system

Abstract

The system according to this embodiment aims to analyze learning progress and provide efficient and effective learning methods and examination plans. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an answer unit, and a plan provision unit. The collection unit collects learning data. The analysis unit analyzes the data collected by the collection unit and analyzes the learning progress. The answer unit answers questions based on the results analyzed by the analysis unit. The plan provision unit provides an examination plan based on the answers provided by the answer 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 efficiently analyze learning progress or provide exam plans, and there is room for improvement.

[0005] The system according to this embodiment aims to analyze learning progress and provide efficient and effective learning methods and examination plans. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an answering unit, and a plan providing unit. The collection unit collects learning data. The analysis unit analyzes the data collected by the collection unit and analyzes learning progress. The answering unit answers questions based on the results of the analysis by the analysis unit. The plan providing unit provides an exam plan based on the answers provided by the answering unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze learning progress and provide efficient and effective learning methods and exam plans. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An interactive online education system according to an embodiment of the present invention utilizes a generative AI to solve various problems associated with traditional cram schools and online education. This interactive online education system provides three types of interactive support, enabling maximum results with minimal effort. First, it provides an efficient and effective learning method. The generative AI analyzes each student's learning progress and level of understanding in real time and proposes an optimal learning plan. Second, it provides thorough instruction from a teacher's perspective. The generative AI immediately answers students' questions and provides detailed explanations. Finally, it provides an exam plan tailored to each exam year. The generative AI considers each student's goals and progress to create an optimal study schedule tailored to each exam year. For example, the generative AI collects students' learning data in real time and analyzes their learning progress. Next, the generative AI answers students' questions based on the analysis results and provides detailed explanations. Finally, the generative AI provides an exam plan based on the answers and creates an optimal study schedule tailored to each exam year. In this way, an interactive online education system using generative AI maximizes students' learning outcomes by providing efficient and effective learning methods, thorough instruction from a teacher's perspective, and exam plans tailored to each exam year. This allows the interactive online education system to collect students' learning data in real time, analyze their learning progress, answer questions, and provide exam plans.

[0029] The interactive online education system according to this embodiment comprises a data collection unit, an analysis unit, an answering unit, and a plan provision unit. The data collection unit collects student learning data. Student learning data includes, but is not limited to, text data, numerical data, and image data. The data collection unit collects, for example, learning data entered by students into an online platform. The data collection unit can also automatically acquire data from learning applications. Furthermore, the data collection unit can collect student learning environment data using sensors. For example, the data collection unit collects data from learning applications in real time to understand learning progress. The analysis unit analyzes the data collected by the data collection unit to analyze learning progress. The analysis unit analyzes the data using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit evaluates learning progress and measures comprehension based on student learning data. The analysis unit can also analyze trends in learning data and evaluate the effectiveness of learning. Furthermore, the analysis unit can analyze data using generative AI to understand learning progress in real time. The answering unit answers student questions based on the results analyzed by the analysis unit. The answering unit provides, for example, FAQ-based answers or automated answers generated by AI. For instance, the answering unit instantly answers students' questions and provides detailed explanations. It can also use AI to generate the optimal answer to a student's question. Furthermore, the answering unit can evaluate the student's level of understanding based on the analysis results and provide appropriate guidance. The plan provisioning unit provides an examination plan based on the answers provided by the answering unit. For example, the plan provisioning unit suggests learning schedules and materials. For instance, it considers the student's goals and progress and creates an optimal learning schedule tailored to the examination year. It can also use AI to analyze the student's learning data and provide an optimal examination plan. Furthermore, the plan provisioning unit can monitor the student's learning progress in real time and adjust the plan as needed. Thus, the interactive online education system according to this embodiment can collect and analyze student learning data, answer questions, and provide examination plans.

[0030] The collection unit can collect student learning data in real time. Real-time includes, but is not limited to, the frequency and delay of data collection. For example, the collection unit collects learning data entered by students into an online platform in real time. The collection unit can also automatically acquire data from a learning application. For example, the collection unit collects data from the learning application in real time to understand the student's learning progress. The collection unit can also collect student learning environment data in real time using sensors. For example, the collection unit collects data such as the temperature and illuminance of the learning environment in real time to help optimize the learning environment. This allows the student's learning data to be collected in real time to understand their latest learning situation. Some or all of the above-described processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can input data from the learning application into the generation AI and have the generation AI perform real-time data collection.

[0031] The analysis unit can analyze learning progress based on the collected data. Learning progress includes, but is not limited to, test results and study time. The analysis unit analyzes the data using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit evaluates learning progress and measures understanding based on the student's learning data. The analysis unit can also analyze trends in the learning data and evaluate the effectiveness of learning. Furthermore, the analysis unit can analyze data using a generation AI to grasp learning progress in real time. For example, the analysis unit can input learning data into the generation AI and have the generation AI analyze the learning progress. In this way, by analyzing learning progress based on the collected data, the student's learning situation can be accurately grasped. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI.

[0032] The answering unit can answer the student's question based on the analysis results. The analysis results include, but are not limited to, numerical data and graph displays. The answering unit can provide, for example, FAQ-based answers or automatic answers using a generation AI. For example, the answering unit can instantly answer the student's question and provide detailed explanations. The answering unit can also generate optimal answers to the student's questions using the generation AI. Furthermore, the answering unit can evaluate the student's understanding based on the analysis results and provide appropriate guidance. For example, the answering unit can input the analysis results to the generation AI and have the generation AI execute an answer to the student's question. This enables appropriate guidance by answering the student's question based on the analysis results. Some or all of the above-mentioned processing in the answering unit can be performed, for example, using the generation AI, or without using the generation AI.

[0033] The plan providing unit can provide an exam plan based on the answer results. The answer results include, but are not limited to, the accuracy rate and the quality of the answers. The plan providing unit, for example, suggests study schedules and study materials. For example, the plan providing unit creates an optimal study schedule tailored to the exam year, taking into account the student's goals and progress. The plan providing unit can also analyze the student's learning data using a generation AI to provide an optimal exam plan. Furthermore, the plan providing unit can monitor the student's learning progress in real time and adjust the plan as needed. For example, the plan providing unit can input the answer results into the generation AI and have the generation AI provide an exam plan. By providing an exam plan based on the answer results, an optimal study schedule can be proposed for the student. Some or all of the above-described processing in the plan providing unit can be performed, for example, using the generation AI, or without using the generation AI.

[0034] The plan providing unit can create a study schedule tailored to the exam year. The exam year includes, but is not limited to, a specific school year or the year of the exam to be taken. The plan providing unit, for example, considers the student's goals and progress to create an optimal study schedule tailored to the exam year. The plan providing unit can also use a generation AI to analyze the student's learning data and provide an optimal exam plan. Furthermore, the plan providing unit can monitor the student's learning progress in real time and adjust the plan as needed. For example, the plan providing unit can input information about the exam year into the generation AI and have the generation AI create a study schedule. This can maximize the student's learning outcomes by creating an optimal study schedule tailored to the exam year. Some or all of the above-described processing in the plan providing unit can be performed, for example, using the generation AI, or without using the generation AI.

[0035] The collection unit can analyze the student's past learning history and select a data collection method. For example, the collection unit can collect data by prioritizing learning methods that were effective for the student in the past. The collection unit can also collect data by focusing on areas in which the student was weak in the past. Furthermore, the collection unit can collect data from the student's learning history at the most efficient time period. This allows the optimal data collection method to be selected by analyzing the student's past learning history. Past learning history includes, for example, past test results and records of study time, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the student's past learning history data into the generation AI and have the generation AI select the optimal data collection method.

[0036] When collecting learning data, the collection unit can filter the data based on the student's current learning environment and areas of interest. For example, if the student is studying in a quiet environment, the collection unit can collect data to improve concentration. The collection unit can also prioritize collecting data related to the student's areas of interest. Furthermore, the collection unit can collect data optimized for the device the student is using. This allows for more relevant data to be collected by filtering the data based on the student's current learning environment and areas of interest. Examples of the learning environment include, but are not limited to, the learning location and the device used. Examples of areas of interest include, but are not limited to, past learning content and hobbies. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the student's learning environment data and area of ​​interest data into the generation AI and have the generation AI filter the data.

[0037] When collecting learning data, the collection unit can prioritize collecting highly relevant data based on the student's geographical location information. For example, if the student lives in a particular area, the collection unit prioritizes collecting data related to that area. Furthermore, if the student is traveling, the collection unit can also collect data related to the student's travel destination. Furthermore, if the student is at school, the collection unit can also collect data related to the school's curriculum. This allows for more relevant data to be collected by taking the student's geographical location information into consideration. Examples of geographical location information include, but are not limited to, GPS data and address information. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the student's geographical location information into the generation AI and cause the generation AI to collect highly relevant data.

[0038] The collection unit can analyze students' social media activities and collect relevant data when collecting learning data. For example, the collection unit can collect data related to topics that students are interested in on social media. The collection unit can also collect useful data from education-related accounts that students follow. Furthermore, the collection unit can collect data based on the activities of online communities in which students participate. This allows for more relevant data to be collected by analyzing students' social media activities. Social media activities include, but are not limited to, the content of posts and the number of likes. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input students' social media activity data into the generation AI and have the generation AI collect relevant data.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the learning data. For example, the analysis unit can perform a detailed analysis of important data and provide detailed feedback. The analysis unit can also perform a concise analysis of less important data and provide feedback that focuses on the key points. Furthermore, the analysis unit can prioritize analysis of more important data based on the student's learning goals. This enables more effective analysis by adjusting the level of detail of the analysis based on the importance of the learning data. Examples of the importance of learning data include, but are not limited to, test scores and study time. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the learning data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the training data. For example, the analysis unit can apply a mathematical expression analysis algorithm to mathematics data. The analysis unit can also apply a natural language processing algorithm to language learning data. Furthermore, the analysis unit can apply an experimental result analysis algorithm to science data. This enables more appropriate analysis by applying different analysis algorithms depending on the category of the training data. Categories of training data include, but are not limited to, by subject or difficulty level. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the category of the training data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0041] During analysis, the analysis unit can determine the analysis priority based on the submission time of the learning data. For example, the analysis unit prioritizes analysis of data with an upcoming deadline. The analysis unit can also prioritize analysis of data with an early submission time. Furthermore, the analysis unit can set priorities according to the submission time based on the student's learning schedule. This enables more effective analysis by determining the analysis priority based on the submission time of the learning data. The submission time includes, but is not limited to, for example, a submission deadline or a submission date. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the submission time of the learning data to the generation AI and have the generation AI determine the analysis priority.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the learning data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can prioritize analysis of highly relevant data based on the student's learning goals. This enables more effective analysis by adjusting the order of analysis based on the relevance of the learning data. Relevance of the learning data includes, but is not limited to, similarity in content and related topics. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the relevance of the learning data into the generation AI and have the generation AI adjust the order of analysis.

[0043] The answering unit can adjust the level of detail of the answer based on the importance of the question when answering. For example, the answering unit can provide a detailed answer to an important question. The answering unit can also provide a concise answer to a less important question. Furthermore, the answering unit can prioritize answers to questions of higher importance based on the student's learning goals. This allows for a more appropriate answer by adjusting the level of detail of the answer based on the importance of the question. The importance of a question includes, for example, the frequency of the question and the content of the question, but is not limited to such examples. Some or all of the above-described processing in the answering unit may be performed using, or without, a generation AI. For example, the answering unit can input the importance of the question to the generation AI and have the generation AI adjust the level of detail of the answer.

[0044] The answering unit can apply different answering algorithms depending on the question category when answering. For example, the answering unit can apply a mathematical expression analysis algorithm to a mathematics question. The answering unit can also apply a natural language processing algorithm to a language learning question. Furthermore, the answering unit can apply an experimental result analysis algorithm to a science question. This allows for more appropriate answers by applying different answering algorithms depending on the question category. Question categories include, for example, by subject or by difficulty level, but are not limited to these examples. Some or all of the above-mentioned processing in the answering unit may be performed using, or without, a generation AI. For example, the answering unit can input the question category into the generation AI and cause the generation AI to apply different answering algorithms.

[0045] When answering a question, the answering unit can determine the priority of the answers based on the time of submission of the question. For example, the answering unit prioritizes answers to questions with an approaching deadline. The answering unit can also prioritize answers to questions with an earlier submission time. Furthermore, the answering unit can set priorities according to the submission time based on the student's study schedule. This enables more effective answers by determining the priority of answers based on the time of submission of the question. The submission time includes, but is not limited to, the submission deadline and the submission date. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the answering unit can input the time of submission of the question to the generation AI and have the generation AI determine the priority of the answers.

[0046] The answering unit can adjust the order of answers based on the relevance of the questions when answering. For example, the answering unit prioritizes answers to highly relevant questions. The answering unit can also postpone answers to less relevant questions. Furthermore, the answering unit can prioritize answers to highly relevant questions based on the student's learning goals. This enables more effective answers by adjusting the order of answers based on the relevance of the questions. The relevance of questions includes, for example, similarity in content and related topics, but is not limited to such examples. Some or all of the above-mentioned processing in the answering unit may be performed using, or without, a generation AI. For example, the answering unit can input the relevance of questions to the generation AI and have the generation AI adjust the order of the answers.

[0047] When providing a plan, the plan providing unit can select a plan by analyzing the student's past learning progress. For example, the plan providing unit can provide a plan by prioritizing learning methods that were effective for the student in the past. The plan providing unit can also provide a plan that focuses on areas in which the student was weak in the past. Furthermore, the plan providing unit can provide a plan for the most efficient time period based on the student's learning history. This allows the optimal plan to be selected by analyzing the student's past learning progress. Past learning progress includes, for example, past test results and records of study time, but is not limited to such examples. Some or all of the above-mentioned processing in the plan providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the plan providing unit can input the student's past learning progress data into the generation AI and have the generation AI select the optimal plan.

[0048] When providing a plan, the plan providing unit can customize the plan based on the student's current learning situation. For example, the plan providing unit customizes the plan based on the student's current learning content. The plan providing unit can also adjust the plan based on the student's current level of understanding. Furthermore, the plan providing unit can provide a plan optimized for the student's current learning environment. This enables providing a more appropriate plan by customizing the plan based on the student's current learning situation. The current learning situation includes, but is not limited to, for example, the student's current learning progress and learning environment. Some or all of the above-described processing in the plan providing unit may be performed using, or without, a generation AI. For example, the plan providing unit can input data on the student's current learning situation into the generation AI and have the generation AI customize the plan.

[0049] When providing a plan, the plan providing unit can select a plan based on the student's geographical location information. For example, if the student lives in a specific area, the plan providing unit can prioritize plans related to that area. Furthermore, if the student is traveling, the plan providing unit can provide plans related to the student's travel destination. Furthermore, if the student is at school, the plan providing unit can provide plans related to the school's curriculum. This allows for providing more relevant plans by taking the student's geographical location information into consideration. Examples of geographical location information include, but are not limited to, GPS data and address information. Some or all of the above-described processing in the plan providing unit may be performed using, or without, a generation AI. For example, the plan providing unit can input the student's geographical location information into the generation AI and have the generation AI select an optimal plan.

[0050] The plan provider can analyze students' social media activity and suggest plan methods when providing a plan. For example, the plan provider can provide plans related to topics that students have shown interest in on social media. It can also provide useful plans from educational accounts that students follow. Furthermore, the plan provider can provide plans based on the activities of online communities that students participate in. This allows for the provision of more relevant plans by analyzing students' social media activity. Social media activity includes, but is not limited to, the content of posts and the number of likes. Some or all of the above processing in the plan provider may be performed using, for example, generative AI, or not using generative AI. For example, the plan provider can input students' social media activity data into a generative AI and have the generative AI suggest plan methods.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The analysis unit can evaluate the reliability of the data when analyzing the training data and prioritize the analysis of highly reliable data. For example, the analysis unit can evaluate reliability based on the data source and acquisition method. The analysis unit can also check the consistency and integrity of the data and exclude unreliable data. Furthermore, the analysis unit can detect outliers by comparing them with past data and filter out unreliable data. This allows for more accurate analysis results by prioritizing the analysis of highly reliable data. Reliability evaluation includes, but is not limited to, the reliability of the data source, data consistency, and comparison with past data. Some or all of the above processing in the analysis unit may be performed using generative AI or not.

[0053] When collecting student learning data, the collection unit can adjust the collection method taking into consideration data privacy protection. For example, the collection unit anonymizes the data and collects it in a form that does not identify individuals. The collection unit can also encrypt the data to prevent unauthorized access by third parties. Furthermore, the collection unit can introduce a process to obtain students' consent when collecting data, thereby thoroughly protecting privacy. This makes it possible to collect learning data while protecting students' privacy. Privacy protection methods include, for example, anonymizing data, encrypting data, and obtaining consent. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI.

[0054] When analyzing learning data, the analysis unit can select an analysis algorithm taking into account the diversity of the data. For example, the analysis unit can combine multiple analysis algorithms to analyze data from students with different learning styles and backgrounds. The analysis unit can also customize the analysis results according to the diversity of the data and provide individualized feedback. Furthermore, the analysis unit can propose a learning plan based on the analysis results that reflect the diversity of the data. This enables analysis that takes into account the diversity of the data and provides more appropriate feedback. Examples of the diversity of the data include, but are not limited to, learning styles, backgrounds, and interests. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the use of a generation AI.

[0055] When answering a student's question, the answering unit can evaluate the reliability of the answer and provide highly reliable answers preferentially. For example, the answering unit can clearly indicate the source and rationale of the answer and evaluate its reliability. The answering unit can also check consistency by comparing it with past answer history and exclude answers with low reliability. Furthermore, the answering unit can also provide answers that have been supervised by an expert preferentially. This allows students to deepen their understanding by providing highly reliable answers. Examples of the evaluation of reliability include, but are not limited to, the source, rationale, consistency, and expert supervision of the answer. Some or all of the above-mentioned processing in the answering unit may be performed using a generation AI or may be performed without using a generation AI.

[0056] When providing an exam plan, the plan providing unit can customize the plan based on the student's learning style. For example, the plan providing unit can provide a plan that makes extensive use of diagrams and graphs to a student who prefers visual learning. The plan providing unit can also provide a plan that utilizes audio and video to a student who prefers auditory learning. Furthermore, the plan providing unit can provide a plan that emphasizes experiments and exercises to a student who prefers practical learning. This maximizes learning effectiveness by providing a plan tailored to the student's learning style. Examples of learning styles include, but are not limited to, visual, auditory, and practical. Some or all of the above-described processing in the plan providing unit may be performed using or without a generation AI.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection unit collects student learning data. This learning data includes text data, numerical data, image data, etc. The collection unit collects learning data entered by students into the online platform, data automatically acquired from learning applications, and learning environment data collected using sensors in real time. Step 2: The analysis unit analyzes the data collected by the collection unit and analyzes learning progress. The analysis unit analyzes the data using statistical analysis and machine learning algorithms to evaluate students' learning progress and level of understanding. It can also analyze trends in learning data and evaluate the effectiveness of learning. It can also analyze data in real time using generative AI. Step 3: The answering unit answers the student's question based on the results of the analysis by the analysis unit. The answering unit provides FAQ-based answers or automated answers generated by generative AI, instantly answering the student's question. It can also evaluate the student's level of understanding based on the analysis results and provide appropriate guidance. Step 4: The plan provider provides an exam plan based on the answers provided by the answer provider. The plan provider proposes study schedules and study materials, and creates an optimal study schedule taking into account the student's goals and progress. In addition, the generator AI can analyze the student's learning data and provide an optimal exam plan. The plan provider monitors the student's learning progress in real time and adjusts the plan as necessary.

[0059] (Example 2) An interactive online education system according to an embodiment of the present invention utilizes a generative AI to solve various problems associated with traditional cram schools and online education. This interactive online education system provides three types of interactive support, enabling maximum results with minimal effort. First, it provides an efficient and effective learning method. The generative AI analyzes each student's learning progress and level of understanding in real time and proposes an optimal learning plan. Second, it provides thorough instruction from a teacher's perspective. The generative AI immediately answers students' questions and provides detailed explanations. Finally, it provides an exam plan tailored to each exam year. The generative AI considers each student's goals and progress to create an optimal study schedule tailored to each exam year. For example, the generative AI collects students' learning data in real time and analyzes their learning progress. Next, the generative AI answers students' questions based on the analysis results and provides detailed explanations. Finally, the generative AI provides an exam plan based on the answers and creates an optimal study schedule tailored to each exam year. In this way, an interactive online education system using generative AI maximizes students' learning outcomes by providing efficient and effective learning methods, thorough instruction from a teacher's perspective, and exam plans tailored to each exam year. This allows the interactive online education system to collect students' learning data in real time, analyze their learning progress, answer questions, and provide exam plans.

[0060] The interactive online education system according to this embodiment comprises a data collection unit, an analysis unit, an answering unit, and a plan provision unit. The data collection unit collects student learning data. Student learning data includes, but is not limited to, text data, numerical data, and image data. The data collection unit collects, for example, learning data entered by students into an online platform. The data collection unit can also automatically acquire data from learning applications. Furthermore, the data collection unit can collect student learning environment data using sensors. For example, the data collection unit collects data from learning applications in real time to understand learning progress. The analysis unit analyzes the data collected by the data collection unit to analyze learning progress. The analysis unit analyzes the data using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit evaluates learning progress and measures comprehension based on student learning data. The analysis unit can also analyze trends in learning data and evaluate the effectiveness of learning. Furthermore, the analysis unit can analyze data using generative AI to understand learning progress in real time. The answering unit answers student questions based on the results analyzed by the analysis unit. The answering unit provides, for example, FAQ-based answers or automated answers generated by AI. For instance, the answering unit instantly answers students' questions and provides detailed explanations. It can also use AI to generate the optimal answer to a student's question. Furthermore, the answering unit can evaluate the student's level of understanding based on the analysis results and provide appropriate guidance. The plan provisioning unit provides an examination plan based on the answers provided by the answering unit. For example, the plan provisioning unit suggests learning schedules and materials. For instance, it considers the student's goals and progress and creates an optimal learning schedule tailored to the examination year. It can also use AI to analyze the student's learning data and provide an optimal examination plan. Furthermore, the plan provisioning unit can monitor the student's learning progress in real time and adjust the plan as needed. Thus, the interactive online education system according to this embodiment can collect and analyze student learning data, answer questions, and provide examination plans.

[0061] The collection unit can collect student learning data in real time. Real-time includes, but is not limited to, the frequency and delay of data collection. For example, the collection unit collects learning data entered by students into an online platform in real time. The collection unit can also automatically acquire data from a learning application. For example, the collection unit collects data from the learning application in real time to understand the student's learning progress. The collection unit can also collect student learning environment data in real time using sensors. For example, the collection unit collects data such as the temperature and illuminance of the learning environment in real time to help optimize the learning environment. This allows the student's learning data to be collected in real time to understand their latest learning situation. Some or all of the above-described processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can input data from the learning application into the generation AI and have the generation AI perform real-time data collection.

[0062] The analysis unit can analyze learning progress based on the collected data. Learning progress includes, but is not limited to, test results and study time. The analysis unit analyzes the data using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit evaluates learning progress and measures understanding based on the student's learning data. The analysis unit can also analyze trends in the learning data and evaluate the effectiveness of learning. Furthermore, the analysis unit can analyze data using a generation AI to grasp learning progress in real time. For example, the analysis unit can input learning data into the generation AI and have the generation AI analyze the learning progress. In this way, by analyzing learning progress based on the collected data, the student's learning situation can be accurately grasped. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI.

[0063] The answering unit can answer the student's question based on the analysis results. The analysis results include, but are not limited to, numerical data and graph displays. The answering unit can provide, for example, FAQ-based answers or automatic answers using a generation AI. For example, the answering unit can instantly answer the student's question and provide detailed explanations. The answering unit can also generate optimal answers to the student's questions using the generation AI. Furthermore, the answering unit can evaluate the student's understanding based on the analysis results and provide appropriate guidance. For example, the answering unit can input the analysis results to the generation AI and have the generation AI execute an answer to the student's question. This enables appropriate guidance by answering the student's question based on the analysis results. Some or all of the above-mentioned processing in the answering unit can be performed, for example, using the generation AI, or without using the generation AI.

[0064] The plan providing unit can provide an exam plan based on the answer results. The answer results include, but are not limited to, the accuracy rate and the quality of the answers. The plan providing unit, for example, suggests study schedules and study materials. For example, the plan providing unit creates an optimal study schedule tailored to the exam year, taking into account the student's goals and progress. The plan providing unit can also analyze the student's learning data using a generation AI to provide an optimal exam plan. Furthermore, the plan providing unit can monitor the student's learning progress in real time and adjust the plan as needed. For example, the plan providing unit can input the answer results into the generation AI and have the generation AI provide an exam plan. By providing an exam plan based on the answer results, an optimal study schedule can be proposed for the student. Some or all of the above-described processing in the plan providing unit can be performed, for example, using the generation AI, or without using the generation AI.

[0065] The plan providing unit can create a study schedule tailored to the exam year. The exam year includes, but is not limited to, a specific school year or the year of the exam to be taken. The plan providing unit, for example, considers the student's goals and progress to create an optimal study schedule tailored to the exam year. The plan providing unit can also use a generation AI to analyze the student's learning data and provide an optimal exam plan. Furthermore, the plan providing unit can monitor the student's learning progress in real time and adjust the plan as needed. For example, the plan providing unit can input information about the exam year into the generation AI and have the generation AI create a study schedule. This can maximize the student's learning outcomes by creating an optimal study schedule tailored to the exam year. Some or all of the above-described processing in the plan providing unit can be performed, for example, using the generation AI, or without using the generation AI.

[0066] The collection unit can estimate the student's emotions and adjust the timing of learning data collection based on the estimated student's emotions. For example, if the student is feeling stressed, the collection unit can delay the collection timing and collect data when the student is relaxed. Furthermore, if the student is concentrating, the collection unit can collect data immediately to prevent interruption of the student's learning flow. Furthermore, if the student is tired, the collection unit can adjust the data collection timing to after a break. This allows for more appropriate data collection by adjusting the timing of learning data collection according to the student's emotions. Emotions can be estimated using, for example, facial expression recognition or questionnaire results. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the student's emotion data into the generation AI and have the generation AI adjust the collection timing.

[0067] The collection unit can analyze the student's past learning history and select a data collection method. For example, the collection unit can collect data by prioritizing learning methods that were effective for the student in the past. The collection unit can also collect data by focusing on areas in which the student was weak in the past. Furthermore, the collection unit can collect data from the student's learning history at the most efficient time period. This allows the optimal data collection method to be selected by analyzing the student's past learning history. Past learning history includes, for example, past test results and records of study time, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the student's past learning history data into the generation AI and have the generation AI select the optimal data collection method.

[0068] When collecting learning data, the collection unit can filter the data based on the student's current learning environment and areas of interest. For example, if the student is studying in a quiet environment, the collection unit can collect data to improve concentration. The collection unit can also prioritize collecting data related to the student's areas of interest. Furthermore, the collection unit can collect data optimized for the device the student is using. This allows for more relevant data to be collected by filtering the data based on the student's current learning environment and areas of interest. Examples of the learning environment include, but are not limited to, the learning location and the device used. Examples of areas of interest include, but are not limited to, past learning content and hobbies. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the student's learning environment data and area of ​​interest data into the generation AI and have the generation AI filter the data.

[0069] The collection unit can estimate the student's emotions and prioritize the data to be collected based on the estimated student's emotions. For example, if the student is feeling anxious, the collection unit can prioritize collecting data that gives the student a sense of security. Furthermore, if the student is excited, the collection unit can prioritize collecting data that piques the student's interest. Furthermore, if the student is relaxed, the collection unit can prioritize collecting data that promotes deeper understanding. This enables more effective data collection by prioritizing the data to be collected according to the student's emotions. Emotions are estimated using, for example, facial expression recognition or questionnaire results. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input the student's emotion data into the generation AI and have the generation AI determine the data priorities.

[0070] When collecting learning data, the collection unit can prioritize collecting highly relevant data based on the student's geographical location information. For example, if the student lives in a particular area, the collection unit prioritizes collecting data related to that area. Furthermore, if the student is traveling, the collection unit can also collect data related to the student's travel destination. Furthermore, if the student is at school, the collection unit can also collect data related to the school's curriculum. This allows for more relevant data to be collected by taking the student's geographical location information into consideration. Examples of geographical location information include, but are not limited to, GPS data and address information. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the student's geographical location information into the generation AI and cause the generation AI to collect highly relevant data.

[0071] The collection unit can analyze students' social media activities and collect relevant data when collecting learning data. For example, the collection unit can collect data related to topics that students are interested in on social media. The collection unit can also collect useful data from education-related accounts that students follow. Furthermore, the collection unit can collect data based on the activities of online communities in which students participate. This allows for more relevant data to be collected by analyzing students' social media activities. Social media activities include, but are not limited to, the content of posts and the number of likes. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input students' social media activity data into the generation AI and have the generation AI collect relevant data.

[0072] The analysis unit can estimate students' emotions and adjust the learning progress analysis method based on the estimated emotions. For example, if a student is stressed, the analysis unit can provide a concise summary of the analysis results. If a student is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if a student is focused, the analysis unit can perform a deeper analysis and provide detailed feedback. This allows for more appropriate analysis by adjusting the learning progress analysis method according to the student's emotions. Emotion estimation is performed using, for example, facial recognition or survey results. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input student emotion data into a generative AI and have the generative AI perform the adjustment of the analysis method.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the learning data. For example, the analysis unit can perform a detailed analysis of important data and provide detailed feedback. The analysis unit can also perform a concise analysis of less important data and provide feedback that focuses on the key points. Furthermore, the analysis unit can prioritize analysis of more important data based on the student's learning goals. This enables more effective analysis by adjusting the level of detail of the analysis based on the importance of the learning data. Examples of the importance of learning data include, but are not limited to, test scores and study time. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the learning data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the training data. For example, the analysis unit can apply a mathematical expression analysis algorithm to mathematics data. The analysis unit can also apply a natural language processing algorithm to language learning data. Furthermore, the analysis unit can apply an experimental result analysis algorithm to science data. This enables more appropriate analysis by applying different analysis algorithms depending on the category of the training data. Categories of training data include, but are not limited to, by subject or difficulty level. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the category of the training data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0075] The analysis unit can estimate the student's emotions and adjust the display method of the analysis results based on the estimated student's emotions. For example, if the student is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the student is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the student is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for more appropriate feedback by adjusting the display method of the analysis results according to the student's emotions. Emotions can be estimated using, for example, facial expression recognition or questionnaire results. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the student's emotion data into the generation AI and have the generation AI adjust the display method.

[0076] During analysis, the analysis unit can determine the analysis priority based on the submission time of the learning data. For example, the analysis unit prioritizes analysis of data with an upcoming deadline. The analysis unit can also prioritize analysis of data with an early submission time. Furthermore, the analysis unit can set priorities according to the submission time based on the student's learning schedule. This enables more effective analysis by determining the analysis priority based on the submission time of the learning data. The submission time includes, but is not limited to, for example, a submission deadline or a submission date. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the submission time of the learning data to the generation AI and have the generation AI determine the analysis priority.

[0077] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the learning data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can prioritize analysis of highly relevant data based on the student's learning goals. This enables more effective analysis by adjusting the order of analysis based on the relevance of the learning data. Relevance of the learning data includes, but is not limited to, similarity in content and related topics. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the relevance of the learning data into the generation AI and have the generation AI adjust the order of analysis.

[0078] The answering unit can estimate the student's emotions and adjust the way the answer is expressed based on the estimated student's emotions. For example, if the student is nervous, the answering unit can provide a simple, highly visible display method. Furthermore, if the student is relaxed, the answering unit can provide a display method that includes detailed information. Furthermore, if the student is in a hurry, the answering unit can provide a display method that focuses on the main points. This allows for more appropriate answers by adjusting the way the answer is expressed based on the student's emotions. Emotions can be estimated using, for example, facial expression recognition or questionnaire results. Some or all of the above-described processing in the answering unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the answering unit can input the student's emotion data into the generation AI and have the generation AI adjust the way the answer is expressed.

[0079] The answering unit can adjust the level of detail of the answer based on the importance of the question when answering. For example, the answering unit can provide a detailed answer to an important question. The answering unit can also provide a concise answer to a less important question. Furthermore, the answering unit can prioritize answers to questions of higher importance based on the student's learning goals. This allows for a more appropriate answer by adjusting the level of detail of the answer based on the importance of the question. The importance of a question includes, for example, the frequency of the question and the content of the question, but is not limited to such examples. Some or all of the above-described processing in the answering unit may be performed using, or without, a generation AI. For example, the answering unit can input the importance of the question to the generation AI and have the generation AI adjust the level of detail of the answer.

[0080] The answering unit can apply different answering algorithms depending on the question category when answering. For example, the answering unit can apply a mathematical expression analysis algorithm to a mathematics question. The answering unit can also apply a natural language processing algorithm to a language learning question. Furthermore, the answering unit can apply an experimental result analysis algorithm to a science question. This allows for more appropriate answers by applying different answering algorithms depending on the question category. Question categories include, for example, by subject or by difficulty level, but are not limited to these examples. Some or all of the above-mentioned processing in the answering unit may be performed using, or without, a generation AI. For example, the answering unit can input the question category into the generation AI and cause the generation AI to apply different answering algorithms.

[0081] The answering unit can estimate the student's emotions and adjust the length of the answer based on the estimated student's emotions. For example, if the student is in a hurry, the answering unit can provide a short, to-the-point answer. If the student is relaxed, the answering unit can provide a longer answer with detailed explanations. Furthermore, if the student is excited, the answering unit can provide an answer with visually stimulating effects. This allows for more appropriate answers by adjusting the length of the answer according to the student's emotions. Emotions can be estimated using, for example, facial expression recognition or questionnaire results. Some or all of the above-described processing in the answering unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the answering unit can input the student's emotion data into the generation AI and have the generation AI adjust the length of the answer.

[0082] When answering a question, the answering unit can determine the priority of the answers based on the time of submission of the question. For example, the answering unit prioritizes answers to questions with an approaching deadline. The answering unit can also prioritize answers to questions with an earlier submission time. Furthermore, the answering unit can set priorities according to the submission time based on the student's study schedule. This enables more effective answers by determining the priority of answers based on the time of submission of the question. The submission time includes, but is not limited to, the submission deadline and the submission date. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the answering unit can input the time of submission of the question to the generation AI and have the generation AI determine the priority of the answers.

[0083] The answering unit can adjust the order of answers based on the relevance of the questions when answering. For example, the answering unit prioritizes answers to highly relevant questions. The answering unit can also postpone answers to less relevant questions. Furthermore, the answering unit can prioritize answers to highly relevant questions based on the student's learning goals. This enables more effective answers by adjusting the order of answers based on the relevance of the questions. The relevance of questions includes, for example, similarity in content and related topics, but is not limited to such examples. Some or all of the above-mentioned processing in the answering unit may be performed using, or without, a generation AI. For example, the answering unit can input the relevance of questions to the generation AI and have the generation AI adjust the order of the answers.

[0084] The plan providing unit can estimate the student's emotions and adjust the method of providing the exam plan based on the estimated student's emotions. For example, if the student is nervous, the plan providing unit can provide a simple, highly visible plan. Furthermore, if the student is relaxed, the plan providing unit can provide a plan that includes detailed information. Furthermore, if the student is in a hurry, the plan providing unit can provide a plan that focuses on the main points. This allows for adjusting the method of providing the exam plan according to the student's emotions, thereby enabling more appropriate plan provision. Emotions are estimated using, for example, facial expression recognition or questionnaire results. Some or all of the above-mentioned processing in the plan providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the plan providing unit can input the student's emotion data into the generation AI and have the generation AI adjust the provision method.

[0085] When providing a plan, the plan providing unit can select a plan by analyzing the student's past learning progress. For example, the plan providing unit can provide a plan by prioritizing learning methods that were effective for the student in the past. The plan providing unit can also provide a plan that focuses on areas in which the student was weak in the past. Furthermore, the plan providing unit can provide a plan for the most efficient time period based on the student's learning history. This allows the optimal plan to be selected by analyzing the student's past learning progress. Past learning progress includes, for example, past test results and records of study time, but is not limited to such examples. Some or all of the above-mentioned processing in the plan providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the plan providing unit can input the student's past learning progress data into the generation AI and have the generation AI select the optimal plan.

[0086] When providing a plan, the plan providing unit can customize the plan based on the student's current learning situation. For example, the plan providing unit customizes the plan based on the student's current learning content. The plan providing unit can also adjust the plan based on the student's current level of understanding. Furthermore, the plan providing unit can provide a plan optimized for the student's current learning environment. This enables providing a more appropriate plan by customizing the plan based on the student's current learning situation. The current learning situation includes, but is not limited to, for example, the student's current learning progress and learning environment. Some or all of the above-described processing in the plan providing unit may be performed using, or without, a generation AI. For example, the plan providing unit can input data on the student's current learning situation into the generation AI and have the generation AI customize the plan.

[0087] The plan providing unit can estimate the student's emotions and prioritize plans based on the estimated student's emotions. For example, if the student is feeling anxious, the plan providing unit can prioritize plans that provide a sense of security. Furthermore, if the student is excited, the plan providing unit can prioritize plans that pique the student's interest. Furthermore, if the student is relaxed, the plan providing unit can prioritize plans that promote deep understanding. This enables more effective plan provision by prioritizing plans based on the student's emotions. Emotions are estimated using, for example, facial expression recognition or questionnaire results. Some or all of the above-described processing in the plan providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the plan providing unit can input student emotion data into the generation AI and have the generation AI determine the plan priorities.

[0088] When providing a plan, the plan providing unit can select a plan based on the student's geographical location information. For example, if the student lives in a specific area, the plan providing unit can prioritize plans related to that area. Furthermore, if the student is traveling, the plan providing unit can provide plans related to the student's travel destination. Furthermore, if the student is at school, the plan providing unit can provide plans related to the school's curriculum. This allows for providing more relevant plans by taking the student's geographical location information into consideration. Examples of geographical location information include, but are not limited to, GPS data and address information. Some or all of the above-described processing in the plan providing unit may be performed using, or without, a generation AI. For example, the plan providing unit can input the student's geographical location information into the generation AI and have the generation AI select an optimal plan.

[0089] The plan provider can analyze students' social media activity and suggest plan methods when providing a plan. For example, the plan provider can provide plans related to topics that students have shown interest in on social media. It can also provide useful plans from educational accounts that students follow. Furthermore, the plan provider can provide plans based on the activities of online communities that students participate in. This allows for the provision of more relevant plans by analyzing students' social media activity. Social media activity includes, but is not limited to, the content of posts and the number of likes. Some or all of the above processing in the plan provider may be performed using, for example, generative AI, or not using generative AI. For example, the plan provider can input students' social media activity data into a generative AI and have the generative AI suggest plan methods. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, response unit, and plan providing unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 and collects the student's learning data in real time. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data and evaluates the student's learning progress. The response unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and answers the student's questions based on the analysis results. The plan providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides an exam plan. === Hard Collateral 1-2 === Each of the multiple elements described above, including the data collection unit, analysis unit, answering unit, and plan provisioning unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects student learning data in real time. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to evaluate learning progress. The answering unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and answers the student's questions based on the analysis results. The plan provisioning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides an examination plan. === Hard Collateral 1-3 === Each of the multiple elements described above, including the data collection unit, analysis unit, answering unit, and plan provisioning unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and collects student learning data in real time. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to evaluate learning progress. The answering unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and answers the student's questions based on the analysis results. The plan provisioning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides an examination plan. === Hard Collateral 1-4 === Each of the multiple elements described above, including the data collection unit, analysis unit, answering unit, and plan provisioning unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and collects student learning data in real time. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data and evaluates learning progress. The answering unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and answers the student's questions based on the analysis results. The plan provisioning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides an examination plan.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The analysis unit can evaluate the reliability of the data when analyzing the training data and prioritize the analysis of highly reliable data. For example, the analysis unit can evaluate reliability based on the data source and acquisition method. The analysis unit can also check the consistency and integrity of the data and exclude unreliable data. Furthermore, the analysis unit can detect outliers by comparing them with past data and filter out unreliable data. This allows for more accurate analysis results by prioritizing the analysis of highly reliable data. Reliability evaluation includes, but is not limited to, the reliability of the data source, data consistency, and comparison with past data. Some or all of the above processing in the analysis unit may be performed using generative AI or not.

[0092] When collecting student learning data, the collection unit can adjust the collection method taking into consideration data privacy protection. For example, the collection unit anonymizes the data and collects it in a form that does not identify individuals. The collection unit can also encrypt the data to prevent unauthorized access by third parties. Furthermore, the collection unit can introduce a process to obtain students' consent when collecting data, thereby thoroughly protecting privacy. This makes it possible to collect learning data while protecting students' privacy. Privacy protection methods include, for example, anonymizing data, encrypting data, and obtaining consent. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI.

[0093] When analyzing learning data, the analysis unit can select an analysis algorithm taking into account the diversity of the data. For example, the analysis unit can combine multiple analysis algorithms to analyze data from students with different learning styles and backgrounds. The analysis unit can also customize the analysis results according to the diversity of the data and provide individualized feedback. Furthermore, the analysis unit can propose a learning plan based on the analysis results that reflect the diversity of the data. This enables analysis that takes into account the diversity of the data and provides more appropriate feedback. Examples of the diversity of the data include, but are not limited to, learning styles, backgrounds, and interests. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the use of a generation AI.

[0094] When answering a student's question, the answering unit can evaluate the reliability of the answer and provide highly reliable answers preferentially. For example, the answering unit can clearly indicate the source and rationale of the answer and evaluate its reliability. The answering unit can also check consistency by comparing it with past answer history and exclude answers with low reliability. Furthermore, the answering unit can also provide answers that have been supervised by an expert preferentially. This allows students to deepen their understanding by providing highly reliable answers. Examples of the evaluation of reliability include, but are not limited to, the source, rationale, consistency, and expert supervision of the answer. Some or all of the above-mentioned processing in the answering unit may be performed using a generation AI or may be performed without using a generation AI.

[0095] When providing an exam plan, the plan providing unit can customize the plan based on the student's learning style. For example, the plan providing unit can provide a plan that makes extensive use of diagrams and graphs to a student who prefers visual learning. The plan providing unit can also provide a plan that utilizes audio and video to a student who prefers auditory learning. Furthermore, the plan providing unit can provide a plan that emphasizes experiments and exercises to a student who prefers practical learning. This maximizes learning effectiveness by providing a plan tailored to the student's learning style. Examples of learning styles include, but are not limited to, visual, auditory, and practical. Some or all of the above-described processing in the plan providing unit may be performed using or without a generation AI.

[0096] The data collection unit can estimate students' emotions and adjust the method of collecting learning data based on the estimated emotions. For example, if a student is feeling stressed, the data collection unit will collect data in a relaxing environment. If a student is concentrating, the data collection unit can also collect data for a short period of time to maintain their concentration. Furthermore, if a student is tired, the data collection unit can adjust the method of collecting data to collect it after a break. This allows for more appropriate data collection by adjusting the method of collecting learning data according to students' emotions. Emotion estimation is performed using methods such as facial recognition or survey results. Some or all of the above-described processes in the data collection unit may be performed using generative AI, or they may be performed without using generative AI.

[0097] The analysis unit can estimate the student's emotions and adjust the feedback method of the analysis results based on the estimated emotions. For example, if the student is nervous, the analysis unit can provide simple and easy-to-understand feedback. If the student is relaxed, the analysis unit can also provide detailed feedback. Furthermore, if the student is in a hurry, the analysis unit can provide concise feedback. By adjusting the feedback method of the analysis results according to the student's emotions, more appropriate feedback becomes possible. Emotion estimation is performed using, for example, facial recognition or survey results. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI.

[0098] The response unit can estimate the student's emotions and adjust the tone of its response based on those emotions. For example, if the student is feeling anxious, the response unit will respond in a reassuring, gentle tone. If the student is excited, the response unit can respond in an engaging, lively tone. Furthermore, if the student is relaxed, the response unit can respond in a calm tone that includes detailed explanations. This allows for more effective communication by adjusting the tone of response according to the student's emotions. Emotion estimation is performed using, for example, facial recognition or survey results. Some or all of the above processing in the response unit may be performed using generative AI, or it may be performed without generative AI.

[0099] The plan providing unit can estimate the student's emotions and adjust the content of the study plan based on the estimated student's emotions. For example, if the student is feeling stressed, the plan providing unit can provide a plan including content that will help the student relax. Furthermore, if the student is excited, the plan providing unit can provide a plan including content that will pique the student's interest. Furthermore, if the student is relaxed, the plan providing unit can provide a plan including content that will encourage deeper understanding. This allows for more effective learning by adjusting the content of the study plan according to the student's emotions. Emotions are estimated using, for example, facial expression recognition or questionnaire results. Some or all of the above-mentioned processing in the plan providing unit may be performed using a generation AI, or may be performed without using a generation AI.

[0100] The plan providing unit can estimate the student's emotions and adjust the progress speed of the learning plan based on the estimated student's emotions. For example, if the student is feeling stressed, the plan providing unit can slow down the progress speed to allow the student to study at a relaxed pace. Furthermore, if the student is concentrating, the plan providing unit can speed up the progress speed to allow the student to study efficiently. Furthermore, if the student is tired, the plan providing unit can adjust the progress speed and allow the student to study while taking breaks. This allows for more effective learning by adjusting the progress speed of the learning plan according to the student's emotions. Emotions can be estimated using, for example, facial expression recognition or questionnaire results. Some or all of the above-described processing in the plan providing unit may be performed using a generation AI, or may be performed without using a generation AI.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The collection unit collects student learning data. This learning data includes text data, numerical data, image data, etc. The collection unit collects learning data entered by students into the online platform, data automatically acquired from learning applications, and learning environment data collected using sensors in real time. Step 2: The analysis unit analyzes the data collected by the collection unit and analyzes learning progress. The analysis unit analyzes the data using statistical analysis and machine learning algorithms to evaluate students' learning progress and level of understanding. It can also analyze trends in learning data and evaluate the effectiveness of learning. It can also analyze data in real time using generative AI. Step 3: The answering unit answers the student's question based on the results of the analysis by the analysis unit. The answering unit provides FAQ-based answers or automated answers generated by generative AI, instantly answering the student's question. It can also evaluate the student's level of understanding based on the analysis results and provide appropriate guidance. Step 4: The plan provider provides an exam plan based on the answers provided by the answer provider. The plan provider proposes study schedules and study materials, and creates an optimal study schedule taking into account the student's goals and progress. In addition, the generator AI can analyze the student's learning data and provide an optimal exam plan. The plan provider monitors the student's learning progress in real time and adjusts the plan as necessary.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

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

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 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.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the 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.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0137] 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.

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

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The 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.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 analyzes the learning progress; an answering unit that answers questions based on the results of the analysis by the analysis unit; a plan providing unit that provides an examination plan based on the answer provided by the answering unit; Equipped with A system characterized by:

2. The collecting unit Collect student learning data in real time 2. The system of claim 1.

3. The analysis unit Analyzing learning progress based on collected data 2. The system of claim 1.

4. The answering section Answer student questions based on the analysis results 2. The system of claim 1.

5. The plan providing unit Providing exam plans based on the answers 2. The system of claim 1.

6. The plan providing unit Create a study schedule for your exam year 2. The system of claim 1.

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

2. The system of claim 1.

8. The collecting unit Analyze students' past learning history and select data collection methods 2. The system of claim 1.

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