System

Abstract

The system according to the embodiment aims to analyze a learning progress and a comprehension degree of a user and provide a personalized learning experience.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a tracking unit. The collection unit collects a search history or a quiz result of the user. The analysis unit analyzes a learning progress and a comprehension level of the user based on the information collected by the collection unit. The generation unit generates a personalized quiz based on the information obtained by the analysis unit. The tracking unit tracks the result of the quiz generated by the generation unit and visualizes the learning progress.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not effectively grasp a user's learning progress and level of understanding, and do not adequately provide a personalized learning experience, so there is room for improvement.

[0005] The system according to the embodiment aims to analyze a user's learning progress and level of understanding and provide a personalized learning experience. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a tracking unit. The collection unit collects a user's search history or quiz results. The analysis unit analyzes the user's learning progress and level of understanding based on the information collected by the collection unit. The generation unit generates personalized quizzes based on the information obtained by the analysis unit. The tracking unit tracks the results of the quizzes generated by the generation unit and visualizes the learning progress. [Effects of the Invention]

[0007] The system according to the embodiment can analyze a user's learning progress and level of understanding, and provide a personalized learning experience. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A learning assistance system according to an embodiment of the present invention allows users to effectively reflect on their own learning and provides a personalized learning experience. The learning assistance system collects a user's search history and quiz results to understand the user's learning progress and level of understanding. Based on this information, the learning assistance system generates personalized quizzes and tracks the quiz results to visualize the user's learning progress. For example, when a user begins learning, the learning assistance system collects the user's search history and quiz results. For example, the system collects data on the user's search history for a specific topic and data on the quiz questions answered correctly and incorrectly. This information is used to understand the user's learning progress and level of understanding. Next, the learning assistance system generates personalized quizzes based on the collected information. For example, generating a quiz that includes many questions on topics the user struggles with can improve the user's level of understanding. Also, including questions on topics the user excels at can help maintain a balanced learning experience. Furthermore, the learning assistance system tracks the quiz results and visualizes the user's learning progress. For example, the system displays the number of questions the user answered correctly in the quiz and changes in the user's level of understanding for a specific topic in graphs and charts. This allows the user to grasp their learning status at a glance. Finally, the learning support system can adjust the user's study plan based on their learning progress. For example, if a user lacks understanding of a particular topic, a study plan can be created that focuses on that topic. Furthermore, for topics in which the user excels, the system can dig deeper to acquire more advanced knowledge. This allows the learning support system to effectively reflect on the user's self-study and provide a personalized learning experience. This allows the learning support system to grasp the user's learning status and generate and track appropriate quizzes, allowing the user to study efficiently.

[0029] A learning assistance system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a tracking unit. The collection unit collects a user's search history or quiz results. For example, the collection unit can collect a user's search history for a specific topic and data on quiz questions answered correctly and incorrectly. The collection unit can also collect information necessary to understand a user's learning progress and level of understanding. The analysis unit analyzes the user's learning progress and level of understanding based on the information collected by the collection unit. For example, the analysis unit can analyze the user's search history and quiz results to evaluate the user's learning progress and level of understanding. The analysis unit can also analyze the user's learning patterns to evaluate the effectiveness of the learning. The generation unit generates personalized quizzes based on the information obtained by the analysis unit. For example, the generation unit can generate quizzes that include many questions on topics that the user is weak at. The generation unit can also maintain a balanced learning experience by including questions on topics that the user is good at. The tracking unit tracks the results of the quizzes generated by the generation unit and visualizes the user's learning progress. For example, the tracking unit can display the number of questions the user answered correctly in a quiz or changes in the user's level of understanding of a particular topic in a graph or chart. The tracking unit can also monitor the user's learning progress in real time and evaluate the effectiveness of the learning. As a result, the learning assistance system according to the embodiment can grasp the user's learning progress and level of understanding, generate personalized quizzes, and visualize the user's learning progress, allowing the user to effectively reflect on their own learning.

[0030] The collection unit can analyze the user's past learning history and select an appropriate collection method. For example, the collection unit can prioritize collecting data from devices and apps that the user has frequently used in the past. The collection unit can also analyze the user's learning patterns and collect data at the optimal timing. The collection unit can also collect data during specific time periods from the user's past learning history. This allows the user's past learning history to be analyzed to select the optimal collection method and collect data efficiently. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past learning history data into the generation AI and have the generation AI select the optimal collection method.

[0031] When collecting search history and quiz results, the collection unit can filter the data based on the user's current learning goals and areas of interest. For example, the collection unit collects only data related to the user's current learning goals. The collection unit can also prioritize collection of highly relevant data based on the user's areas of interest. The collection unit can also filter unnecessary data based on the user's learning goals and areas of interest. This allows for efficient collection of highly relevant data by filtering data based on the user's learning goals and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's learning goal data to a generation AI and have the generation AI perform filtering.

[0032] When collecting search history and quiz results, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user is using voice input, the collection unit can prioritize collecting voice data. Also, if the user is using text input, the collection unit can prioritize collecting text data. Also, if the user is using image input, the collection unit can prioritize collecting image data. This allows efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and have the generation AI select the optimal collection means.

[0033] When collecting search history and quiz results, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, if the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, if the user is at home, the collection unit can prioritize collecting data related to the area around the user's home. This allows for efficient collection of highly relevant data by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location data to the generation AI and cause the generation AI to select highly relevant data.

[0034] The collection unit can analyze the user's social media activities and collect related data when collecting search history and quiz results. The collection unit can collect related data based on, for example, information shared by the user on social media. The collection unit can also analyze the user's social media activities and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. In this way, highly relevant data can be efficiently collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related data.

[0035] The collection unit can customize the collection method by reflecting the user's past feedback when collecting search history and quiz results. The collection unit adjusts the collection method based on, for example, feedback provided by the user in the past. The collection unit can also select the optimal collection means based on the user's past feedback. The collection unit can also adjust the collection timing by reflecting the user's feedback. In this way, by reflecting the user's past feedback, the optimal collection method can be selected and data can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into the generation AI and have the generation AI customize the collection method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the user's learning history. For example, if the user has an extensive learning history, the analysis unit performs a detailed analysis. Furthermore, if the user has a limited learning history, the analysis unit can also perform a simplified analysis. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the importance of the user's learning history. This allows for efficient analysis of learning progress and comprehension by adjusting the level of detail of the analysis based on the importance of the user's learning history. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's learning history data into a generation AI and have the generation AI adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the learning topic. For example, the analysis unit can apply a numerical analysis algorithm to science learning topics. The analysis unit can also apply a text analysis algorithm to humanities learning topics. The analysis unit can also select the optimal analysis algorithm depending on the category of the learning topic. This makes it possible to efficiently analyze learning progress and comprehension by applying the optimal analysis algorithm depending on the category of the learning topic. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input learning topic data to a generation AI and have the generation AI select the optimal analysis algorithm.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis from the user's past analysis results. The analysis unit can also optimize the analysis method by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can determine the analysis priority based on the submission time of the learning topic. For example, the analysis unit prioritizes analysis of learning topics with upcoming submission deadlines. The analysis unit can also postpone analysis of learning topics with distant submission deadlines. The analysis unit can also adjust the analysis priority based on the submission time of the learning topic. In this way, by determining the analysis priority based on the submission time of the learning topic, learning progress and comprehension level can be analyzed efficiently. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission time of the learning topic to the generation AI and have the generation AI determine the analysis priority.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the learning topics. For example, the analysis unit prioritizes analysis of highly relevant learning topics. The analysis unit can also postpone analysis of less relevant learning topics. The analysis unit can also adjust the order of analysis based on the relevance of the learning topics. In this way, by adjusting the order of analysis based on the relevance of the learning topics, it is possible to efficiently analyze learning progress and comprehension. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of learning topics to a generation AI and have the generation AI adjust the order of analysis.

[0041] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user's level of expertise is high, the analysis unit uses a lot of technical terms. Furthermore, if the user's level of expertise is low, the analysis unit can also avoid technical terms. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide results in a form that is easy for the user to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0042] When generating a quiz, the generation unit can adjust the level of detail of the quiz based on the importance of the learning topic. For example, the generation unit generates a detailed quiz for a learning topic with high importance. The generation unit can also generate a simplified quiz for a learning topic with low importance. The generation unit can also adjust the level of detail of the quiz based on the importance of the learning topic. This allows learning to proceed efficiently by adjusting the level of detail of the quiz based on the importance of the learning topic. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input importance data of the learning topic to the generation AI and cause the generation AI to adjust the level of detail of the quiz.

[0043] When generating a quiz, the generation unit can apply different quiz generation algorithms depending on the category of the learning topic. For example, the generation unit can apply a numerical analysis algorithm to a science learning topic. The generation unit can also apply a text analysis algorithm to a humanities learning topic. The generation unit can also select the optimal quiz generation algorithm depending on the category of the learning topic. This allows learning to proceed efficiently by applying the optimal quiz generation algorithm depending on the category of the learning topic. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input learning topic data into the generation AI and cause the generation AI to select the optimal quiz generation algorithm.

[0044] When generating a quiz, the generation unit can improve the accuracy of the quiz by referring to the user's past quiz results. The generation unit, for example, adjusts the quiz generation algorithm based on the user's past quiz results. The generation unit can also improve the accuracy of the quiz based on the user's past quiz results. The generation unit can also optimize the quiz generation method by referring to the user's past quiz results. This allows the accuracy of the quiz to be improved by referring to the user's past quiz results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past quiz result data into the generation AI and cause the generation AI to improve the accuracy of the quiz.

[0045] When generating quizzes, the generation unit can determine the priority of the quizzes based on the submission times of the learning topics. For example, the generation unit prioritizes generating quizzes related to learning topics with upcoming submission deadlines. The generation unit can also postpone quizzes related to learning topics with distant submission deadlines. The generation unit can also adjust the priority of the quizzes based on the submission times of the learning topics. This allows for efficient learning by determining the priority of the quizzes based on the submission times of the learning topics. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the submission times of the learning topics into the generation AI and have the generation AI determine the priority of the quizzes.

[0046] When generating quizzes, the generation unit can adjust the order of the quizzes based on the relevance of the learning topics. For example, the generation unit prioritizes generating quizzes related to highly relevant learning topics. The generation unit can also postpone quizzes related to less relevant learning topics. The generation unit can also adjust the order of the quizzes based on the relevance of the learning topics. This allows learning to proceed efficiently by adjusting the order of the quizzes based on the relevance of the learning topics. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input relevance data of the learning topics into the generation AI and cause the generation AI to adjust the order of the quizzes.

[0047] When generating a quiz, the generation unit can adjust the use of technical terminology in the quiz according to the user's level of expertise. For example, if the user's level of expertise is high, the generation unit can generate a quiz that uses a lot of technical terminology. Furthermore, if the user's level of expertise is low, the generation unit can also generate a quiz that avoids technical terminology. Furthermore, the generation unit can adjust the use of technical terminology in the quiz according to the user's level of expertise. This allows for an optimal learning experience for the user by adjusting the use of technical terminology in the quiz according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0048] During tracking, the tracking unit can predict current learning progress by referring to past tracking data. The tracking unit predicts current learning progress based on, for example, the user's past tracking data. The tracking unit can also analyze learning tendencies from the user's past tracking data and predict current progress. The tracking unit can also predict learning progress by referring to the user's past tracking data. In this way, current learning progress can be predicted by referring to the past tracking data. Some or all of the above-described processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input past tracking data into a generation AI and cause the generation AI to predict current learning progress.

[0049] The tracking unit can apply different tracking methods to each learning topic during tracking. For example, the tracking unit can apply a tracking method based on numerical data to science learning topics. The tracking unit can also apply a tracking method based on text data to humanities learning topics. The tracking unit can also select the optimal tracking method for each learning topic. This allows for efficient tracking of learning progress by applying the optimal tracking method for each learning topic. Some or all of the above-mentioned processing in the tracking unit can be performed using, for example, AI, or can be performed without using AI. For example, the tracking unit can input learning topic data into a generation AI and have the generation AI select the optimal tracking method.

[0050] The tracking unit can perform tracking while taking into account the user's attribute information. The tracking unit adjusts the tracking method according to, for example, the user's age and gender. The tracking unit can also adjust the tracking method according to the user's occupation and educational background. The tracking unit can also select the optimal tracking method by taking into account the user's attribute information. This makes it possible to select the optimal tracking method by taking into account the user's attribute information and efficiently track learning progress. Some or all of the above-described processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the user's attribute information data into the generation AI and cause the generation AI to adjust the tracking method.

[0051] During tracking, the tracking unit can analyze tracking changes based on the submission time of the learning topic. For example, the tracking unit prioritizes analysis of tracking results for learning topics with upcoming submission deadlines. The tracking unit can also postpone tracking results for learning topics with distant submission deadlines. The tracking unit can also analyze tracking changes based on the submission time of the learning topic. This makes it possible to efficiently track learning progress by analyzing tracking changes based on the submission time of the learning topic. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input data on the submission time of the learning topic into the generation AI and cause the generation AI to analyze tracking changes.

[0052] The tracking unit can analyze tracking by referring to market data related to the learning topic during tracking. The tracking unit, for example, analyzes tracking results based on market data related to the learning topic. The tracking unit can also analyze tracking results by referring to market trends of the learning topic. The tracking unit can also analyze tracking changes by referring to market data related to the learning topic. This allows learning progress to be tracked efficiently by referring to market data related to the learning topic. Some or all of the above-described processing in the tracking unit can be performed using, for example, AI, or can be performed without using AI. For example, the tracking unit can input related market data to a generation AI and have the generation AI perform tracking analysis.

[0053] The tracking unit can analyze tracking taking into account the technical maturity of the learning topic during tracking. For example, the tracking unit prioritizes analysis of tracking results for technically mature learning topics. The tracking unit can also postpone tracking results for technically immature learning topics. The tracking unit can also analyze tracking changes taking into account the technical maturity of the learning topic. This makes it possible to efficiently track learning progress by taking into account the technical maturity of the learning topic. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input technical maturity data into a generation AI and have the generation AI perform tracking analysis.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The analysis unit can also estimate the user's learning style and adjust the analysis method based on the estimated learning style. For example, if the user is a visual learner, the analysis unit can provide analysis results that make extensive use of graphs and charts. If the user is an auditory learner, the analysis unit can provide analysis results that include audio feedback. Furthermore, if the user is an experiential learner, the analysis unit can provide analysis results that include interactive elements. In this way, by providing an analysis method that suits the user's learning style, more effective learning support can be achieved.

[0056] The collection unit can also detect the user's learning environment and adjust the data collection method based on the detected learning environment. For example, if the user is learning in a quiet environment, the collection unit can prioritize voice input. Also, if the user is on the move, the collection unit can prioritize data collection from a mobile device. Furthermore, if the user is learning in a group, the collection unit can also collect interaction data with other users. This makes it possible to provide more appropriate learning support by providing a data collection method that suits the user's learning environment.

[0057] The generation unit can also analyze the user's learning history and adjust the difficulty of the quiz based on the user's past learning patterns. For example, a high level of difficulty quiz can be generated for a topic on which the user has previously achieved a high score. A low level of difficulty quiz can also be generated for a topic on which the user has difficulty. Furthermore, the difficulty of the quiz can be gradually increased according to the user's learning progress. In this way, by adjusting the difficulty of the quiz based on the user's learning history, effective learning support can be provided.

[0058] The tracking unit can also set a user's learning goals and customize tracking results based on the set learning goals. For example, if a user's goal is to pass a specific exam, the tracking unit can prioritize and display progress on topics related to that exam. Also, if a user's goal is to acquire a new skill, the tracking unit can display detailed learning progress related to that skill. Furthermore, if a user has a long-term learning goal, the tracking unit can display progress toward that goal in stages. This makes it possible to provide effective learning support by providing tracking results according to the user's learning goals.

[0059] The analysis unit can also predict the effectiveness of learning based on the user's learning history and suggest a study plan based on the prediction results. For example, if the user has previously achieved high scores on a specific topic, the analysis unit can suggest advanced study content related to that topic. It can also suggest basic study content for topics in which the user is weak. It can also adjust the progress of the study plan according to the user's learning pace. This can improve learning efficiency by providing an effective study plan based on the user's learning history.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit collects the user's search history or quiz results. For example, the collection unit collects the user's search history for a specific topic and data on the questions they answered correctly and incorrectly in the quiz. The collection unit also collects information necessary to understand the user's learning progress and level of understanding. Step 2: The analysis unit analyzes the user's learning progress and level of understanding based on the information collected by the collection unit. For example, the analysis unit analyzes the user's search history and quiz results to evaluate the user's learning progress and level of understanding. The analysis unit can also analyze the user's learning patterns to evaluate the effectiveness of their learning. Step 3: The generator generates a personalized quiz based on the information obtained by the analyzer. For example, the generator generates a quiz that includes many questions on topics that the user is weak at. The generator can also include questions on topics that the user is strong at, thereby maintaining a balanced learning experience. Step 4: The tracking unit tracks the results of the quiz generated by the generation unit and visualizes the learning progress. For example, the tracking unit displays the number of questions the user answered correctly in the quiz and the change in the user's understanding of a specific topic in graphs and charts. The tracking unit can also monitor the user's learning progress in real time and evaluate the effectiveness of the learning.

[0062] (Example 2) A learning assistance system according to an embodiment of the present invention allows users to effectively reflect on their own learning and provides a personalized learning experience. The learning assistance system collects a user's search history and quiz results to understand the user's learning progress and level of understanding. Based on this information, the learning assistance system generates personalized quizzes and tracks the quiz results to visualize the user's learning progress. For example, when a user begins learning, the learning assistance system collects the user's search history and quiz results. For example, the system collects data on the user's search history for a specific topic and data on the quiz questions answered correctly and incorrectly. This information is used to understand the user's learning progress and level of understanding. Next, the learning assistance system generates personalized quizzes based on the collected information. For example, generating a quiz that includes many questions on topics the user struggles with can improve the user's level of understanding. Also, including questions on topics the user excels at can help maintain a balanced learning experience. Furthermore, the learning assistance system tracks the quiz results and visualizes the user's learning progress. For example, the system displays the number of questions the user answered correctly in the quiz and changes in the user's level of understanding for a specific topic in graphs and charts. This allows the user to grasp their learning status at a glance. Finally, the learning support system can adjust the user's study plan based on their learning progress. For example, if a user lacks understanding of a particular topic, a study plan can be created that focuses on that topic. Furthermore, for topics in which the user excels, the system can dig deeper to acquire more advanced knowledge. This allows the learning support system to effectively reflect on the user's self-study and provide a personalized learning experience. This allows the learning support system to grasp the user's learning status and generate and track appropriate quizzes, allowing the user to study efficiently.

[0063] A learning assistance system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a tracking unit. The collection unit collects a user's search history or quiz results. For example, the collection unit can collect a user's search history for a specific topic and data on quiz questions answered correctly and incorrectly. The collection unit can also collect information necessary to understand a user's learning progress and level of understanding. The analysis unit analyzes the user's learning progress and level of understanding based on the information collected by the collection unit. For example, the analysis unit can analyze the user's search history and quiz results to evaluate the user's learning progress and level of understanding. The analysis unit can also analyze the user's learning patterns to evaluate the effectiveness of the learning. The generation unit generates personalized quizzes based on the information obtained by the analysis unit. For example, the generation unit can generate quizzes that include many questions on topics that the user is weak at. The generation unit can also maintain a balanced learning experience by including questions on topics that the user is good at. The tracking unit tracks the results of the quizzes generated by the generation unit and visualizes the user's learning progress. For example, the tracking unit can display the number of questions the user answered correctly in a quiz or changes in the user's level of understanding of a particular topic in a graph or chart. The tracking unit can also monitor the user's learning progress in real time and evaluate the effectiveness of the learning. As a result, the learning assistance system according to the embodiment can grasp the user's learning progress and level of understanding, generate personalized quizzes, and visualize the user's learning progress, allowing the user to effectively reflect on their own learning.

[0064] The collection unit estimates the user's emotions and adjusts the timing of collecting search history and quiz results based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit delays the collection timing to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can also accelerate the collection timing to efficiently collect data. Furthermore, if the user is concentrating, the collection unit can also adjust the collection timing to avoid interfering with learning. By adjusting the collection timing according to the user's emotions, the user's burden can be reduced and data can be collected efficiently. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0065] The collection unit can analyze the user's past learning history and select an appropriate collection method. For example, the collection unit can prioritize collecting data from devices and apps that the user has frequently used in the past. The collection unit can also analyze the user's learning patterns and collect data at the optimal timing. The collection unit can also collect data during specific time periods from the user's past learning history. This allows the user's past learning history to be analyzed to select the optimal collection method and collect data efficiently. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past learning history data into the generation AI and have the generation AI select the optimal collection method.

[0066] When collecting search history and quiz results, the collection unit can filter the data based on the user's current learning goals and areas of interest. For example, the collection unit collects only data related to the user's current learning goals. The collection unit can also prioritize collection of highly relevant data based on the user's areas of interest. The collection unit can also filter unnecessary data based on the user's learning goals and areas of interest. This allows for efficient collection of highly relevant data by filtering data based on the user's learning goals and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's learning goal data to a generation AI and have the generation AI perform filtering.

[0067] When collecting search history and quiz results, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user is using voice input, the collection unit can prioritize collecting voice data. Also, if the user is using text input, the collection unit can prioritize collecting text data. Also, if the user is using image input, the collection unit can prioritize collecting image data. This allows efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and have the generation AI select the optimal collection means.

[0068] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit postpones the collection of less important data. Furthermore, when the user is relaxed, the collection unit can also prioritize the collection of more important data. Furthermore, when the user is concentrating, the collection unit can also prioritize the collection of learning-related data. Thus, by determining the priority of data according to the user's emotions, important data can be efficiently collected. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0069] When collecting search history and quiz results, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, if the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, if the user is at home, the collection unit can prioritize collecting data related to the area around the user's home. This allows for efficient collection of highly relevant data by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location data to the generation AI and cause the generation AI to select highly relevant data.

[0070] The collection unit can analyze the user's social media activities and collect related data when collecting search history and quiz results. The collection unit can collect related data based on, for example, information shared by the user on social media. The collection unit can also analyze the user's social media activities and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. In this way, highly relevant data can be efficiently collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related data.

[0071] The collection unit can customize the collection method by reflecting the user's past feedback when collecting search history and quiz results. The collection unit adjusts the collection method based on, for example, feedback provided by the user in the past. The collection unit can also select the optimal collection means based on the user's past feedback. The collection unit can also adjust the collection timing by reflecting the user's feedback. In this way, by reflecting the user's past feedback, the optimal collection method can be selected and data can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into the generation AI and have the generation AI customize the collection method.

[0072] The analysis unit estimates the user's emotions and adjusts the analysis method for the learning progress and comprehension level based on the estimated user emotions. For example, when the user is feeling stressed, the analysis unit simplifies the analysis method to reduce the user's burden. Furthermore, when the user is relaxed, the analysis unit can perform a detailed analysis to improve accuracy. Furthermore, when the user is concentrating, the analysis unit can quickly analyze the learning progress and comprehension level. This reduces the user's burden by adjusting the analysis method according to the user's emotions, and enables efficient analysis of the learning progress and comprehension level. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI. For example, the analysis unit may input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the user's learning history. For example, if the user has an extensive learning history, the analysis unit performs a detailed analysis. Furthermore, if the user has a limited learning history, the analysis unit can also perform a simplified analysis. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the importance of the user's learning history. This allows for efficient analysis of learning progress and comprehension by adjusting the level of detail of the analysis based on the importance of the user's learning history. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's learning history data into a 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 learning topic. For example, the analysis unit can apply a numerical analysis algorithm to science learning topics. The analysis unit can also apply a text analysis algorithm to humanities learning topics. The analysis unit can also select the optimal analysis algorithm depending on the category of the learning topic. This makes it possible to efficiently analyze learning progress and comprehension by applying the optimal analysis algorithm depending on the category of the learning topic. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input learning topic data to a generation AI and have the generation AI select the optimal analysis algorithm.

[0075] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis from the user's past analysis results. The analysis unit can also optimize the analysis method by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0076] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. By adjusting the display method of the analysis results according to the user's emotions, the results can be provided in a format that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit may input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0077] During analysis, the analysis unit can determine the analysis priority based on the submission time of the learning topic. For example, the analysis unit prioritizes analysis of learning topics with upcoming submission deadlines. The analysis unit can also postpone analysis of learning topics with distant submission deadlines. The analysis unit can also adjust the analysis priority based on the submission time of the learning topic. In this way, by determining the analysis priority based on the submission time of the learning topic, learning progress and comprehension level can be analyzed efficiently. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission time of the learning topic to the generation AI and have the generation AI determine the analysis priority.

[0078] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the learning topics. For example, the analysis unit prioritizes analysis of highly relevant learning topics. The analysis unit can also postpone analysis of less relevant learning topics. The analysis unit can also adjust the order of analysis based on the relevance of the learning topics. In this way, by adjusting the order of analysis based on the relevance of the learning topics, it is possible to efficiently analyze learning progress and comprehension. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of learning topics to a generation AI and have the generation AI adjust the order of analysis.

[0079] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user's level of expertise is high, the analysis unit uses a lot of technical terms. Furthermore, if the user's level of expertise is low, the analysis unit can also avoid technical terms. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide results in a form that is easy for the user to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0080] The generation unit estimates the user's emotions and adjusts the presentation of the quiz based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a quiz that progresses at a leisurely pace. If the user is in a hurry, the generation unit can also generate a quiz that emphasizes the shortest route. If the user is excited, the generation unit can also generate a quiz that adds visually stimulating effects. By adjusting the presentation of the quiz according to the user's emotions, an optimal learning experience can be provided for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0081] When generating a quiz, the generation unit can adjust the level of detail of the quiz based on the importance of the learning topic. For example, the generation unit generates a detailed quiz for a learning topic with high importance. The generation unit can also generate a simplified quiz for a learning topic with low importance. The generation unit can also adjust the level of detail of the quiz based on the importance of the learning topic. This allows learning to proceed efficiently by adjusting the level of detail of the quiz based on the importance of the learning topic. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input importance data of the learning topic to the generation AI and cause the generation AI to adjust the level of detail of the quiz.

[0082] When generating a quiz, the generation unit can apply different quiz generation algorithms depending on the category of the learning topic. For example, the generation unit can apply a numerical analysis algorithm to a science learning topic. The generation unit can also apply a text analysis algorithm to a humanities learning topic. The generation unit can also select the optimal quiz generation algorithm depending on the category of the learning topic. This allows learning to proceed efficiently by applying the optimal quiz generation algorithm depending on the category of the learning topic. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input learning topic data into the generation AI and cause the generation AI to select the optimal quiz generation algorithm.

[0083] When generating a quiz, the generation unit can improve the accuracy of the quiz by referring to the user's past quiz results. The generation unit, for example, adjusts the quiz generation algorithm based on the user's past quiz results. The generation unit can also improve the accuracy of the quiz based on the user's past quiz results. The generation unit can also optimize the quiz generation method by referring to the user's past quiz results. This allows the accuracy of the quiz to be improved by referring to the user's past quiz results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past quiz result data into the generation AI and cause the generation AI to improve the accuracy of the quiz.

[0084] The generation unit estimates the user's emotions and adjusts the length of the quiz based on the estimated user emotions. For example, if the user is in a hurry, the generation unit generates a short, to-the-point quiz. Alternatively, if the user is relaxed, the generation unit can generate a longer quiz with detailed explanations. Alternatively, if the user is excited, the generation unit can generate a quiz with visually stimulating effects. By adjusting the length of the quiz according to the user's emotions, the optimal learning experience can be provided for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI. For example, the generation unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0085] When generating quizzes, the generation unit can determine the priority of the quizzes based on the submission times of the learning topics. For example, the generation unit prioritizes generating quizzes related to learning topics with upcoming submission deadlines. The generation unit can also postpone quizzes related to learning topics with distant submission deadlines. The generation unit can also adjust the priority of the quizzes based on the submission times of the learning topics. This allows for efficient learning by determining the priority of the quizzes based on the submission times of the learning topics. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the submission times of the learning topics into the generation AI and have the generation AI determine the priority of the quizzes.

[0086] When generating quizzes, the generation unit can adjust the order of the quizzes based on the relevance of the learning topics. For example, the generation unit prioritizes generating quizzes related to highly relevant learning topics. The generation unit can also postpone quizzes related to less relevant learning topics. The generation unit can also adjust the order of the quizzes based on the relevance of the learning topics. This allows learning to proceed efficiently by adjusting the order of the quizzes based on the relevance of the learning topics. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input relevance data of the learning topics into the generation AI and cause the generation AI to adjust the order of the quizzes.

[0087] When generating a quiz, the generation unit can adjust the use of technical terminology in the quiz according to the user's level of expertise. For example, if the user's level of expertise is high, the generation unit can generate a quiz that uses a lot of technical terminology. Furthermore, if the user's level of expertise is low, the generation unit can also generate a quiz that avoids technical terminology. Furthermore, the generation unit can adjust the use of technical terminology in the quiz according to the user's level of expertise. This allows for an optimal learning experience for the user by adjusting the use of technical terminology in the quiz according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0088] The tracking unit estimates the user's emotions and adjusts the display method of the tracking results based on the estimated user emotions. For example, if the user is nervous, the tracking unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the tracking unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the tracking unit can provide a display method that focuses on the main points. By adjusting the display method of the tracking results according to the user's emotions, the results can be provided in a format that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the tracking unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the tracking unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0089] During tracking, the tracking unit can predict current learning progress by referring to past tracking data. The tracking unit predicts current learning progress based on, for example, the user's past tracking data. The tracking unit can also analyze learning tendencies from the user's past tracking data and predict current progress. The tracking unit can also predict learning progress by referring to the user's past tracking data. In this way, current learning progress can be predicted by referring to the past tracking data. Some or all of the above-described processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input past tracking data into a generation AI and cause the generation AI to predict current learning progress.

[0090] The tracking unit can apply different tracking methods to each learning topic during tracking. For example, the tracking unit can apply a tracking method based on numerical data to science learning topics. The tracking unit can also apply a tracking method based on text data to humanities learning topics. The tracking unit can also select the optimal tracking method for each learning topic. This allows for efficient tracking of learning progress by applying the optimal tracking method for each learning topic. Some or all of the above-mentioned processing in the tracking unit can be performed using, for example, AI, or can be performed without using AI. For example, the tracking unit can input learning topic data into a generation AI and have the generation AI select the optimal tracking method.

[0091] The tracking unit can perform tracking while taking into account the user's attribute information. The tracking unit adjusts the tracking method according to, for example, the user's age and gender. The tracking unit can also adjust the tracking method according to the user's occupation and educational background. The tracking unit can also select the optimal tracking method by taking into account the user's attribute information. This makes it possible to select the optimal tracking method by taking into account the user's attribute information and efficiently track learning progress. Some or all of the above-described processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the user's attribute information data into the generation AI and cause the generation AI to adjust the tracking method.

[0092] The tracking unit estimates the user's emotions and adjusts the importance of tracking results based on the estimated user emotions. For example, if the user is nervous, the tracking unit postpones tracking results with lower importance. Furthermore, if the user is relaxed, the tracking unit can prioritize displaying tracking results with higher importance. Furthermore, if the user is concentrating, the tracking unit can prioritize displaying tracking results related to learning. By adjusting the importance of tracking results according to the user's emotions, it is possible to provide information important to the user preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the tracking unit may be performed using, for example, an AI. For example, the tracking unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0093] During tracking, the tracking unit can analyze tracking changes based on the submission time of the learning topic. For example, the tracking unit prioritizes analysis of tracking results for learning topics with upcoming submission deadlines. The tracking unit can also postpone tracking results for learning topics with distant submission deadlines. The tracking unit can also analyze tracking changes based on the submission time of the learning topic. This makes it possible to efficiently track learning progress by analyzing tracking changes based on the submission time of the learning topic. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input data on the submission time of the learning topic into the generation AI and cause the generation AI to analyze tracking changes.

[0094] The tracking unit can analyze tracking by referring to market data related to the learning topic during tracking. The tracking unit, for example, analyzes tracking results based on market data related to the learning topic. The tracking unit can also analyze tracking results by referring to market trends of the learning topic. The tracking unit can also analyze tracking changes by referring to market data related to the learning topic. This allows learning progress to be tracked efficiently by referring to market data related to the learning topic. Some or all of the above-described processing in the tracking unit can be performed using, for example, AI, or can be performed without using AI. For example, the tracking unit can input related market data to a generation AI and have the generation AI perform tracking analysis.

[0095] The tracking unit can analyze tracking taking into account the technical maturity of the learning topic during tracking. For example, the tracking unit prioritizes analysis of tracking results for technically mature learning topics. The tracking unit can also postpone tracking results for technically immature learning topics. The tracking unit can also analyze tracking changes taking into account the technical maturity of the learning topic. This makes it possible to efficiently track learning progress by taking into account the technical maturity of the learning topic. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input technical maturity data into a generation AI and have the generation AI perform tracking analysis. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and tracking unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the user's search history and quiz results using the camera 42 and microphone 38B of the smart device 14, and grasps the user's learning progress and level of understanding using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's learning progress and level of understanding based on the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates personalized quizzes based on the analysis results. The tracking unit is realized, for example, by the control unit 46A of the smart device 14, and tracks the quiz results and visualizes the learning progress. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and tracking unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the user's search history and quiz results using the camera 42 and microphone 238 of the smart glasses 214, and grasps the user's learning progress and level of understanding using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's learning progress and level of understanding based on the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates personalized quizzes based on the analysis results. The tracking unit is realized, for example, by the control unit 46A of the smart glasses 214, and tracks the quiz results and visualizes the learning progress. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and tracking unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects the user's search history and quiz results using the camera 42 and microphone 238 of the headset-type terminal 314, and grasps the user's learning progress and level of understanding using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's learning progress and level of understanding based on the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates personalized quizzes based on the analysis results. The tracking unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and tracks the quiz results and visualizes the learning progress. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and tracking unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the user's search history and quiz results using the camera 42 and microphone 238 of the robot 414, and grasps the user's learning progress and level of understanding using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's learning progress and level of understanding based on the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates personalized quizzes based on the analysis results. The tracking unit is realized, for example, by the control unit 46A of the robot 414, and tracks the quiz results and visualizes the learning progress.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The analysis unit can also estimate the user's learning style and adjust the analysis method based on the estimated learning style. For example, if the user is a visual learner, the analysis unit can provide analysis results that make extensive use of graphs and charts. If the user is an auditory learner, the analysis unit can provide analysis results that include audio feedback. Furthermore, if the user is an experiential learner, the analysis unit can provide analysis results that include interactive elements. In this way, by providing an analysis method that suits the user's learning style, more effective learning support can be achieved.

[0098] The collection unit can also detect the user's learning environment and adjust the data collection method based on the detected learning environment. For example, if the user is learning in a quiet environment, the collection unit can prioritize voice input. Also, if the user is on the move, the collection unit can prioritize data collection from a mobile device. Furthermore, if the user is learning in a group, the collection unit can also collect interaction data with other users. This makes it possible to provide more appropriate learning support by providing a data collection method that suits the user's learning environment.

[0099] The generation unit can also analyze the user's learning history and adjust the difficulty of the quiz based on the user's past learning patterns. For example, a high level of difficulty quiz can be generated for a topic on which the user has previously achieved a high score. A low level of difficulty quiz can also be generated for a topic on which the user has difficulty. Furthermore, the difficulty of the quiz can be gradually increased according to the user's learning progress. In this way, by adjusting the difficulty of the quiz based on the user's learning history, effective learning support can be provided.

[0100] The tracking unit can also set a user's learning goals and customize tracking results based on the set learning goals. For example, if a user's goal is to pass a specific exam, the tracking unit can prioritize and display progress on topics related to that exam. Also, if a user's goal is to acquire a new skill, the tracking unit can display detailed learning progress related to that skill. Furthermore, if a user has a long-term learning goal, the tracking unit can display progress toward that goal in stages. This makes it possible to provide effective learning support by providing tracking results according to the user's learning goals.

[0101] The analysis unit can also predict the effectiveness of learning based on the user's learning history and suggest a study plan based on the prediction results. For example, if the user has previously achieved high scores on a specific topic, the analysis unit can suggest advanced study content related to that topic. It can also suggest basic study content for topics in which the user is weak. It can also adjust the progress of the study plan according to the user's learning pace. This can improve learning efficiency by providing an effective study plan based on the user's learning history.

[0102] The collection unit can also estimate the user's emotions and adjust the type of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting data related to relaxing content. Also, if the user is excited, the collection unit can collect data related to challenging content. Furthermore, if the user is concentrating, the collection unit can prioritize collecting data related to learning. In this way, by collecting data according to the user's emotions, more effective learning support can be provided.

[0103] The analysis unit can also estimate the user's emotions and adjust the method of providing feedback on the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can emphasize positive feedback. If the user is relaxed, the analysis unit can provide detailed feedback. Furthermore, if the user is in a hurry, the analysis unit can provide concise feedback that focuses on the main points. In this way, providing feedback that matches the user's emotions can make it easier to maintain motivation to learn.

[0104] The generation unit can also estimate the user's emotions and adjust the quiz format based on the estimated user's emotions. For example, if the user is relaxed, a multiple-choice quiz can be provided. If the user is concentrating, a descriptive quiz can be provided. Furthermore, if the user is excited, an interactive quiz can be provided. This makes it possible to maximize the learning effect by providing a quiz format that suits the user's emotions.

[0105] The tracking unit can also estimate the user's emotions and adjust the notification method of the tracking results based on the estimated user's emotions. For example, if the user is nervous, the tracking unit can reduce the notification. If the user is relaxed, the tracking unit can provide detailed notifications. Furthermore, if the user is in a hurry, the tracking unit can provide concise notifications that focus on the main points. In this way, by providing a notification method that corresponds to the user's emotions, it is possible to effectively manage the progress of learning.

[0106] The analysis unit can also estimate the user's emotions and adjust the timing of displaying the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the display of the analysis results can be delayed. Alternatively, if the user is relaxed, the analysis results can be displayed immediately. Furthermore, if the user is concentrating, the analysis results can be displayed at a time that does not interfere with learning. This makes it possible to improve learning efficiency by providing a timing for displaying the analysis results according to the user's emotions.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The collection unit collects the user's search history or quiz results. For example, the collection unit collects the user's search history for a specific topic and data on the questions they answered correctly and incorrectly in the quiz. The collection unit also collects information necessary to understand the user's learning progress and level of understanding. Step 2: The analysis unit analyzes the user's learning progress and level of understanding based on the information collected by the collection unit. For example, the analysis unit analyzes the user's search history and quiz results to evaluate the user's learning progress and level of understanding. The analysis unit can also analyze the user's learning patterns to evaluate the effectiveness of their learning. Step 3: The generator generates a personalized quiz based on the information obtained by the analyzer. For example, the generator generates a quiz that includes many questions on topics that the user is weak at. The generator can also include questions on topics that the user is strong at, thereby maintaining a balanced learning experience. Step 4: The tracking unit tracks the results of the quiz generated by the generation unit and visualizes the learning progress. For example, the tracking unit displays the number of questions the user answered correctly in the quiz and the change in the user's understanding of a specific topic in graphs and charts. The tracking unit can also monitor the user's learning progress in real time and evaluate the effectiveness of the learning.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] [Explanation of symbols]

[0181] 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 user search history or quiz results; an analysis unit that analyzes the user's learning progress and understanding level based on the information collected by the collection unit; a generator that generates a personalized quiz based on the information obtained by the analyzer; a tracking unit that tracks the results of the quiz generated by the generation unit and visualizes the learning progress. A system characterized by:

2. The collecting unit Estimate user emotions and adjust the timing of collecting search history and quiz results based on the estimated user emotions.

2. The system of claim 1.

3. The collecting unit Analyze the user's past learning history and select the appropriate collection method 2. The system of claim 1.

4. The collecting unit Filtering search history and quiz results based on the user's current learning goals and interests when collecting them 2. The system of claim 1.

5. The collecting unit When collecting search history and quiz results, select the optimal collection method depending on the user's input method.

2. The system of claim 1.

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

7. The collecting unit When collecting search history and quiz results, the system prioritizes collecting relevant data based on the user's geographic location.

2. The system of claim 1.

8. The collecting unit Analyze users' social media activity and collect related data when collecting search history and quiz results 2. The system of claim 1.

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