System

Abstract

To provide a system that accurately determines learning proficiency of students and provides appropriate learning support.SOLUTION: A system according to the embodiment comprises a collection unit, a determination unit, a provision unit, and a presentation unit. The collection unit collects learning data of students. The determination unit determines proficiency of the student based on the learning data collected by the collection unit. The provision unit provides the students with explanations and practice problems in areas in which the proficiency determined by the determination unit is lacking. The presentation unit presents the students with the proficiency determined by the determination unit.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have the problem that it is difficult to accurately assess a student's learning proficiency and provide appropriate learning support.

[0005] The system according to the embodiment aims to accurately determine the learning proficiency of students and provide appropriate learning support. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a determination unit, a provision unit, and a presentation unit. The collection unit collects learning data of students. The determination unit determines the proficiency of the students based on the learning data collected by the collection unit. The provision unit provides the students with explanations and practice problems in areas in which the proficiency determined by the determination unit is lacking. The presentation unit presents the proficiency determined by the determination unit to the students. Effect of the Invention

[0007] The system according to the embodiment can accurately determine the learning proficiency of students and provide appropriate learning support. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The learning support system according to the embodiment of the present invention is a system for cram schools and schools that judges the learning proficiency of students and provides explanations and practice problems at an appropriate level for areas where the proficiency is low. This learning support system first collects learning data of students, and then judges the proficiency of students based on the collected learning data. Explanations and practice problems are provided for areas where the judged proficiency is lacking, and the judged proficiency is presented to the student, thereby improving the student's motivation. For example, the learning support system collects data such as the student's test scores, homework submission status, and remarks made during class. These data are collected by a collection unit. Next, the student's proficiency is judged based on the collected learning data. The judgment unit analyzes the collected data and judges the student's proficiency. For example, areas where test scores are low and areas where homework submission status is poor are judged. Explanations and practice problems are provided for areas where the judged proficiency is lacking. The provision unit provides explanations and practice problems at an appropriate level for areas where the proficiency is lacking as judged by the judgment unit. This allows the student to overcome problems that he or she is weak at. Furthermore, the determined proficiency level is presented to the student. The presentation unit presents the proficiency level determined by the determination unit to the student. The presentation is visualized using a graph or chart. This allows the student to understand his / her own proficiency level and improve his / her motivation. This system makes it possible to effectively determine the student's learning proficiency level and provide appropriate support. For example, by providing explanations and practice problems for areas where the student has a low test score, the student can overcome problems that he / she is weak at. In addition, by visualizing the proficiency level, the student's motivation can be improved. This realizes learning support like individualized instruction. This allows the learning support system to effectively determine the student's learning proficiency level and provide appropriate support.

[0029] The learning support system according to the embodiment includes a collection unit, a determination unit, a provision unit, and a presentation unit. The collection unit collects learning data of students. The learning data of students includes, for example, test scores, homework submission status, and remarks made during class, but is not limited to such examples. The collection unit, for example, automatically acquires test scores from an online platform. The collection unit can also acquire homework submission status from a learning management system. Furthermore, the collection unit can convert remarks made during class into text data using voice recognition technology and collect the text data. For example, the collection unit acquires test scores from an online platform and stores the scores in a database. The homework submission status is acquired from a learning management system, and the submission status is recorded in a database. The remarks made during class are converted into text data in real time using voice recognition technology and collected. The determination unit determines the proficiency of students based on the learning data collected by the collection unit. The determination is performed based on, for example, test scores, homework submission status, and remarks made during class, but is not limited to such examples. For example, the determination unit identifies a field in which the test scores are low and determines that the proficiency in that field is insufficient. The determination unit can also identify a field in which the homework submission status is poor and determine that the proficiency in that field is insufficient. Furthermore, the determination unit can analyze the content of remarks made during class, identify a field in which there are few remarks, and determine that the proficiency in that field is insufficient. For example, the determination unit determines that a field in which the test scores are less than 50 points is insufficient in proficiency. The determination unit determines that a field in which the homework submission status is less than 50% is insufficient in proficiency. The determination unit determines that a field in which there are few remarks made during class is insufficient in proficiency. The provision unit provides the student with an explanation and practice problems in the field in which the proficiency is insufficient as determined by the determination unit. The provision is performed, for example, in a text format, a video format, an interactive question format, or the like, but is not limited to such examples. For example, the provision unit provides the explanation in text format on an online platform. The provision unit can also provide the explanation in video format through a streaming service. Furthermore, the provision unit can provide practice problems in an interactive question format.For example, the providing unit provides the explanation in text format on an online platform so that the students can freely view it. The explanation in video format is provided through a streaming service so that the students can watch it. The interactive problem-type practice questions are provided on an online platform so that the students can answer them. The presenting unit presents the proficiency level determined by the determining unit to the students. The presentation is visualized, for example, using a graph or a chart, but is not limited to such examples. For example, the presenting unit displays the proficiency level in a bar graph so that the students can visually understand it. The presenting unit can also display the proficiency level in a pie chart so that the students can grasp the overall balance. Furthermore, the presenting unit can display the proficiency level in a line graph so that the students can understand the change over time. For example, the presenting unit displays the proficiency level in a bar graph so that the proficiency level in each field can be compared. The presenting unit displays the proficiency level in a pie chart so that the overall balance can be grasped. The presenting unit displays the proficiency level in a line graph so that the students can understand the change over time. As a result, the learning support system according to the embodiment can effectively determine the student's learning proficiency and provide appropriate support. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can determine the proficiency using an AI model that inputs the learning data collected by the collection unit and outputs the proficiency. A part or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can provide explanations and practice questions using an AI model that inputs the field in which the proficiency determined by the determination unit is lacking and outputs explanations and practice questions. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can visualize the proficiency using an AI model that inputs the proficiency determined by the determination unit and outputs a graph or chart.

[0030] The collection unit collects the learning data of the students. The learning data of the students includes, for example, test scores, homework submission status, and remarks made during class, but is not limited to such examples. For example, the collection unit automatically acquires test scores from an online platform. The collection unit can also acquire the homework submission status from a learning management system. Furthermore, the collection unit can convert the remarks made during class into text data using voice recognition technology and collect them. For example, the collection unit acquires test scores from an online platform and stores them in a database. The homework submission status is acquired from the learning management system and the submission status is recorded in a database. The remarks made during class are converted into text data in real time using voice recognition technology and collected. The collection unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and can be accessed by the judgment unit and the provision unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. Furthermore, the collection unit can introduce encryption technology and access control to ensure the privacy and security of the data. This allows the collection unit to collect data efficiently and effectively, improving overall system performance.

[0031] The judgment unit judges the proficiency of the student based on the learning data collected by the collection unit. The judgment is made based on, for example, test scores, homework submission status, and remarks during class, but is not limited to such examples. For example, the judgment unit identifies a field in which the test scores are low and judges that the proficiency in that field is insufficient. The judgment unit can also identify a field in which the homework submission status is poor and judge that the proficiency in that field is insufficient. Furthermore, the judgment unit can analyze the remarks during class, identify a field in which there are few remarks, and judge that the proficiency in that field is insufficient. For example, the judgment unit judges that the proficiency in a field in which the test score is less than 50 points is insufficient. The judgment unit judges that the proficiency in a field in which the homework submission status is less than 50% is insufficient. The judgment unit judges that the proficiency in a field in which there are few remarks during class is insufficient. The judgment unit can analyze these data using AI to judge the proficiency. For example, the assessment unit can assess proficiency levels using an AI model that receives the learning data collected by the collection unit as input and outputs proficiency levels. The AI ​​model can make more accurate assessments by learning past data and understanding the learning patterns and tendencies of each student. This allows the assessment unit to quickly and accurately assess students' proficiency levels and build a foundation for providing appropriate support.

[0032] The providing unit provides the student with an explanation and practice problems in the field in which the proficiency level determined by the determining unit is lacking. The provision is performed, for example, in a text format, a video format, an interactive problem format, or the like, but is not limited to such examples. For example, the providing unit provides the explanation in text format on an online platform. The providing unit can also provide the explanation in video format through a streaming service. Furthermore, the providing unit can provide practice problems in an interactive problem format. For example, the providing unit provides the explanation in text format on an online platform so that the student can freely view it. The explanation in video format is provided through a streaming service so that the student can view it. The practice problems in interactive problem format are provided on an online platform so that the student can answer them. The providing unit can analyze these data using AI and provide optimal explanations and practice problems. For example, the providing unit can provide explanations and practice problems using an AI model that takes the field in which the proficiency level determined by the determining unit is lacking as input and outputs explanations and practice problems. The AI ​​model can generate learning content that is individually optimized by taking into account the student's learning history and performance. This enables the department to provide effective learning support tailored to each individual student.

[0033] The presentation unit presents the proficiency level determined by the determination unit to the student. The presentation is visualized, for example, using a graph or a chart, but is not limited to such examples. For example, the presentation unit displays the proficiency level in a bar graph so that the student can visually understand it. The presentation unit can also display the proficiency level in a pie chart so that the student can grasp the overall balance. Furthermore, the presentation unit can display the proficiency level in a line graph so that the student can understand the change over time. For example, the presentation unit displays the proficiency level in a bar graph so that the proficiency level in each field can be compared. The presentation unit displays the proficiency level in a pie chart so that the overall balance can be grasped. The presentation unit displays the proficiency level in a line graph so that the change over time can be understood. The presentation unit can analyze these data using AI and provide an optimal visualization method. For example, the presentation unit can visualize the proficiency level using an AI model that inputs the proficiency level determined by the determination unit and outputs a graph or chart. The AI ​​model can select the optimal display format in consideration of the student's level of understanding and visual preferences. This allows the presentation unit to support students in intuitively understanding their own learning situation and creating effective learning plans.

[0034] The collection unit can collect data on the students' test scores, homework submission status, and remarks made during class in a specific manner. Specific methods include, for example, automatic acquisition from an online platform, acquisition of data from a learning management system, and collection of remarks made during class using voice recognition technology, but are not limited to such examples. For example, the collection unit automatically acquires test scores from an online platform and stores them in a database. The collection unit can also acquire homework submission status from a learning management system and record the submission status in a database. Furthermore, the collection unit can convert remarks made during class into text data in real time using voice recognition technology and collect the data. For example, the collection unit acquires test scores from an online platform and stores them in a database. The homework submission status is acquired from a learning management system and the submission status is recorded in a database. The remarks made during class are converted into text data in real time using voice recognition technology and collected. This makes it possible to collect students' learning data from multiple angles, thereby making it possible to more accurately determine their proficiency. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input test scores obtained from an online platform into the generation AI and have the generation AI store the scores in a database.

[0035] The presentation unit can visualize the proficiency using a graph or chart. Specific types of graphs or charts include, but are not limited to, bar graphs, pie charts, line graphs, and the like. The presentation unit, for example, displays the proficiency in a bar graph so that the student can visually understand it. The presentation unit can also display the proficiency in a pie chart so that the student can grasp the overall balance. Furthermore, the presentation unit can display the proficiency in a line graph so that the student can understand the change over time. For example, the presentation unit displays the proficiency in a bar graph so that the proficiency in each field can be compared. The presentation unit displays the proficiency in a pie chart so that the overall balance can be grasped. The presentation unit displays the proficiency in a line graph so that the change over time can be understood. In this way, by visually displaying the proficiency, it is possible to help the student understand and improve their motivation. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can visualize the proficiency level using an AI model that receives the proficiency level determined by the determination unit and outputs a graph or chart.

[0036] The collection unit can analyze the student's past learning history and select a data collection method. For example, the collection unit collects data in a similar format based on the format of a test in which the student has previously scored high. The collection unit can also analyze the content of homework submitted by the student in the past and collect homework of a similar difficulty level. The collection unit can also analyze the content of the student's remarks during class and preferentially collect data from classes in which the student has many remarks. For example, the collection unit collects data in a similar format based on the format of a test in which the student has previously scored high. The collection unit analyzes the content of homework submitted by the student in the past and collects homework of a similar difficulty level. The collection unit analyzes the content of the student's remarks during class and preferentially collects data from classes in which the student has many remarks. This enables efficient data collection by selecting an optimal data collection method based on the past learning history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the student's past learning history data to the generation AI and cause the generation AI to select an optimal data collection method.

[0037] When collecting learning data, the collection unit can perform filtering based on the student's current learning situation and field of interest. For example, the collection unit preferentially collects data related to the subject the student is currently studying. The collection unit can also collect data related to the field in which the student is interested to increase the student's motivation to study. Furthermore, the collection unit can collect only necessary data according to the student's learning progress. For example, the collection unit preferentially collects data related to the subject the student is currently studying. The collection unit collects data related to the field in which the student is interested to increase the student's motivation to study. The collection unit collects only necessary data according to the student's learning progress. This allows more relevant data to be collected by filtering data based on the student's current learning situation and field of interest. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data on the student's current learning situation and field of interest to the generation AI and cause the generation AI to perform filtering.

[0038] When collecting learning data, the collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the student. For example, if the student lives in a specific area, the collection unit preferentially collects data related to the area. In addition, if the student attends a specific school, the collection unit can also preferentially collect data related to the school's curriculum. In addition, if the student participates in an event held in a specific area, the collection unit can also preferentially collect data related to the event. For example, if the student lives in a specific area, the collection unit preferentially collects data related to the area. If the student attends a specific school, the collection unit preferentially collects data related to the school's curriculum. In addition, if the student participates in an event held in a specific area, the collection unit preferentially collects data related to the event. In this way, more relevant data can be collected by considering the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the student's geographical location information to the generation AI and cause the generation AI to perform preferential collection of highly relevant data.

[0039] The collection unit can analyze the social media activities of the student and collect related data when collecting the learning data. The collection unit collects related data, for example, based on the learning content shared by the student on social media. The collection unit can also collect related data from education-related accounts that the student follows on social media. The collection unit can also collect related data based on the activity content of the learning group in which the student participates on social media. For example, the collection unit collects related data based on the learning content shared by the student on social media. The collection unit collects related data from education-related accounts that the student follows on social media. The collection unit collects related data based on the activity content of the learning group in which the student participates on social media. This makes it possible to collect more multifaceted data by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the social media activity data of the student to the generation AI and cause the generation AI to collect related data.

[0040] The judgment unit can adjust the level of detail of the judgment based on the importance of the learning data at the time of judgment. The judgment unit makes a detailed judgment based on, for example, the score of an important test. The judgment unit can also make a simplified judgment based on the homework submission status. Furthermore, the judgment unit can make a judgment with an appropriate level of detail based on the content of remarks during class. For example, the judgment unit makes a detailed judgment based on the score of an important test. The judgment unit makes a simplified judgment based on the homework submission status. The judgment unit makes a judgment with an appropriate level of detail based on the content of remarks during class. This allows for a more accurate judgment by adjusting the level of detail of the judgment based on the importance of the learning data. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the importance of the learning data to the generation AI and cause the generation AI to adjust the level of detail of the judgment.

[0041] The judgment unit can apply different judgment algorithms depending on the category of the learning data at the time of judgment. The judgment unit applies a specific algorithm based on, for example, the test score to make a judgment. The judgment unit can also apply another algorithm based on the homework submission status to make a judgment. Furthermore, the judgment unit can apply yet another algorithm based on the content of the remark during the class to make a judgment. For example, the judgment unit applies a specific algorithm based on the test score to make a judgment. Based on the homework submission status, a different algorithm is applied to make a judgment. Based on the content of the remark during the class, a still another algorithm is applied to make a judgment. This makes it possible to make a more appropriate judgment by applying different algorithms depending on the category of the learning data. Part or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the category of the learning data to the generation AI and cause the generation AI to apply different judgment algorithms.

[0042] The judgment unit can determine the priority of judgment based on the submission time of the learning data at the time of judgment. For example, the judgment unit gives priority to the score of the test recently submitted. The judgment unit can also give priority to data that is submitted late based on the submission time of the homework. Furthermore, the judgment unit can also give priority to the latest data based on the submission time of the remarks made during class. For example, the judgment unit gives priority to the score of the test recently submitted. The judgment unit gives priority to data that is submitted late based on the submission time of the homework. The judgment unit gives priority to the latest data based on the submission time of the remarks made during class. This allows for more efficient judgment by determining the priority based on the submission time. Part or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the submission time of the learning data to the generation AI and cause the generation AI to execute the determination of the priority of judgment.

[0043] The judgment unit can adjust the order of judgment based on the relevance of the learning data at the time of judgment. For example, the judgment unit preferentially judges data with high relevance in test scores. The judgment unit can also preferentially judge data with high relevance in homework submission status. Furthermore, the judgment unit can also preferentially judge data with high relevance in remarks made during class. For example, the judgment unit preferentially judges data with high relevance in test scores. The judgment unit preferentially judges data with high relevance in homework submission status. The judgment unit preferentially judges data with high relevance in remarks made during class. This allows for more appropriate judgment by adjusting the order of judgment based on relevance. Part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the relevance of the learning data to the generation AI and cause the generation AI to adjust the order of judgment.

[0044] The providing unit can adjust the level of detail of the provision based on the importance of the learning content at the time of provision. For example, the providing unit provides detailed explanations and practice questions for important learning content. The providing unit can also provide simple explanations and practice questions for less important learning content. Furthermore, the providing unit can also provide explanations and practice questions with a moderate level of detail according to the importance of the learning content. For example, the providing unit provides detailed explanations and practice questions for important learning content. The providing unit provides simple explanations and practice questions for less important learning content. The providing unit provides explanations and practice questions with a moderate level of detail according to the importance of the learning content. This enables more appropriate learning support by adjusting the level of detail based on the importance of the learning content. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the importance of the learning content to the generating AI and cause the generating AI to adjust the level of detail of the provision.

[0045] The providing unit can apply different provision algorithms according to the category of the learning content at the time of provision. For example, the providing unit applies a specific algorithm to the learning content of mathematics to provide explanations and practice problems. The providing unit can also apply another algorithm to the learning content of English to provide explanations and practice problems. Furthermore, the providing unit can apply yet another algorithm to the learning content of science to provide explanations and practice problems. For example, the providing unit applies a specific algorithm to the learning content of mathematics to provide explanations and practice problems. For the learning content of English to provide explanations and practice problems. For the learning content of science to provide explanations and practice problems, another algorithm is applied. This makes it possible to provide more appropriate learning support by applying different algorithms according to the category of the learning content. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the category of the learning content to the generating AI and cause the generating AI to apply different provision algorithms.

[0046] The providing unit can determine the priority of provision based on the submission time of the learning content at the time of provision. For example, the providing unit provides explanations and practice questions preferentially for recently submitted learning content. The providing unit can also provide explanations and practice questions preferentially for learning content submitted late. Furthermore, the providing unit can provide explanations and practice questions with appropriate priority based on the submission time. For example, the providing unit provides explanations and practice questions with priority for recently submitted learning content. The providing unit provides explanations and practice questions with priority for learning content submitted late. The providing unit provides explanations and practice questions with appropriate priority based on the submission time. As a result, more effective learning support is possible by determining the priority based on the submission time. Part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the submission time of the learning content to the generating AI and cause the generating AI to determine the priority of provision.

[0047] The providing unit can adjust the order of provision based on the relevance of the learning contents at the time of provision. For example, the providing unit provides explanations and practice questions preferentially for important learning contents. The providing unit can also provide explanations and practice questions preferentially for highly relevant learning contents. Furthermore, the providing unit can provide explanations and practice questions in an appropriate order based on the relevance of the learning contents. For example, the providing unit provides explanations and practice questions preferentially for important learning contents. The providing unit provides explanations and practice questions preferentially for highly relevant learning contents. The providing unit provides explanations and practice questions in an appropriate order based on the relevance of the learning contents. This enables more effective learning support by adjusting the order of provision based on the relevance. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the relevance of the learning contents to the generating AI and cause the generating AI to adjust the order of provision.

[0048] The presentation unit can select the display method by referring to the student's past learning history at the time of presentation. The presentation unit provides an optimal display method, for example, based on a display method that the student has used with preference in the past. The presentation unit can also select a display method with high visibility from the student's past learning history. The presentation unit can also analyze the student's past learning history and provide the most effective display method. For example, the presentation unit provides an optimal display method based on a display method that the student has used with preference in the past. A display method with high visibility is selected from the student's past learning history. The student's past learning history is analyzed and the most effective display method is provided. This allows for more effective feedback by selecting the optimal display method based on the past learning history. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the student's past learning history data to the generation AI and cause the generation AI to select the optimal display method.

[0049] The presentation unit can apply different display algorithms according to the proficiency category at the time of presentation. The presentation unit applies a specific display algorithm to the proficiency of mathematics, for example. The presentation unit can also apply another display algorithm to the proficiency of English. The presentation unit can also apply yet another display algorithm to the proficiency of science. For example, the presentation unit applies a specific display algorithm to the proficiency of mathematics. The presentation unit applies another display algorithm to the proficiency of English. The presentation unit applies yet another display algorithm to the proficiency of science. This enables more effective feedback by applying different display algorithms according to the proficiency category. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the proficiency category to the generation AI and cause the generation AI to apply the different display algorithms.

[0050] The presentation unit can select a display method by considering the geographical location information of the student at the time of presentation. For example, when the student lives in a specific area, the presentation unit provides a display method related to the area. In addition, when the student attends a specific school, the presentation unit can also provide a display method related to the school's curriculum. In addition, when the student participates in an event held in a specific area, the presentation unit can also provide a display method related to the event. For example, when the student lives in a specific area, the presentation unit provides a display method related to the area. When the student attends a specific school, the presentation unit provides a display method related to the school's curriculum. When the student participates in an event held in a specific area, the presentation unit provides a display method related to the event. In this way, by considering the geographical location information, a more relevant display method can be provided. Part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the geographical location information of the student to the generation AI and cause the generation AI to select the optimal display method.

[0051] The presentation unit can display related information by analyzing the student's social media activity at the time of presentation. The presentation unit displays related information based on, for example, the learning content shared by the student on social media. The presentation unit can also display related information from education-related accounts followed by the student on social media. The presentation unit can also display related information based on the activity content of the learning group in which the student participates on social media. For example, the presentation unit displays related information based on the learning content shared by the student on social media. The presentation unit displays related information from education-related accounts followed by the student on social media. The presentation unit displays related information based on the activity content of the learning group in which the student participates on social media. This makes it possible to provide more multifaceted information by analyzing social media activity. Part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the student's social media activity data to the generation AI and cause the generation AI to execute display of related information.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The learning support system can further analyze the learning style of each student and provide an individually optimized learning plan. For example, a student who prefers visual learning can be provided with learning materials that make heavy use of videos and infographics. A student who prefers auditory learning can be provided with learning materials in the form of audio commentary or podcasts. Furthermore, a student who prefers hands-on learning can be provided with learning materials that include interactive simulations and experiments. This maximizes the learning effect by providing an optimal learning plan according to the student's learning style.

[0054] The learning support system can also predict future learning content based on the student's learning history and support anticipatory learning. For example, it can analyze past test results and homework submission status to predict what content should be studied next. It can also provide appropriate learning materials and practice questions based on the predicted learning content. It can also conduct tests to check the student's understanding of the predicted learning content and adjust the learning plan based on the results. This makes it possible to anticipate the student's learning progress and support efficient learning.

[0055] The learning support system can also monitor the student's learning environment and provide an optimal learning environment. For example, it can use sensors to detect noise levels and lighting brightness during learning and provide advice on maintaining an appropriate environment. It can also monitor posture during learning and timing of breaks and provide advice on promoting healthy learning habits. It can also adjust the temperature and humidity of the learning environment to provide a comfortable learning environment. This can support students in learning in an optimal environment.

[0056] The learning support system can also set learning goals for students and monitor their achievement. For example, it can record the short-term and long-term learning goals set by students and periodically check their achievement status. It can also visualize the progress toward achieving the goals and provide feedback to students. It can also suggest action plans for achieving the goals and support students to achieve them effectively. This allows students to work effectively toward their learning goals.

[0057] The learning support system can also evaluate students' learning performance in real time and provide immediate feedback. For example, it can instantly analyze the results of online tests and quizzes and provide feedback to students. It can also monitor learning progress in real time and provide advice and support when necessary. In addition, it can provide chatbots and online support to resolve questions and problems during learning in real time. This can quickly solve problems students face during learning and support effective learning.

[0058] The process flow of the first embodiment will be briefly described below.

[0059] Step 1: The collection unit collects learning data of students. The learning data of students includes, for example, test scores, homework submission status, and comments made during class. The collection unit automatically obtains test scores from an online platform and obtains homework submission status from a learning management system. In addition, the collection unit converts comments made during class into text data using voice recognition technology and collects the text data. Step 2: The assessment unit assesses the student's proficiency based on the learning data collected by the collection unit. The assessment is based on test scores, homework submission status, and comments made during class. For example, it identifies areas where the student has low test scores, poor homework submission status, or few comments made during class, and assesses that the student's proficiency in those areas is insufficient. Step 3: The provision unit provides the students with explanations and practice problems in the areas where the proficiency level is lacking as determined by the assessment unit. The provision is performed in a text format, a video format, an interactive problem format, or the like. For example, the text format explanation is provided on an online platform, the video format explanation is provided through a streaming service, and the practice problems are provided in the form of interactive problems. Step 4: The presentation unit presents the proficiency level determined by the assessment unit to the student. The presentation is visualized using graphs and charts. For example, the proficiency level is displayed as a bar graph, pie chart, or line graph to enable the student to visually understand.

[0060] (Example 2) The learning support system according to the embodiment of the present invention is a system for cram schools and schools that judges the learning proficiency of students and provides explanations and practice problems at an appropriate level for areas where the proficiency is low. This learning support system first collects learning data of students, and then judges the proficiency of students based on the collected learning data. Explanations and practice problems are provided for areas where the judged proficiency is lacking, and the judged proficiency is presented to the student, thereby improving the student's motivation. For example, the learning support system collects data such as the student's test scores, homework submission status, and remarks made during class. These data are collected by a collection unit. Next, the student's proficiency is judged based on the collected learning data. The judgment unit analyzes the collected data and judges the student's proficiency. For example, areas where test scores are low and areas where homework submission status is poor are judged. Explanations and practice problems are provided for areas where the judged proficiency is lacking. The provision unit provides explanations and practice problems at an appropriate level for areas where the proficiency is lacking as judged by the judgment unit. This allows the student to overcome problems that he or she is weak at. Furthermore, the determined proficiency level is presented to the student. The presentation unit presents the proficiency level determined by the determination unit to the student. The presentation is visualized using a graph or chart. This allows the student to understand his / her own proficiency level and improve his / her motivation. This system makes it possible to effectively determine the student's learning proficiency level and provide appropriate support. For example, by providing explanations and practice problems for areas where the student has a low test score, the student can overcome problems that he / she is weak at. In addition, by visualizing the proficiency level, the student's motivation can be improved. This realizes learning support like individualized instruction. This allows the learning support system to effectively determine the student's learning proficiency level and provide appropriate support.

[0061] The learning support system according to the embodiment includes a collection unit, a determination unit, a provision unit, and a presentation unit. The collection unit collects learning data of students. The learning data of students includes, for example, test scores, homework submission status, and remarks made during class, but is not limited to such examples. The collection unit, for example, automatically acquires test scores from an online platform. The collection unit can also acquire homework submission status from a learning management system. Furthermore, the collection unit can convert remarks made during class into text data using voice recognition technology and collect the text data. For example, the collection unit acquires test scores from an online platform and stores the scores in a database. The homework submission status is acquired from a learning management system, and the submission status is recorded in a database. The remarks made during class are converted into text data in real time using voice recognition technology and collected. The determination unit determines the proficiency of students based on the learning data collected by the collection unit. The determination is performed based on, for example, test scores, homework submission status, and remarks made during class, but is not limited to such examples. For example, the determination unit identifies a field in which the test scores are low and determines that the proficiency in that field is insufficient. The determination unit can also identify a field in which the homework submission status is poor and determine that the proficiency in that field is insufficient. Furthermore, the determination unit can analyze the content of remarks made during class, identify a field in which there are few remarks, and determine that the proficiency in that field is insufficient. For example, the determination unit determines that a field in which the test scores are less than 50 points is insufficient in proficiency. The determination unit determines that a field in which the homework submission status is less than 50% is insufficient in proficiency. The determination unit determines that a field in which there are few remarks made during class is insufficient in proficiency. The provision unit provides the student with an explanation and practice problems in the field in which the proficiency is insufficient as determined by the determination unit. The provision is performed, for example, in a text format, a video format, an interactive question format, or the like, but is not limited to such examples. For example, the provision unit provides the explanation in text format on an online platform. The provision unit can also provide the explanation in video format through a streaming service. Furthermore, the provision unit can provide practice problems in an interactive question format.For example, the providing unit provides the explanation in text format on an online platform so that the students can freely view it. The explanation in video format is provided through a streaming service so that the students can watch it. The interactive problem-type practice questions are provided on an online platform so that the students can answer them. The presenting unit presents the proficiency level determined by the determining unit to the students. The presentation is visualized, for example, using a graph or a chart, but is not limited to such examples. For example, the presenting unit displays the proficiency level in a bar graph so that the students can visually understand it. The presenting unit can also display the proficiency level in a pie chart so that the students can grasp the overall balance. Furthermore, the presenting unit can display the proficiency level in a line graph so that the students can understand the change over time. For example, the presenting unit displays the proficiency level in a bar graph so that the proficiency level in each field can be compared. The presenting unit displays the proficiency level in a pie chart so that the overall balance can be grasped. The presenting unit displays the proficiency level in a line graph so that the students can understand the change over time. As a result, the learning support system according to the embodiment can effectively determine the student's learning proficiency and provide appropriate support. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can determine the proficiency using an AI model that inputs the learning data collected by the collection unit and outputs the proficiency. A part or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can provide explanations and practice questions using an AI model that inputs the field in which the proficiency determined by the determination unit is lacking and outputs explanations and practice questions. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can visualize the proficiency using an AI model that inputs the proficiency determined by the determination unit and outputs a graph or chart.

[0062] The collection unit collects the learning data of the students. The learning data of the students includes, for example, test scores, homework submission status, and remarks made during class, but is not limited to such examples. For example, the collection unit automatically acquires test scores from an online platform. The collection unit can also acquire the homework submission status from a learning management system. Furthermore, the collection unit can convert the remarks made during class into text data using voice recognition technology and collect them. For example, the collection unit acquires test scores from an online platform and stores them in a database. The homework submission status is acquired from the learning management system and the submission status is recorded in a database. The remarks made during class are converted into text data in real time using voice recognition technology and collected. The collection unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and can be accessed by the judgment unit and the provision unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. Furthermore, the collection unit can introduce encryption technology and access control to ensure the privacy and security of the data. This allows the collection unit to collect data efficiently and effectively, improving overall system performance.

[0063] The judgment unit judges the proficiency of the student based on the learning data collected by the collection unit. The judgment is made based on, for example, test scores, homework submission status, and remarks during class, but is not limited to such examples. For example, the judgment unit identifies a field in which the test scores are low and judges that the proficiency in that field is insufficient. The judgment unit can also identify a field in which the homework submission status is poor and judge that the proficiency in that field is insufficient. Furthermore, the judgment unit can analyze the remarks during class, identify a field in which there are few remarks, and judge that the proficiency in that field is insufficient. For example, the judgment unit judges that the proficiency in a field in which the test score is less than 50 points is insufficient. The judgment unit judges that the proficiency in a field in which the homework submission status is less than 50% is insufficient. The judgment unit judges that the proficiency in a field in which there are few remarks during class is insufficient. The judgment unit can analyze these data using AI to judge the proficiency. For example, the assessment unit can assess proficiency levels using an AI model that receives the learning data collected by the collection unit as input and outputs proficiency levels. The AI ​​model can make more accurate assessments by learning past data and understanding the learning patterns and tendencies of each student. This allows the assessment unit to quickly and accurately assess students' proficiency levels and build a foundation for providing appropriate support.

[0064] The providing unit provides the student with an explanation and practice problems in the field in which the proficiency level determined by the determining unit is lacking. The provision is performed, for example, in a text format, a video format, an interactive problem format, or the like, but is not limited to such examples. For example, the providing unit provides the explanation in text format on an online platform. The providing unit can also provide the explanation in video format through a streaming service. Furthermore, the providing unit can provide practice problems in an interactive problem format. For example, the providing unit provides the explanation in text format on an online platform so that the student can freely view it. The explanation in video format is provided through a streaming service so that the student can view it. The practice problems in interactive problem format are provided on an online platform so that the student can answer them. The providing unit can analyze these data using AI and provide optimal explanations and practice problems. For example, the providing unit can provide explanations and practice problems using an AI model that takes the field in which the proficiency level determined by the determining unit is lacking as input and outputs explanations and practice problems. The AI ​​model can generate learning content that is individually optimized by taking into account the student's learning history and performance. This enables the department to provide effective learning support tailored to each individual student.

[0065] The presentation unit presents the proficiency level determined by the determination unit to the student. The presentation is visualized, for example, using a graph or a chart, but is not limited to such examples. For example, the presentation unit displays the proficiency level in a bar graph so that the student can visually understand it. The presentation unit can also display the proficiency level in a pie chart so that the student can grasp the overall balance. Furthermore, the presentation unit can display the proficiency level in a line graph so that the student can understand the change over time. For example, the presentation unit displays the proficiency level in a bar graph so that the proficiency level in each field can be compared. The presentation unit displays the proficiency level in a pie chart so that the overall balance can be grasped. The presentation unit displays the proficiency level in a line graph so that the change over time can be understood. The presentation unit can analyze these data using AI and provide an optimal visualization method. For example, the presentation unit can visualize the proficiency level using an AI model that inputs the proficiency level determined by the determination unit and outputs a graph or chart. The AI ​​model can select the optimal display format in consideration of the student's level of understanding and visual preferences. This allows the presentation unit to support students in intuitively understanding their own learning situation and creating effective learning plans.

[0066] The collection unit can collect data on the students' test scores, homework submission status, and remarks made during class in a specific manner. Specific methods include, for example, automatic acquisition from an online platform, acquisition of data from a learning management system, and collection of remarks made during class using voice recognition technology, but are not limited to such examples. For example, the collection unit automatically acquires test scores from an online platform and stores them in a database. The collection unit can also acquire homework submission status from a learning management system and record the submission status in a database. Furthermore, the collection unit can convert remarks made during class into text data in real time using voice recognition technology and collect the data. For example, the collection unit acquires test scores from an online platform and stores them in a database. The homework submission status is acquired from a learning management system and the submission status is recorded in a database. The remarks made during class are converted into text data in real time using voice recognition technology and collected. This makes it possible to collect students' learning data from multiple angles, thereby making it possible to more accurately determine their proficiency. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input test scores obtained from an online platform into the generation AI and have the generation AI store the scores in a database.

[0067] The presentation unit can visualize the proficiency using a graph or chart. Specific types of graphs or charts include, but are not limited to, bar graphs, pie charts, line graphs, and the like. The presentation unit, for example, displays the proficiency in a bar graph so that the student can visually understand it. The presentation unit can also display the proficiency in a pie chart so that the student can grasp the overall balance. Furthermore, the presentation unit can display the proficiency in a line graph so that the student can understand the change over time. For example, the presentation unit displays the proficiency in a bar graph so that the proficiency in each field can be compared. The presentation unit displays the proficiency in a pie chart so that the overall balance can be grasped. The presentation unit displays the proficiency in a line graph so that the change over time can be understood. In this way, by visually displaying the proficiency, it is possible to help the student understand and improve their motivation. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can visualize the proficiency level using an AI model that receives the proficiency level determined by the determination unit and outputs a graph or chart.

[0068] The collection unit can estimate the student's emotion and adjust the timing of collecting the learning data based on the estimated student's emotion. For example, when the student is stressed, the collection unit provides a break time to collect the learning data in a relaxed state. In addition, when the student is concentrating, the collection unit can also collect the test scores and the homework submission status at that timing. Furthermore, when the student is tired, the collection unit can adjust the schedule to collect the learning data on the next day. For example, when the student is stressed, the collection unit provides a break time to collect the learning data in a relaxed state. When the student is concentrating, the collection unit collects the test scores and the homework submission status at that timing. When the student is tired, the schedule is adjusted to collect the learning data on the next day. This allows more appropriate data collection by adjusting the timing of data collection according to the student's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. Some or all of the above-mentioned processes in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input emotion data of students to a generation AI and have the generation AI perform emotion estimation.

[0069] The collection unit can analyze the student's past learning history and select a data collection method. For example, the collection unit collects data in a similar format based on the format of a test in which the student has previously scored high. The collection unit can also analyze the content of homework submitted by the student in the past and collect homework of a similar difficulty level. The collection unit can also analyze the content of the student's remarks during class and preferentially collect data from classes in which the student has many remarks. For example, the collection unit collects data in a similar format based on the format of a test in which the student has previously scored high. The collection unit analyzes the content of homework submitted by the student in the past and collects homework of a similar difficulty level. The collection unit analyzes the content of the student's remarks during class and preferentially collects data from classes in which the student has many remarks. This enables efficient data collection by selecting an optimal data collection method based on the past learning history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the student's past learning history data to the generation AI and cause the generation AI to select an optimal data collection method.

[0070] When collecting learning data, the collection unit can perform filtering based on the student's current learning situation and field of interest. For example, the collection unit preferentially collects data related to the subject the student is currently studying. The collection unit can also collect data related to the field in which the student is interested to increase the student's motivation to study. Furthermore, the collection unit can collect only necessary data according to the student's learning progress. For example, the collection unit preferentially collects data related to the subject the student is currently studying. The collection unit collects data related to the field in which the student is interested to increase the student's motivation to study. The collection unit collects only necessary data according to the student's learning progress. This allows more relevant data to be collected by filtering data based on the student's current learning situation and field of interest. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data on the student's current learning situation and field of interest to the generation AI and cause the generation AI to perform filtering.

[0071] The collection unit can estimate the student's emotion and determine the priority of data to be collected based on the estimated student's emotion. For example, when the student is stressed, the collection unit preferentially collects data with easy questions or relaxing content. In addition, when the student is concentrating, the collection unit can also preferentially collect difficult questions or important data. In addition, when the student is tired, the collection unit can also preferentially collect data with light content. For example, when the student is stressed, the collection unit preferentially collects data with easy questions or relaxing content. When the student is concentrating, the collection unit preferentially collects difficult questions or important data. When the student is tired, the collection unit preferentially collects data with light content. This enables more effective data collection by determining the priority of data according to the student's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input students' emotion data into the generation AI and have the generation AI perform emotion estimation.

[0072] When collecting learning data, the collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the student. For example, if the student lives in a specific area, the collection unit preferentially collects data related to the area. In addition, if the student attends a specific school, the collection unit can also preferentially collect data related to the school's curriculum. In addition, if the student participates in an event held in a specific area, the collection unit can also preferentially collect data related to the event. For example, if the student lives in a specific area, the collection unit preferentially collects data related to the area. If the student attends a specific school, the collection unit preferentially collects data related to the school's curriculum. In addition, if the student participates in an event held in a specific area, the collection unit preferentially collects data related to the event. In this way, more relevant data can be collected by considering the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the student's geographical location information to the generation AI and cause the generation AI to perform preferential collection of highly relevant data.

[0073] The collection unit can analyze the social media activities of the student and collect related data when collecting the learning data. The collection unit collects related data, for example, based on the learning content shared by the student on social media. The collection unit can also collect related data from education-related accounts that the student follows on social media. The collection unit can also collect related data based on the activity content of the learning group in which the student participates on social media. For example, the collection unit collects related data based on the learning content shared by the student on social media. The collection unit collects related data from education-related accounts that the student follows on social media. The collection unit collects related data based on the activity content of the learning group in which the student participates on social media. This makes it possible to collect more multifaceted data by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the social media activity data of the student to the generation AI and cause the generation AI to collect related data.

[0074] The judgment unit can estimate the student's emotion and adjust the judgment criteria of the proficiency level based on the estimated emotion of the student. For example, when the student is stressed, the judgment unit relaxes the judgment criteria and increases positive feedback. Also, when the student is relaxed, the judgment unit can tighten the judgment criteria and perform an accurate evaluation. Furthermore, when the student is concentrating, the judgment unit can moderately adjust the judgment criteria and perform a balanced evaluation. For example, when the student is stressed, the judgment unit relaxes the judgment criteria and increases positive feedback. When the student is relaxed, the judgment criteria are tightened and an accurate evaluation is performed. When the student is concentrating, the judgment unit moderately adjusts the judgment criteria and performs a balanced evaluation. This allows for a more appropriate proficiency level judgment by adjusting the judgment criteria according to the student's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit may input the emotion data of the student to the generation AI and cause the generation AI to estimate the emotion.

[0075] The judgment unit can adjust the level of detail of the judgment based on the importance of the learning data at the time of judgment. The judgment unit makes a detailed judgment based on, for example, the score of an important test. The judgment unit can also make a simplified judgment based on the homework submission status. Furthermore, the judgment unit can make a judgment with an appropriate level of detail based on the content of remarks during class. For example, the judgment unit makes a detailed judgment based on the score of an important test. The judgment unit makes a simplified judgment based on the homework submission status. The judgment unit makes a judgment with an appropriate level of detail based on the content of remarks during class. This allows for a more accurate judgment by adjusting the level of detail of the judgment based on the importance of the learning data. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the importance of the learning data to the generation AI and cause the generation AI to adjust the level of detail of the judgment.

[0076] The judgment unit can apply different judgment algorithms depending on the category of the learning data at the time of judgment. The judgment unit applies a specific algorithm based on, for example, the test score to make a judgment. The judgment unit can also apply another algorithm based on the homework submission status to make a judgment. Furthermore, the judgment unit can apply yet another algorithm based on the content of the remark during the class to make a judgment. For example, the judgment unit applies a specific algorithm based on the test score to make a judgment. Based on the homework submission status, a different algorithm is applied to make a judgment. Based on the content of the remark during the class, a still another algorithm is applied to make a judgment. This makes it possible to make a more appropriate judgment by applying different algorithms depending on the category of the learning data. Part or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the category of the learning data to the generation AI and cause the generation AI to apply different judgment algorithms.

[0077] The determination unit can estimate the student's emotion and adjust the display method of the determination result based on the estimated student's emotion. For example, when the student is nervous, the determination unit provides a simple and highly visible display method. Furthermore, when the student is relaxed, the determination unit can also provide a display method including detailed information. Furthermore, when the student is in a hurry, the determination unit can also provide a display method that focuses on the main points. For example, when the student is nervous, the determination unit provides a simple and highly visible display method. When the student is relaxed, the determination unit provides a display method including detailed information. When the student is in a hurry, the determination unit provides a display method that focuses on the main points. This allows for more effective feedback by adjusting the display method according to the student's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit can input emotional data of students to the generation AI and have the generation AI perform emotion estimation.

[0078] The judgment unit can determine the priority of judgment based on the submission time of the learning data at the time of judgment. For example, the judgment unit gives priority to the score of the test recently submitted. The judgment unit can also give priority to data that is submitted late based on the submission time of the homework. Furthermore, the judgment unit can also give priority to the latest data based on the submission time of the remarks made during class. For example, the judgment unit gives priority to the score of the test recently submitted. The judgment unit gives priority to data that is submitted late based on the submission time of the homework. The judgment unit gives priority to the latest data based on the submission time of the remarks made during class. This allows for more efficient judgment by determining the priority based on the submission time. Part or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the submission time of the learning data to the generation AI and cause the generation AI to execute the determination of the priority of judgment.

[0079] The judgment unit can adjust the order of judgment based on the relevance of the learning data at the time of judgment. For example, the judgment unit preferentially judges data with high relevance in test scores. The judgment unit can also preferentially judge data with high relevance in homework submission status. Furthermore, the judgment unit can also preferentially judge data with high relevance in remarks made during class. For example, the judgment unit preferentially judges data with high relevance in test scores. The judgment unit preferentially judges data with high relevance in homework submission status. The judgment unit preferentially judges data with high relevance in remarks made during class. This allows for more appropriate judgment by adjusting the order of judgment based on relevance. Part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the relevance of the learning data to the generation AI and cause the generation AI to adjust the order of judgment.

[0080] The providing unit can estimate the student's emotion and adjust the method of providing the explanation and the exercise based on the estimated emotion of the student. For example, when the student is stressed, the providing unit provides a relaxing explanation and an easy exercise. In addition, when the student is concentrating, the providing unit can also provide a difficult explanation and an exercise. In addition, when the student is tired, the providing unit can also provide a light explanation and an exercise. For example, when the student is stressed, the providing unit provides a relaxing explanation and an easy exercise. When the student is concentrating, the providing unit provides a difficult explanation and an exercise. When the student is tired, the providing unit provides a light explanation and an exercise. This allows for more effective learning support by adjusting the method of providing according to the student's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input emotion data of students into the generating AI and have the generating AI perform emotion estimation.

[0081] The providing unit can adjust the level of detail of the provision based on the importance of the learning content at the time of provision. For example, the providing unit provides detailed explanations and practice questions for important learning content. The providing unit can also provide simple explanations and practice questions for less important learning content. Furthermore, the providing unit can also provide explanations and practice questions with a moderate level of detail according to the importance of the learning content. For example, the providing unit provides detailed explanations and practice questions for important learning content. The providing unit provides simple explanations and practice questions for less important learning content. The providing unit provides explanations and practice questions with a moderate level of detail according to the importance of the learning content. This enables more appropriate learning support by adjusting the level of detail based on the importance of the learning content. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the importance of the learning content to the generating AI and cause the generating AI to adjust the level of detail of the provision.

[0082] The providing unit can apply different provision algorithms according to the category of the learning content at the time of provision. For example, the providing unit applies a specific algorithm to the learning content of mathematics to provide explanations and practice problems. The providing unit can also apply another algorithm to the learning content of English to provide explanations and practice problems. Furthermore, the providing unit can apply yet another algorithm to the learning content of science to provide explanations and practice problems. For example, the providing unit applies a specific algorithm to the learning content of mathematics to provide explanations and practice problems. For the learning content of English to provide explanations and practice problems. For the learning content of science to provide explanations and practice problems, another algorithm is applied. This makes it possible to provide more appropriate learning support by applying different algorithms according to the category of the learning content. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the category of the learning content to the generating AI and cause the generating AI to apply different provision algorithms.

[0083] The providing unit can estimate the emotion of the student and determine the priority of the explanations and exercises to be provided based on the estimated emotion of the student. For example, when the student is stressed, the providing unit can provide easy explanations and exercises preferentially. In addition, when the student is concentrating, the providing unit can also provide explanations and exercises with high difficulty preferentially. In addition, when the student is tired, the providing unit can also provide explanations and exercises with light content preferentially. For example, when the student is stressed, the providing unit can provide easy explanations and exercises preferentially. When the student is concentrating, the providing unit can provide explanations and exercises with high difficulty preferentially. When the student is tired, the providing unit can provide explanations and exercises with light content preferentially. In this way, by determining the priority according to the emotion of the student, more effective learning support is possible. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input emotion data of students into the generating AI and have the generating AI perform emotion estimation.

[0084] The providing unit can determine the priority of provision based on the submission time of the learning content at the time of provision. For example, the providing unit provides explanations and practice questions preferentially for recently submitted learning content. The providing unit can also provide explanations and practice questions preferentially for learning content submitted late. Furthermore, the providing unit can provide explanations and practice questions with appropriate priority based on the submission time. For example, the providing unit provides explanations and practice questions with priority for recently submitted learning content. The providing unit provides explanations and practice questions with priority for learning content submitted late. The providing unit provides explanations and practice questions with appropriate priority based on the submission time. As a result, more effective learning support is possible by determining the priority based on the submission time. Part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the submission time of the learning content to the generating AI and cause the generating AI to determine the priority of provision.

[0085] The providing unit can adjust the order of provision based on the relevance of the learning contents at the time of provision. For example, the providing unit provides explanations and practice questions preferentially for important learning contents. The providing unit can also provide explanations and practice questions preferentially for highly relevant learning contents. Furthermore, the providing unit can provide explanations and practice questions in an appropriate order based on the relevance of the learning contents. For example, the providing unit provides explanations and practice questions preferentially for important learning contents. The providing unit provides explanations and practice questions preferentially for highly relevant learning contents. The providing unit provides explanations and practice questions in an appropriate order based on the relevance of the learning contents. This enables more effective learning support by adjusting the order of provision based on the relevance. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the relevance of the learning contents to the generating AI and cause the generating AI to adjust the order of provision.

[0086] The presentation unit can estimate the student's emotion and adjust the display method of the proficiency level based on the estimated emotion of the student. For example, when the student is nervous, the presentation unit provides a simple and highly visible display method. In addition, when the student is relaxed, the presentation unit can also provide a display method including detailed information. In addition, when the student is in a hurry, the presentation unit can also provide a display method that focuses on the main points. For example, when the student is nervous, the presentation unit provides a simple and highly visible display method. When the student is relaxed, the presentation unit provides a display method including detailed information. When the student is in a hurry, the presentation unit provides a display method that focuses on the main points. This allows for more effective feedback by adjusting the display method according to the student's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the presentation unit can input emotional data of students to the generation AI and have the generation AI perform emotion estimation.

[0087] The presentation unit can select the display method by referring to the student's past learning history at the time of presentation. The presentation unit provides an optimal display method, for example, based on a display method that the student has used with preference in the past. The presentation unit can also select a display method with high visibility from the student's past learning history. The presentation unit can also analyze the student's past learning history and provide the most effective display method. For example, the presentation unit provides an optimal display method based on a display method that the student has used with preference in the past. A display method with high visibility is selected from the student's past learning history. The student's past learning history is analyzed and the most effective display method is provided. This allows for more effective feedback by selecting the optimal display method based on the past learning history. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the student's past learning history data to the generation AI and cause the generation AI to select the optimal display method.

[0088] The presentation unit can apply different display algorithms according to the proficiency category at the time of presentation. The presentation unit applies a specific display algorithm to the proficiency of mathematics, for example. The presentation unit can also apply another display algorithm to the proficiency of English. The presentation unit can also apply yet another display algorithm to the proficiency of science. For example, the presentation unit applies a specific display algorithm to the proficiency of mathematics. The presentation unit applies another display algorithm to the proficiency of English. The presentation unit applies yet another display algorithm to the proficiency of science. This enables more effective feedback by applying different display algorithms according to the proficiency category. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the proficiency category to the generation AI and cause the generation AI to apply the different display algorithms.

[0089] The presentation unit can estimate the student's emotion and adjust the display order of the proficiency levels based on the estimated emotion of the student. For example, when the student is stressed, the presentation unit displays the content in order from easy to difficult. Furthermore, when the student is concentrating, the presentation unit can also display the content in order from difficult to difficult. Furthermore, when the student is tired, the presentation unit can also display the content in order from light to easy. For example, when the student is stressed, the presentation unit displays the content in order from easy to difficult. When the student is concentrating, the presentation unit displays the content in order from difficult to difficult. When the student is tired, the presentation unit displays the content in order from light to easy. In this way, by adjusting the display order according to the student's emotion, more effective feedback can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the presentation unit can input the emotion data of the student to the generation AI and cause the generation AI to execute emotion estimation.

[0090] The presentation unit can select a display method by considering the geographical location information of the student at the time of presentation. For example, when the student lives in a specific area, the presentation unit provides a display method related to the area. In addition, when the student attends a specific school, the presentation unit can also provide a display method related to the school's curriculum. In addition, when the student participates in an event held in a specific area, the presentation unit can also provide a display method related to the event. For example, when the student lives in a specific area, the presentation unit provides a display method related to the area. When the student attends a specific school, the presentation unit provides a display method related to the school's curriculum. When the student participates in an event held in a specific area, the presentation unit provides a display method related to the event. In this way, by considering the geographical location information, a more relevant display method can be provided. Part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the geographical location information of the student to the generation AI and cause the generation AI to select the optimal display method.

[0091] The presentation unit can display related information by analyzing the student's social media activity at the time of presentation. The presentation unit displays related information based on, for example, the learning content shared by the student on social media. The presentation unit can also display related information from education-related accounts followed by the student on social media. The presentation unit can also display related information based on the activity content of the learning group in which the student participates on social media. For example, the presentation unit displays related information based on the learning content shared by the student on social media. The presentation unit displays related information from education-related accounts followed by the student on social media. The presentation unit displays related information based on the activity content of the learning group in which the student participates on social media. This makes it possible to provide more multifaceted information by analyzing social media activity. Part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the student's social media activity data to the generation AI and cause the generation AI to execute display of related information.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The learning support system can further analyze the learning style of each student and provide an individually optimized learning plan. For example, a student who prefers visual learning can be provided with learning materials that make heavy use of videos and infographics. A student who prefers auditory learning can be provided with learning materials in the form of audio commentary or podcasts. Furthermore, a student who prefers hands-on learning can be provided with learning materials that include interactive simulations and experiments. This maximizes the learning effect by providing an optimal learning plan according to the student's learning style.

[0094] The learning support system can also predict future learning content based on the student's learning history and support anticipatory learning. For example, it can analyze past test results and homework submission status to predict what content should be studied next. It can also provide appropriate learning materials and practice questions based on the predicted learning content. It can also conduct tests to check the student's understanding of the predicted learning content and adjust the learning plan based on the results. This makes it possible to anticipate the student's learning progress and support efficient learning.

[0095] The learning support system can also monitor the student's learning environment and provide an optimal learning environment. For example, it can use sensors to detect noise levels and lighting brightness during learning and provide advice on maintaining an appropriate environment. It can also monitor posture during learning and timing of breaks and provide advice on promoting healthy learning habits. It can also adjust the temperature and humidity of the learning environment to provide a comfortable learning environment. This can support students in learning in an optimal environment.

[0096] The learning support system can also set learning goals for students and monitor their achievement. For example, it can record the short-term and long-term learning goals set by students and periodically check their achievement status. It can also visualize the progress toward achieving the goals and provide feedback to students. It can also suggest action plans for achieving the goals and support students to achieve them effectively. This allows students to work effectively toward their learning goals.

[0097] The learning support system can also evaluate students' learning performance in real time and provide immediate feedback. For example, it can instantly analyze the results of online tests and quizzes and provide feedback to students. It can also monitor learning progress in real time and provide advice and support when necessary. In addition, it can provide chatbots and online support to resolve questions and problems during learning in real time. This can quickly solve problems students face during learning and support effective learning.

[0098] The learning support system can further estimate the student's emotions and adjust the learning progress based on the estimated emotions. For example, if the student is stressed, the learning pace can be slowed down. Also, if the student is relaxed, the learning pace can be increased. Furthermore, if the student is focused, the student can be given a more difficult task. In this way, the learning progress can be adjusted according to the student's emotions, thereby supporting more effective learning.

[0099] The learning support system can further estimate the student's emotions and personalize the learning content based on the estimated emotions. For example, if the student is feeling stressed, learning materials with relaxing content can be provided. If the student is relaxed, learning materials with more difficult content can be provided. Furthermore, if the student is concentrating, challenging tasks can be provided. In this way, more effective learning can be supported by personalizing the learning content according to the student's emotions.

[0100] The learning support system can further estimate the student's emotions and provide feedback to improve the student's motivation to learn based on the estimated emotions. For example, if the student is feeling stressed, an encouraging message can be provided. Also, if the student is relaxed, feedback that makes the student feel a sense of accomplishment can be provided. Furthermore, if the student is concentrating, a challenging goal can be set and support can be provided to achieve it. In this way, by providing feedback to improve the student's motivation according to the student's emotions, it is possible to increase the student's motivation to learn.

[0101] The learning support system can further estimate the student's emotions and adjust the timing of study breaks based on the estimated emotions. For example, if a student is tired, it can encourage him or her to take a break at an appropriate time. Also, if a student is concentrating, it can encourage the student to postpone the break and continue studying. Furthermore, if a student is feeling stressed, it can suggest a break that will allow them to relax. In this way, learning efficiency can be improved by adjusting the timing of breaks according to the student's emotions.

[0102] The learning support system can further estimate the student's emotions and adjust how it reports learning progress based on the estimated emotions. For example, if a student is stressed, it can report mainly positive feedback. If a student is relaxed, it can report detailed progress. Furthermore, if a student is focused, it can provide specific advice for taking the next step. This allows for more effective feedback by adjusting how it reports progress according to the student's emotions.

[0103] The process flow of the second embodiment will be briefly described below.

[0104] Step 1: The collection unit collects learning data of students. The learning data of students includes, for example, test scores, homework submission status, and comments made during class. The collection unit automatically obtains test scores from an online platform and obtains homework submission status from a learning management system. In addition, the collection unit converts comments made during class into text data using voice recognition technology and collects the text data. Step 2: The assessment unit assesses the student's proficiency based on the learning data collected by the collection unit. The assessment is based on test scores, homework submission status, and comments made during class. For example, it identifies areas where the student has low test scores, poor homework submission status, or few comments made during class, and assesses that the student's proficiency in those areas is insufficient. Step 3: The provision unit provides the students with explanations and practice problems in the areas where the proficiency level is lacking as determined by the assessment unit. The provision is performed in a text format, a video format, an interactive problem format, or the like. For example, the text format explanation is provided on an online platform, the video format explanation is provided through a streaming service, and the practice problems are provided in the form of interactive problems. Step 4: The presentation unit presents the proficiency level determined by the assessment unit to the student. The presentation is visualized using graphs and charts. For example, the proficiency level is displayed as a bar graph, pie chart, or line graph to enable the student to visually understand.

[0105] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0106] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0107] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0108] For example, the collection unit can collect the learning data of the student using the camera 42 and microphone 38B of the smart device 14. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected learning data to determine the proficiency of the student. For example, the provision unit is realized by the control unit 46A of the smart device 14, and provides appropriate explanations and practice questions for areas in which the determined proficiency is lacking. For example, the presentation unit can visualize and present the proficiency to the student using the display 40A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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, the RAM 30, and the 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.

[0112] 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.

[0113] 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 the voice according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0115] 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 performed in a secure state.

[0116] Fig. 4 shows an example of 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.

[0117] 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.

[0118] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0120] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0121] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0122] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0123] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0124] For example, the collecting unit can collect the learning data of the student using the camera 42 and the microphone 238 of the smart glasses 214. For example, the determining unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected learning data to determine the proficiency of the student. For example, the providing unit is realized by the control unit 46A of the smart glasses 214, and provides appropriate explanations and practice questions for the areas in which the determined proficiency is lacking. For example, the presenting unit can visualize and present the proficiency to the student using the display of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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, the RAM 30, and the 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.

[0128] 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.

[0129] 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 the voice according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0131] 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 performed in a secure state.

[0132] 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.

[0133] 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.

[0134] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0135] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0136] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0137] 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 voice indicating a user input for 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.

[0138] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0139] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0140] For example, the collecting unit can collect the learning data of the student using the camera 42 and the microphone 238 of the headset type terminal 314. For example, the determining unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected learning data to determine the proficiency of the student. For example, the providing unit is realized by the control unit 46A of the headset type terminal 314, and provides appropriate explanations and practice questions for the areas in which the determined proficiency is lacking. For example, the presenting unit can visualize and present the proficiency to the student using the display 343 of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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, the RAM 30, and the 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.

[0144] 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.

[0145] 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 the voice according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0147] 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 performed in a secure state.

[0148] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0149] 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.

[0150] 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.

[0151] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0152] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0153] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0154] 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 a voice indicating a user input for 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.

[0155] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0156] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0157] For example, the collecting unit can collect the learning data of the student using the camera 42 and the microphone 238 of the robot 414. For example, the determining unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected learning data to determine the proficiency of the student. For example, the providing unit is realized by the control unit 46A of the robot 414, and provides appropriate explanations and practice questions for the areas in which the determined proficiency is lacking. For example, the presenting unit can visualize and present the proficiency to the student using the display of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0158] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0159] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0161] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0163] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0165] 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.

[0166] 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 Universal Serial Bus (USB) 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.

[0167] In addition, 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 upon request from the data processing device 12.

[0168] 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.

[0169] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0170] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0171] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0172] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0174] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0176] (Appendix 1) A collection unit that collects learning data of students; a determination unit that determines a proficiency level of the student based on the learning data collected by the collection unit; a providing unit that provides the student with explanations and practice questions in the field in which the proficiency level determined by the determining unit is lacking; A presentation unit that presents the proficiency level determined by the determination unit to the student. A system characterized by: (Appendix 2) The collecting unit includes: Collect data on student test scores, homework turn-in status, and class content in a concrete way 2. The system of claim 1, (Appendix 3) The presentation unit is Visualize proficiency with graphs or charts 2. The system of claim 1, (Appendix 4) The collecting unit includes: To estimate students' emotions in a specific way and adjust the timing of collecting learning data based on the estimated students' emotions. 2. The system of claim 1, (Appendix 5) The collecting unit includes: Analyze students' past learning histories and select data collection methods 2. The system of claim 1, (Appendix 6) The collecting unit includes: When collecting learning data, filter it in a specific way based on the student's current learning status and areas of interest. 2. The system of claim 1, (Appendix 7) The collecting unit includes: Estimate student emotions and prioritize data collection based on estimated student emotions 2. The system of claim 1, (Appendix 8) The collecting unit includes: When collecting learning data, take into account students' geographic location in a specific way to prioritize collecting the most relevant data. 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting learning data, analyze students' social media activities in a specific way and collect relevant data. 2. The system of claim 1, (Appendix 10) The determination unit is Estimate students' emotions in a specific way and adjust the proficiency criteria based on the estimated student emotions. 2. The system of claim 1, (Appendix 11) The determination unit is When making a judgment, the level of detail of the judgment is adjusted based on the importance of the training data. 2. The system of claim 1, (Appendix 12) The determination unit is When making a decision, different decision algorithms are applied depending on the category of the training data. 2. The system of claim 1, (Appendix 13) The determination unit is Estimate students' emotions in a specific way and adjust the way the judgment results are displayed based on the estimated student emotions 2. The system of claim 1, (Appendix 14) The determination unit is At the time of evaluation, the priority of the evaluation is decided based on the time of submission of the training data. 2. The system of claim 1, (Appendix 15) The determination unit is At the time of decision making, the order of decisions is adjusted based on the relevance of the training data. 2. The system of claim 1, (Appendix 16) The providing unit is Estimate students' emotions in a specific way and adjust the way explanations and exercises are provided based on the estimated student emotions. 2. The system of claim 1, (Appendix 17) The providing unit is When delivering, adjust the level of detail of the delivery based on the importance of the learning content 2. The system of claim 1, (Appendix 18) The providing unit is During delivery, different delivery algorithms are applied depending on the category of learning content. 2. The system of claim 1, (Appendix 19) The providing unit is Estimate students' feelings in a specific way and prioritize the explanations and exercises to provide based on the estimated students' feelings. 2. The system of claim 1, (Appendix 20) The providing unit is When delivered, delivery will be prioritized based on when learning content is submitted 2. The system of claim 1, (Appendix 21) The providing unit is As it is delivered, adjust the order of delivery based on the relevance of the learning content 2. The system of claim 1, (Appendix 22) The presentation unit is Estimate student emotions in a specific way and adjust the way proficiency is displayed based on the estimated student emotions 2. The system of claim 1, (Appendix 23) The presentation unit is When presenting, the display method is selected by referring to the student's past learning history. 2. The system of claim 1, (Appendix 24) The presentation unit is At presentation time, different display algorithms are applied depending on the proficiency category 2. The system of claim 1, (Appendix 25) The presentation unit is Estimate students' emotions in a specific way and adjust the display order of proficiency levels based on the estimated students' emotions. 2. The system of claim 1, (Appendix 26) The presentation unit is When presenting, the display method is selected taking into account the student's geographic location information. 2. The system of claim 1, (Appendix 27) The presentation unit is Analyze students' social media activity and display relevant information at presentation time 2. The system of claim 1, [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects learning data of students; a determination unit that determines a proficiency level of the student based on the learning data collected by the collection unit; a providing unit that provides the student with explanations and practice questions in the field in which the proficiency level determined by the determining unit is lacking; A presentation unit that presents the proficiency level determined by the determination unit to the student. A system characterized by:

2. The collecting unit includes: Collect data on the student's test scores, homework submission status, and class comments.

2. The system of claim 1 .

3. The presentation unit is Visualize proficiency with graphs or charts 2. The system of claim 1 .

4. The collecting unit includes: The emotion of the student is estimated in a specific manner, and a timing of collecting learning data is adjusted based on the estimated emotion of the student.

2. The system of claim 1 .

5. The collecting unit includes: Analyze the student's past learning history and select a data collection method.

2. The system of claim 1 .

6. The collecting unit includes: When collecting learning data, filtering is performed based on the student's current learning status and areas of interest.

2. The system of claim 1 .

7. The collecting unit includes: Estimating the student's emotions and prioritizing data to be collected based on the estimated student's emotions.

2. The system of claim 1 .

8. The collecting unit includes: When collecting learning data, the geographic location information of the student is taken into consideration in a specific manner to preferentially collect relevant data.

2. The system of claim 1 .

9. The collecting unit includes: When collecting learning data, analyze the social media activities of the students in a specific manner and collect relevant data.

2. The system of claim 1 .

10. The determination unit is Inferring an emotion of the student and adjusting a proficiency criterion based on the estimated emotion of the student.

2. The system of claim 1 .

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