System

Abstract

An object of a system according to an embodiment is to quickly create an optimal learning material according to a learner's progress and degree of understanding.SOLUTION: A system includes a data collection unit, an analysis unit, and a teaching material generation unit. The data collection unit collects learner data. The analysis unit analyzes the learner data collected by the data collection unit. The teaching material generation unit generates a learning teaching material on the basis of the result analyzed by the analysis 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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] Conventional techniques have had the problem of making it difficult to quickly create optimal learning materials that match a learner's progress and level of understanding.

[0005] The system according to the embodiment aims to quickly create optimal learning materials according to the progress and level of understanding of the learner. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, and a learning material generation unit. The data collection unit collects learner data. The analysis unit analyzes the learner data collected by the data collection unit. The learning material generation unit generates learning materials based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly create optimal learning materials according to the progress and level of understanding of the learner. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The learning material generation system according to an embodiment of the present invention is a system that uses the power of AI to quickly create optimized and personalized learning materials according to the learner's progress and level of understanding, thereby providing each learner with an optimal learning experience.

[0029] A learning material generation system according to an embodiment includes a data collection unit, an analysis unit, and a learning material generation unit. The data collection unit collects learner data. For example, the data collection unit collects information on learners' test results, study time, and comprehension of the learning content. The data collection unit can also collect learner behavior data. For example, the data collection unit collects information on which questions the learner answered correctly, which questions the learner answered incorrectly, and how long the learner spent studying. The analysis unit analyzes the learner data collected by the data collection unit. For example, the analysis unit can use data mining technology to identify the learner's strengths and weaknesses. The analysis unit can also grasp the learner's learning progress using statistical analysis techniques. For example, the analysis unit can analyze the learner's test results and evaluate the learner's learning progress. The learning material generation unit generates learning materials based on the results of the analysis by the analysis unit. For example, the learning material generation unit generates learning materials that are individually customized according to the learner's strengths and weaknesses. The learning material generation unit can also customize the content and format of the learning materials according to the learner's interests. For example, a learner who is good at solving math problems can be provided with more difficult problems, while a learner who is not so good at math problems can be provided with learning materials containing many basic problems. As a result, the learning material generation system according to the embodiment can quickly create learning materials that are optimized and personalized according to the learner's progress and level of understanding. For example, learners can study efficiently at their own pace. Furthermore, learners can understand their strengths and weaknesses and study effectively.

[0030] The data collection unit collects biometric data of the learner, and the analysis unit can analyze the learner's level of concentration and fatigue based on the biometric data. The data collection unit, for example, monitors the learner's heart rate and analyzes the learner's level of concentration and fatigue. For example, the data collection unit evaluates the learner's level of concentration based on heart rate fluctuations and suggests taking a break if fatigue is observed. The data collection unit also uses an electroencephalogram (EEG) sensor to collect the learner's brain wave data and analyzes the learner's level of concentration and fatigue. For example, if a specific brain wave pattern is observed, the learning content is adjusted. The data collection unit also collects the learner's biometric data in real time and analyzes it in association with the learner's progress and level of understanding. For example, more difficult questions are provided during times of high concentration, and easier questions are provided when fatigue is observed. This makes it possible to create a learning plan that takes the learner's level of concentration and fatigue into account.

[0031] The data collection unit collects the learner's social media activity or online behavior history, and the analysis unit can analyze the learner's learning interests and concerns based on the learner's social media activity or online behavior history. The data collection unit, for example, analyzes the learner's social media activity to understand the learner's interests and concerns. For example, the learner's areas of interest are identified based on the topics the learner frequently posts to and the accounts the learner follows. The data collection unit also analyzes the learner's online behavior history to understand the learner's learning interests and concerns. For example, the learner's topics of interest are identified based on the websites the learner frequently visits and their search history. The data collection unit also analyzes the learner's interests and concerns in detail based on the learner's social media activity and online behavior history to create an individual learning plan. For example, the data collection unit provides learning materials related to the topics of interest. This makes it possible to create a learning plan that takes the learner's interests and concerns into consideration.

[0032] The data collection unit collects data on the learner's home environment or lifestyle habits, and the analysis unit can analyze factors that affect learning progress based on the home environment or lifestyle habits. The data collection unit, for example, collects data on the learner's home environment and analyzes factors that affect learning progress. For example, learning progress is evaluated based on the home learning environment and the parents' educational level. The data collection unit also collects data on the learner's lifestyle habits and analyzes factors that affect learning progress. For example, the learner's concentration and comprehension are evaluated based on sleep time and meal quality. The data collection unit also analyzes in detail the factors that affect learning progress based on the data on the home environment and lifestyle habits and creates an individual learning plan. For example, it suggests improvements to lifestyle habits. This makes it possible to create a learning plan that takes the learner's home environment and lifestyle habits into consideration.

[0033] The data collection unit collects data on the learner's friendships or group learning, and the analysis unit can analyze the effectiveness of collaborative learning based on the friendships or group learning. The data collection unit, for example, collects data on the learner's friendships and analyzes the effectiveness of collaborative learning. For example, the effectiveness of collaborative learning is evaluated based on the frequency of learning with friends and the results of joint projects. The data collection unit also collects data on group learning and analyzes the effectiveness of collaborative learning. For example, the effectiveness of collaborative learning is evaluated based on the content and results of group discussions. The data collection unit also analyzes the effectiveness of collaborative learning in detail based on the data on friendships and group learning, and creates individual study plans. For example, it makes suggestions to increase opportunities for collaborative learning. This makes it possible to create study plans that take into account the learner's friendships and the effects of group learning.

[0034] The analysis unit can identify learning methods that were effective in the past based on the learner's learning history, and the teaching material generation unit can generate teaching materials that reflect the identified learning methods. The analysis unit, for example, analyzes the learner's learning history and identifies learning methods that were effective in the past. For example, if a learner achieved high grades using a specific learning method, teaching materials that reflect that method are provided. The analysis unit can also identify effective learning methods based on the learner's learning history and generate teaching materials that reflect those methods. For example, teaching materials that reproduce learning methods that were successful in the past are provided. The analysis unit can also analyze the learner's learning history in detail, identify effective learning methods, and generate teaching materials that reflect those methods. For example, the analysis unit can suggest optimal learning methods based on past learning data. This makes it possible to provide teaching materials that reflect learning methods that were effective in the past.

[0035] The analysis unit analyzes the learner's learning style, and the teaching material generation unit can generate teaching materials in a format that is optimal for that learning style. The analysis unit, for example, analyzes the learner's learning style and provides teaching materials in a format that is optimal for that learning style. For example, visual teaching materials are provided to visual learners, and audio teaching materials are provided to auditory learners. The analysis unit also provides teaching materials in a format that is optimal for the learner's learning style. For example, interactive simulation teaching materials are provided to experiential learners. The analysis unit also analyzes the learner's learning style in detail and provides teaching materials in a format that is optimal for that learning style. For example, customized teaching materials are generated according to the learning style. This makes it possible to provide teaching materials in a format that is optimal for the learner's learning style.

[0036] The analysis unit identifies content related to the learner's hobbies and interests, and the teaching material generation unit can generate teaching materials that incorporate the identified content. For example, the analysis unit incorporates content related to the learner's hobbies and interests into the teaching materials. For example, a learner who likes sports is provided with sports-related math problems. The analysis unit also incorporates content related to the hobbies and interests into the teaching materials to draw out the learner's interest. For example, a learner who likes music is provided with music-related physics problems. The analysis unit also generates teaching materials that draw out the learner's interest in learning based on content related to the learner's hobbies and interests. For example, a learner who likes movies is provided with movie-related history problems. In this way, teaching materials that reflect the learner's hobbies and interests can be provided.

[0037] The analysis unit customizes the content of the teaching materials according to the learner's cultural background and language, and the teaching material generation unit can generate customized teaching materials. The analysis unit customizes the content of the teaching materials according to, for example, the learner's cultural background. For example, it provides examples and cases related to a specific culture. The analysis unit also customizes the content of the teaching materials according to the learner's language. For example, it provides explanations and translations in the learner's native language. The analysis unit also customizes the content of the teaching materials in detail according to the learner's cultural background and language. For example, it provides examples and cases that are culturally appropriate. This makes it possible to provide teaching materials according to the learner's cultural background and language.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The learning material generation system can further customize learning materials based on the learner's hobbies and interests. For example, the data collection unit can collect information on the learner's hobbies, such as sports, music, and art. The analysis unit analyzes the collected hobbies and interests data and identifies learning materials that are likely to interest the learner. The learning material generation unit can generate learning materials that the learner can enjoy while learning, based on the identified hobbies and interests. For example, a learner who likes sports can be provided with sports-related math problems, and a learner who likes music can be provided with music-related physics problems. This can pique the learner's interest and increase their motivation to learn.

[0040] The learning material generation system can further customize the content of the learning materials according to the learner's cultural background and language. For example, the analysis unit customizes the content of the learning materials according to the learner's cultural background. For example, it provides examples and examples related to a particular culture. The analysis unit also customizes the content of the learning materials according to the learner's language. For example, it provides explanations and translations in the learner's native language. The analysis unit also customizes the content of the learning materials in detail according to the learner's cultural background and language. For example, it provides examples and examples that are culturally appropriate. This makes it possible to provide learning materials according to the learner's cultural background and language.

[0041] The learning material generation system can further customize the format of the learning materials according to the learner's learning style. For example, the analysis unit analyzes the learner's learning style and provides the learning materials in the format that is most suitable for that. For example, visual learning materials are provided to visual learners, and audio learning materials are provided to auditory learners. The analysis unit also provides the learning materials in the format that is most suitable for the learner's learning style. For example, interactive simulation learning materials are provided to experiential learners. The analysis unit also analyzes the learner's learning style in detail and provides the learning materials in the format that is most suitable for that. For example, customized learning materials are generated according to the learning style. This makes it possible to provide the learning materials in the format that is most suitable for the learner's learning style.

[0042] The learning material generation system can further identify learning methods that were effective in the past based on the learner's learning history, and the learning material generation unit can generate learning materials that reflect the identified learning methods. The analysis unit, for example, analyzes the learner's learning history and identifies learning methods that were effective in the past. For example, if a learner achieved high grades using a specific learning method, it provides learning materials that reflect that method. The analysis unit can also identify effective learning methods based on the learner's learning history and generate learning materials that reflect those methods. For example, it provides learning materials that reproduce learning methods that were successful in the past. The analysis unit can also analyze the learner's learning history in detail, identify effective learning methods, and generate learning materials that reflect those methods. For example, it can suggest optimal learning methods based on past learning data. This makes it possible to provide learning materials that reflect learning methods that were effective in the past.

[0043] The learning materials generation system further collects data on the learner's home environment or lifestyle habits, and the analysis unit can analyze factors that affect learning progress based on the home environment or lifestyle habits. The data collection unit, for example, collects data on the learner's home environment and analyzes factors that affect learning progress. For example, learning progress is evaluated based on the home learning environment and the parents' educational level. The data collection unit also collects data on the learner's lifestyle habits and analyzes factors that affect learning progress. For example, the data collection unit evaluates the learner's concentration and comprehension based on the amount of sleep and the quality of meals. The data collection unit also analyzes in detail the factors that affect learning progress based on the data on the home environment and lifestyle habits and creates an individual learning plan. For example, it suggests improvements to lifestyle habits. This makes it possible to create a learning plan that takes the learner's home environment and lifestyle habits into consideration.

[0044] The learning material generation system further collects data on the learner's friendships or group learning, and the analysis unit can analyze the effectiveness of collaborative learning based on the friendships or group learning. The data collection unit, for example, collects data on the learner's friendships and analyzes the effectiveness of collaborative learning. For example, the effectiveness of collaborative learning is evaluated based on the frequency of learning with friends and the results of joint projects. The data collection unit also collects data on group learning and analyzes the effectiveness of collaborative learning. For example, the effectiveness of collaborative learning is evaluated based on the content and results of group discussions. The data collection unit also analyzes the effectiveness of collaborative learning in detail based on the data on friendships and group learning, and creates individual learning plans. For example, it makes suggestions to increase opportunities for collaborative learning. This makes it possible to create learning plans that take into account the learner's friendships and the effects of group learning.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The data collection unit collects learner data. For example, the data collection unit collects learner test results, study time, and level of comprehension of the learning content. The data collection unit can also collect learner behavior data. For example, it collects information such as which questions the learner answered correctly, which questions they answered incorrectly, and how long they spent studying. Step 2: The analysis unit analyzes the learner data collected by the data collection unit. For example, the analysis unit may use data mining techniques to identify the learner's strengths and weaknesses. The analysis unit may also use statistical analysis techniques to grasp the learner's learning progress. For example, the analysis unit may analyze the learner's test results and evaluate the learner's learning progress. Step 3: The learning material generation unit generates learning materials based on the results of the analysis by the analysis unit. For example, the learning material generation unit generates learning materials that are individually customized according to the learner's strengths and weaknesses. The learning material generation unit can also customize the content and format of the learning materials according to the learner's interests. For example, a learner who is good at solving math problems can be provided with more difficult problems, and conversely, a learner who is not good at math problems can be provided with learning materials that include many basic problems.

[0047] (Example 2) The learning material generation system according to an embodiment of the present invention is a system that uses the power of AI to quickly create optimized and personalized learning materials according to the learner's progress and level of understanding, thereby providing each learner with an optimal learning experience.

[0048] A learning material generation system according to an embodiment includes a data collection unit, an analysis unit, and a learning material generation unit. The data collection unit collects learner data. For example, the data collection unit collects information on learners' test results, study time, and comprehension of the learning content. The data collection unit can also collect learner behavior data. For example, the data collection unit collects information on which questions the learner answered correctly, which questions the learner answered incorrectly, and how long the learner spent studying. The analysis unit analyzes the learner data collected by the data collection unit. For example, the analysis unit can use data mining technology to identify the learner's strengths and weaknesses. The analysis unit can also grasp the learner's learning progress using statistical analysis techniques. For example, the analysis unit can analyze the learner's test results and evaluate the learner's learning progress. The learning material generation unit generates learning materials based on the results of the analysis by the analysis unit. For example, the learning material generation unit generates learning materials that are individually customized according to the learner's strengths and weaknesses. The learning material generation unit can also customize the content and format of the learning materials according to the learner's interests. For example, a learner who is good at solving math problems can be provided with more difficult problems, while a learner who is not so good at math problems can be provided with learning materials containing many basic problems. As a result, the learning material generation system according to the embodiment can quickly create learning materials that are optimized and personalized according to the learner's progress and level of understanding. For example, learners can study efficiently at their own pace. Furthermore, learners can understand their strengths and weaknesses and study effectively.

[0049] The data collection unit collects emotional data from learners, and the analysis unit can analyze their learning stress levels and motivation based on the emotional data. The data collection unit, for example, analyzes facial expressions and vocal tones to estimate the learner's stress and motivation while studying in real time. For example, a camera and microphone are used to capture changes in the learner's facial expressions and voice and calculate an emotional score. The data collection unit also collects the learner's emotional data and analyzes it in association with their learning progress and level of understanding. For example, if a learner feels stressed about a particular problem, the difficulty of that problem is adjusted. The data collection unit also analyzes the learner's learning stress level and motivation based on the learner's emotional data and creates an individualized study plan. For example, if stress is high, the data collection unit suggests taking a break, and if motivation is low, it displays an encouraging message. This makes it possible to create a study plan that takes the learner's emotions into consideration.

[0050] The data collection unit collects biometric data of the learner, and the analysis unit can analyze the learner's level of concentration and fatigue based on the biometric data. The data collection unit, for example, monitors the learner's heart rate and analyzes the learner's level of concentration and fatigue. For example, the data collection unit evaluates the learner's level of concentration based on heart rate fluctuations and suggests taking a break if fatigue is observed. The data collection unit also uses an electroencephalogram (EEG) sensor to collect the learner's brain wave data and analyzes the learner's level of concentration and fatigue. For example, if a specific brain wave pattern is observed, the learning content is adjusted. The data collection unit also collects the learner's biometric data in real time and analyzes it in association with the learner's progress and level of understanding. For example, more difficult questions are provided during times of high concentration, and easier questions are provided when fatigue is observed. This makes it possible to create a learning plan that takes the learner's level of concentration and fatigue into account.

[0051] The data collection unit collects the learner's social media activity or online behavior history, and the analysis unit can analyze the learner's learning interests and concerns based on the learner's social media activity or online behavior history. The data collection unit, for example, analyzes the learner's social media activity to understand the learner's interests and concerns. For example, the learner's areas of interest are identified based on the topics the learner frequently posts to and the accounts the learner follows. The data collection unit also analyzes the learner's online behavior history to understand the learner's learning interests and concerns. For example, the learner's topics of interest are identified based on the websites the learner frequently visits and their search history. The data collection unit also analyzes the learner's interests and concerns in detail based on the learner's social media activity and online behavior history to create an individual learning plan. For example, the data collection unit provides learning materials related to the topics of interest. This makes it possible to create a learning plan that takes the learner's interests and concerns into consideration.

[0052] The data collection unit collects data on the learner's home environment or lifestyle habits, and the analysis unit can analyze factors that affect learning progress based on the home environment or lifestyle habits. The data collection unit, for example, collects data on the learner's home environment and analyzes factors that affect learning progress. For example, learning progress is evaluated based on the home learning environment and the parents' educational level. The data collection unit also collects data on the learner's lifestyle habits and analyzes factors that affect learning progress. For example, the learner's concentration and comprehension are evaluated based on sleep time and meal quality. The data collection unit also analyzes in detail the factors that affect learning progress based on the data on the home environment and lifestyle habits and creates an individual learning plan. For example, it suggests improvements to lifestyle habits. This makes it possible to create a learning plan that takes the learner's home environment and lifestyle habits into consideration.

[0053] The data collection unit collects data on the learner's friendships or group learning, and the analysis unit can analyze the effectiveness of collaborative learning based on the friendships or group learning. The data collection unit, for example, collects data on the learner's friendships and analyzes the effectiveness of collaborative learning. For example, the effectiveness of collaborative learning is evaluated based on the frequency of learning with friends and the results of joint projects. The data collection unit also collects data on group learning and analyzes the effectiveness of collaborative learning. For example, the effectiveness of collaborative learning is evaluated based on the content and results of group discussions. The data collection unit also analyzes the effectiveness of collaborative learning in detail based on the data on friendships and group learning, and creates individual study plans. For example, it makes suggestions to increase opportunities for collaborative learning. This makes it possible to create study plans that take into account the learner's friendships and the effects of group learning.

[0054] The data collection unit collects emotional data from learners, and the analysis unit can analyze how learners feel about specific learning materials based on the emotional data. The data collection unit, for example, analyzes facial expressions and vocal tones to analyze the emotions learners feel about specific learning materials in real time. For example, a camera and microphone are used to capture changes in the learner's facial expressions and voice and calculate an emotion score. The data collection unit also collects emotional data from learners and analyzes their emotions about specific learning materials. For example, it prioritizes providing learning materials that evoke positive emotions in learners. The data collection unit also performs a detailed analysis of learners' emotions about specific learning materials based on the emotional data and uses the analysis to select learning materials. For example, it avoids learning materials that evoke negative emotions and provides learning materials that evoke positive emotions. This makes it possible to select learning materials that take learners' emotions into consideration.

[0055] The analysis unit can identify the teaching material format that evokes the most positive emotions in the learner based on the learner's emotional data, and the teaching material generation unit can generate teaching materials in the identified teaching material format. The analysis unit, for example, analyzes facial expressions and vocal tones to identify the teaching material format that evokes the most positive emotions in the learner. For example, a camera and a microphone can be used to capture changes in the learner's facial expressions and voice and calculate an emotion score. The analysis unit also collects the learner's emotional data and identifies the teaching material format that evokes the most positive emotions. For example, the analysis unit provides teaching materials in the form of video teaching materials or interactive quizzes. The analysis unit also performs a detailed analysis of the teaching material format that evokes the most positive emotions based on the learner's emotional data and generates teaching materials in that format. For example, the analysis unit preferentially provides teaching material formats that evoke positive emotions. This allows the learner to progress in their studies using a teaching material format that evokes positive emotions.

[0056] The analysis unit can identify learning methods that were effective in the past based on the learner's learning history, and the teaching material generation unit can generate teaching materials that reflect the identified learning methods. The analysis unit, for example, analyzes the learner's learning history and identifies learning methods that were effective in the past. For example, if a learner achieved high grades using a specific learning method, teaching materials that reflect that method are provided. The analysis unit can also identify effective learning methods based on the learner's learning history and generate teaching materials that reflect those methods. For example, teaching materials that reproduce learning methods that were successful in the past are provided. The analysis unit can also analyze the learner's learning history in detail, identify effective learning methods, and generate teaching materials that reflect those methods. For example, the analysis unit can suggest optimal learning methods based on past learning data. This makes it possible to provide teaching materials that reflect learning methods that were effective in the past.

[0057] The analysis unit analyzes the learner's learning style, and the teaching material generation unit can generate teaching materials in a format that is optimal for that learning style. The analysis unit, for example, analyzes the learner's learning style and provides teaching materials in a format that is optimal for that learning style. For example, visual teaching materials are provided to visual learners, and audio teaching materials are provided to auditory learners. The analysis unit also provides teaching materials in a format that is optimal for the learner's learning style. For example, interactive simulation teaching materials are provided to experiential learners. The analysis unit also analyzes the learner's learning style in detail and provides teaching materials in a format that is optimal for that learning style. For example, customized teaching materials are generated according to the learning style. This makes it possible to provide teaching materials in a format that is optimal for the learner's learning style.

[0058] The analysis unit identifies content related to the learner's hobbies and interests, and the teaching material generation unit can generate teaching materials that incorporate the identified content. For example, the analysis unit incorporates content related to the learner's hobbies and interests into the teaching materials. For example, a learner who likes sports is provided with sports-related math problems. The analysis unit also incorporates content related to the hobbies and interests into the teaching materials to draw out the learner's interest. For example, a learner who likes music is provided with music-related physics problems. The analysis unit also generates teaching materials that draw out the learner's interest in learning based on content related to the learner's hobbies and interests. For example, a learner who likes movies is provided with movie-related history problems. In this way, teaching materials that reflect the learner's hobbies and interests can be provided.

[0059] The analysis unit customizes the content of the teaching materials according to the learner's cultural background and language, and the teaching material generation unit can generate customized teaching materials. The analysis unit customizes the content of the teaching materials according to, for example, the learner's cultural background. For example, it provides examples and cases related to a specific culture. The analysis unit also customizes the content of the teaching materials according to the learner's language. For example, it provides explanations and translations in the learner's native language. The analysis unit also customizes the content of the teaching materials in detail according to the learner's cultural background and language. For example, it provides examples and cases that are culturally appropriate. This makes it possible to provide teaching materials according to the learner's cultural background and language.

[0060] The analysis unit can identify the most relaxing environment for the learner based on the learner's emotional data and provide advice to the learner regarding environmental settings. For example, the analysis unit can use a learner's emotion estimation function to identify the most relaxing environment and provide advice to help the learner study in that environment. For example, it can suggest studying in a quiet place or while listening to music. The analysis unit can also identify the most relaxing environment based on the learner's emotional data and provide advice to help the learner study in that environment. For example, it can suggest studying under natural light. The analysis unit can also analyze the learner's emotional data in detail to identify the most relaxing environment and provide advice to help the learner study in that environment. For example, it can suggest a specific scent or temperature setting. This allows the learner to study in a relaxing environment.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The learning material generation system can further customize learning materials based on the learner's hobbies and interests. For example, the data collection unit can collect information on the learner's hobbies, such as sports, music, and art. The analysis unit analyzes the collected hobbies and interests data and identifies learning materials that are likely to interest the learner. The learning material generation unit can generate learning materials that the learner can enjoy while learning, based on the identified hobbies and interests. For example, a learner who likes sports can be provided with sports-related math problems, and a learner who likes music can be provided with music-related physics problems. This can pique the learner's interest and increase their motivation to learn.

[0063] The learning materials generation system can further identify the most relaxing environment for the learner based on the learner's emotional data and provide advice to help the learner study in that environment. For example, the analysis unit can use the learner's emotion estimation function to identify the most relaxing environment and provide advice to help the learner study in that environment. For example, it can suggest studying in a quiet place or while listening to music. The analysis unit can also identify the most relaxing environment based on the learner's emotional data and provide advice to help the learner study in that environment. For example, it can suggest studying under natural light. The analysis unit can also analyze the learner's emotional data in detail to identify the most relaxing environment and provide advice to help the learner study in that environment. For example, it can suggest a specific scent or temperature setting. This allows the learner to study in a relaxing environment.

[0064] The learning material generation system can further customize the content of the learning materials according to the learner's cultural background and language. For example, the analysis unit customizes the content of the learning materials according to the learner's cultural background. For example, it provides examples and examples related to a particular culture. The analysis unit also customizes the content of the learning materials according to the learner's language. For example, it provides explanations and translations in the learner's native language. The analysis unit also customizes the content of the learning materials in detail according to the learner's cultural background and language. For example, it provides examples and examples that are culturally appropriate. This makes it possible to provide learning materials according to the learner's cultural background and language.

[0065] The learning material generation system can further customize the format of the learning materials according to the learner's learning style. For example, the analysis unit analyzes the learner's learning style and provides the learning materials in the format that is most suitable for that. For example, visual learning materials are provided to visual learners, and audio learning materials are provided to auditory learners. The analysis unit also provides the learning materials in the format that is most suitable for the learner's learning style. For example, interactive simulation learning materials are provided to experiential learners. The analysis unit also analyzes the learner's learning style in detail and provides the learning materials in the format that is most suitable for that. For example, customized learning materials are generated according to the learning style. This makes it possible to provide the learning materials in the format that is most suitable for the learner's learning style.

[0066] The learning material generation system can further analyze the learner's emotions toward specific learning materials based on the learner's emotional data. The data collection unit, for example, analyzes facial expressions and vocal tones to analyze the learner's emotions toward specific learning materials in real time. For example, a camera and microphone are used to capture changes in the learner's facial expressions and voice and calculate an emotion score. The data collection unit also collects the learner's emotional data and analyzes their emotions toward specific learning materials. For example, it prioritizes providing learning materials that evoke positive emotions in the learner. The data collection unit also performs a detailed analysis of the learner's emotions toward specific learning materials based on the learner's emotional data and uses this information to select learning materials. For example, it avoids learning materials that evoke negative emotions and provides learning materials that evoke positive emotions. This makes it possible to select learning materials that take the learner's emotions into consideration.

[0067] The learning material generation system can further identify learning methods that were effective in the past based on the learner's learning history, and the learning material generation unit can generate learning materials that reflect the identified learning methods. The analysis unit, for example, analyzes the learner's learning history and identifies learning methods that were effective in the past. For example, if a learner achieved high grades using a specific learning method, it provides learning materials that reflect that method. The analysis unit can also identify effective learning methods based on the learner's learning history and generate learning materials that reflect those methods. For example, it provides learning materials that reproduce learning methods that were successful in the past. The analysis unit can also analyze the learner's learning history in detail, identify effective learning methods, and generate learning materials that reflect those methods. For example, it can suggest optimal learning methods based on past learning data. This makes it possible to provide learning materials that reflect learning methods that were effective in the past.

[0068] The learning material generation system further identifies the learning material format that evokes the most positive emotions in the learner based on the learner's emotional data, and the learning material generation unit can generate learning materials in the identified learning material format. The analysis unit, for example, analyzes facial expressions and vocal tones to identify the learning material format that evokes the most positive emotions in the learner. For example, a camera and a microphone are used to capture changes in the learner's facial expressions and voice and calculate an emotion score. The analysis unit also collects the learner's emotional data and identifies the learning material format that evokes the most positive emotions. For example, it provides learning materials in the form of video learning materials or interactive quizzes. The analysis unit also performs a detailed analysis of the learning material format that evokes the most positive emotions based on the learner's emotional data and generates learning materials in that format. For example, it provides learning material formats that evoke positive emotions preferentially. This allows the learner to progress in learning using a learning material format that evokes positive emotions.

[0069] The learning materials generation system further collects data on the learner's home environment or lifestyle habits, and the analysis unit can analyze factors that affect learning progress based on the home environment or lifestyle habits. The data collection unit, for example, collects data on the learner's home environment and analyzes factors that affect learning progress. For example, learning progress is evaluated based on the home learning environment and the parents' educational level. The data collection unit also collects data on the learner's lifestyle habits and analyzes factors that affect learning progress. For example, the data collection unit evaluates the learner's concentration and comprehension based on the amount of sleep and the quality of meals. The data collection unit also analyzes in detail the factors that affect learning progress based on the data on the home environment and lifestyle habits and creates an individual learning plan. For example, it suggests improvements to lifestyle habits. This makes it possible to create a learning plan that takes the learner's home environment and lifestyle habits into consideration.

[0070] The learning material generation system further collects data on the learner's friendships or group learning, and the analysis unit can analyze the effectiveness of collaborative learning based on the friendships or group learning. The data collection unit, for example, collects data on the learner's friendships and analyzes the effectiveness of collaborative learning. For example, the effectiveness of collaborative learning is evaluated based on the frequency of learning with friends and the results of joint projects. The data collection unit also collects data on group learning and analyzes the effectiveness of collaborative learning. For example, the effectiveness of collaborative learning is evaluated based on the content and results of group discussions. The data collection unit also analyzes the effectiveness of collaborative learning in detail based on the data on friendships and group learning, and creates individual learning plans. For example, it makes suggestions to increase opportunities for collaborative learning. This makes it possible to create learning plans that take into account the learner's friendships and the effects of group learning.

[0071] The learning materials generation system can further analyze the learner's learning stress level and motivation based on the learner's emotional data. The data collection unit, for example, analyzes facial expressions and vocal tone to estimate the learner's stress and motivation while studying in real time. For example, a camera and microphone are used to capture changes in the learner's facial expressions and voice and calculate an emotional score. The data collection unit also collects the learner's emotional data and analyzes it in association with the learner's learning progress and comprehension. For example, if the learner feels stressed about a particular problem, the difficulty of that problem is adjusted. The data collection unit also analyzes the learner's learning stress level and motivation based on the learner's emotional data and creates an individualized study plan. For example, if stress is high, the system may suggest a break, and if motivation is low, it may display an encouraging message. This makes it possible to create a study plan that takes the learner's emotions into consideration.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The data collection unit collects learner data. For example, the data collection unit collects learner test results, study time, and level of comprehension of the learning content. The data collection unit can also collect learner behavior data. For example, it collects information such as which questions the learner answered correctly, which questions they answered incorrectly, and how long they spent studying. Step 2: The analysis unit analyzes the learner data collected by the data collection unit. For example, the analysis unit may use data mining techniques to identify the learner's strengths and weaknesses. The analysis unit may also use statistical analysis techniques to grasp the learner's learning progress. For example, the analysis unit may analyze the learner's test results and evaluate the learner's learning progress. Step 3: The learning material generation unit generates learning materials based on the results of the analysis by the analysis unit. For example, the learning material generation unit generates learning materials that are individually customized according to the learner's strengths and weaknesses. The learning material generation unit can also customize the content and format of the learning materials according to the learner's interests. For example, a learner who is good at solving math problems can be provided with more difficult problems, and conversely, a learner who is not good at math problems can be provided with learning materials that include many basic problems.

[0074] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0075] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0076] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 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.

[0079] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0080] 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.

[0081] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0082] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0083] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0084] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0085] 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.

[0086] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0087] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0088] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0089] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0090] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 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.

[0094] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0095] 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.

[0096] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0097] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0098] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0099] 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.

[0100] 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.

[0101] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0102] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0103] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0104] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The 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.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0115] 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.

[0116] 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.

[0117] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0124] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0125] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0126] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0127] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0128] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0129] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0130] 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.

[0131] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0132] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0133] 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.

[0134] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0135] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0136] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0137] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0138] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0139] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0140] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0141] 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 data collection unit that collects learner data; an analysis unit that analyzes the learner data collected by the data collection unit; a learning material generation unit that generates learning materials based on the results of the analysis by the analysis unit. A system characterized by:

2. The data collection unit Collect learners' emotional data, The analysis unit Analyzing learning stress levels and motivation based on the emotion data 2. The system of claim 1.

3. The data collection unit Collect data on the learner's home environment or lifestyle; The analysis unit Analyzing factors that affect learning progress based on the home environment or lifestyle habits 2. The system of claim 1.

4. The analysis unit Identifying the educational material format that the learner feels most positively about based on the learner's emotional data; The teaching material generation unit Generate teaching materials in the specified teaching material format.

2. The system of claim 1.

5. The analysis unit Analyze the learner's learning style, The teaching material generation unit Generate learning materials in a format that best suits the learning style 2. The system of claim 1.

6. The data collection unit Collect learners' emotional data, The analysis unit Analyzing the learner's feelings toward a specific learning material based on the emotion data 2. The system of claim 1.

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