data. Learning data includes learning progress, test results, and answer trends. For example, the collection unit records study time and achievement level, and collects test scores, accuracy rates, and correct and incorrect answer patterns as data. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies each individual's level of understanding and areas of strength and weakness. The analysis is carried out using machine learning algorithms, and the collected data is analyzed to evaluate each individual's level of understanding and areas of strength and weakness. Step 3: The planning department creates individual learning plans based on the analysis results obtained by the analysis department. The learning plans are created based on the learning goals set by teachers and parents, and also include the selection of learning schedules and teaching materials. Step 4: The problem generator automatically generates problems based on the learning plan created by the planner. It provides many practice problems for areas with low comprehension, application problems for areas with high comprehension, and problems of moderate difficulty for areas with medium comprehension. Step 5: The provider provides the questions and study plans generated by the question generator via a web application, a mobile application, or email.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

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

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0166] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0176] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0177] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0178] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects learning data; an analysis unit that analyzes the data collected by the collection unit and identifies the understanding state or strengths and weaknesses of each individual; a planning unit that creates an individual learning plan based on the analysis results obtained by the analysis unit; a question generation unit that automatically generates questions based on the learning plan established by the planning unit; a providing unit that provides the questions and study plans generated by the question generating unit. A system characterized by:

2. The collecting unit Collect data such as learning progress or test results, and response trends 2. The system of claim 1.

3. The analysis unit The collected data is analyzed using machine learning algorithms to identify each individual's level of understanding and areas of strength and weakness.

2. The system of claim 1.

4. The planning unit Create individualized learning plans based on learning goals set by teachers and parents 2. The system of claim 1.

5. The question generator Provide more practice questions for areas where understanding is low, and more application questions for areas where understanding is high 2. The system of claim 1.

6. The providing unit Providing generated questions and study plans to users 2. The system of claim 1.

7. The collecting unit Estimate user emotions and adjust the timing of learning data collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit When collecting learning data, analyze the user's past learning history and select the optimal collection method.

2. The system of claim 1.

9. The collecting unit When collecting learning data, filter it based on the user's current learning environment and areas of interest.

2. The system of claim 1.

10. The collecting unit Estimate user emotions and prioritize data collection based on the estimated user emotions 2. The system of claim 1.

Images