System

Abstract

An object of a system according to an embodiment is to provide individually optimized learning support by accumulating and analyzing learning data of individuals.SOLUTION: A system according to an embodiment includes a learning data accumulation unit, a learning data analysis unit, a homework generation unit, and a hint generation unit. The learning data accumulation unit accumulates personal learning data. The learning data analysis unit analyzes the learning data accumulated by the learning data accumulation unit. The homework generation unit generates a homework based on the result of the analysis by the learning data analysis unit. The hint generation unit generates a hint for a question that the user does not know about the homework assignment generated by the homework assignment generation 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 technologies have had the problem of not being able to consistently accumulate and analyze individual learning data and provide individually optimized learning support.

[0005] The system according to the embodiment aims to accumulate and analyze individual learning data and provide individually optimized learning support. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning data storage unit, a learning data analysis unit, a homework generation unit, and a hint generation unit. The learning data storage unit stores individual learning data. The learning data analysis unit analyzes the learning data stored by the learning data storage unit. The homework generation unit generates homework based on the results of the analysis by the learning data analysis unit. The hint generation unit generates hints for unclear questions in the homework generated by the homework generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can accumulate and analyze individual learning data and provide individually optimized learning support. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​tutoring system according to an embodiment of the present invention is a system that generates an AI tutor that is fully customized for each individual, accumulates and analyzes learning data, and provides optimal learning support. As a result, the AI ​​tutoring system can accumulate and analyze individual learning data over a long period of time, thereby providing optimal support for individual learning needs.

[0029] The AI ​​tutoring system according to the embodiment includes a learning data storage unit, a learning data analysis unit, a homework generation unit, and a hint generation unit. The learning data storage unit stores individual learning data. For example, it collects data such as test results, study time, and learning content and stores it in a database. The learning data storage unit can also record the learner's progress and learning history. The learning data analysis unit analyzes the learning data stored by the learning data storage unit. For example, it identifies the learner's strengths and weaknesses using statistical analysis or machine learning algorithms. The learning data analysis unit can also evaluate the learner's learning style and learning performance. The homework generation unit generates homework based on the results of the analysis by the learning data analysis unit. For example, it may provide more difficult questions for areas of strength and more basic questions for areas of weakness. The homework generation unit can also provide review questions at appropriate times according to the learner's learning progress. The hint generation unit generates hints for questions in the homework generated by the homework generation unit that the learner does not understand. For example, it may provide clues to the answer or reference materials. The hint generation unit can also use the emotion estimation function to analyze the learner's stress level in response to the questions and provide hints to reduce stress. This allows the AI ​​tutoring system according to the embodiment to provide optimal learning support to individuals and improve learning efficiency. For example, it can help learners overcome their weaknesses and further develop their strengths. Furthermore, by monitoring the learner's emotional state and providing appropriate feedback, it is possible to maintain their motivation to learn.

[0030] The learning data analysis unit can analyze an individual's learning style and provide the optimal teaching material format based on that. For example, the learning data analysis unit uses generative AI to analyze an individual's learning style and provide materials that make extensive use of videos and illustrations to students who are good at visual learning. For example, when solving math problems, explanations can be provided using figures and graphs. Students who are good at auditory learning can be provided with materials in the form of audio commentary or podcasts. For example, in a history class, important events can be explained in audio to deepen understanding through listening. Students who are good at tactile learning can be provided with interactive simulations and experiments. For example, in a science class, students can experience chemical reactions through virtual experiments. This makes it possible to provide the optimal teaching materials according to each individual's learning style.

[0031] The learning data analysis unit can predict future learning needs based on an individual's learning history and propose advanced learning plans. In the learning data analysis unit, for example, the generative AI analyzes past learning history and predicts the skills and knowledge that will be required in the future. For example, a student with a solid foundation in mathematics can be given advanced instruction in the basics of calculus. A system can also be built that suggests what to learn next based on learning history. For example, a student who is good at English grammar can be suggested to practice English composition next. The generative AI can also analyze learning history and propose learning plans tailored to future career paths. For example, a student aiming for a career in the medical field can be suggested to advance their learning of biology or chemistry. This makes it possible to predict future learning needs and provide advanced learning plans.

[0032] The system can be applied not only to private tutoring, but also to skill learning in sports, music, and other fields. For example, the generative AI can be applied to sports skill learning, providing training plans tailored to an individual's athletic ability and technical level. For example, it can suggest practice menus to improve soccer dribbling techniques. It can also be applied to music skill learning, providing lesson plans tailored to an individual's performance technique and understanding of music theory. For example, it can suggest practice pieces to improve piano playing techniques. It can also be applied to art and design skill learning, providing tasks tailored to an individual's creativity and technical level. For example, it can suggest sketching tasks to improve painting techniques. This allows the system to be applied to skill learning beyond private tutoring.

[0033] The system can work with other devices in the home to optimize the entire learning environment. For example, the generative AI can work with a smart speaker to provide a study plan via voice instructions. For example, it can provide study time reminders and answer questions via voice. It can also work with a tablet to provide interactive learning materials. For example, it can advance learning through animations that run on the tablet and interactive quizzes. It can also work with smart devices in the home to optimize the learning environment. For example, it can adjust lighting and temperature to create an environment that makes it easier to concentrate. In this way, the system can work with devices in the home to optimize the learning environment.

[0034] The learning data accumulation unit can propose a plan that maximizes learning efficiency by taking into account an individual's lifestyle habits. The learning data accumulation unit, for example, collects and analyzes an individual's lifestyle habits data along with learning data. For example, it proposes the optimal study time by taking into account sleep time and eating patterns. It also proposes a plan to maximize learning efficiency based on the lifestyle habits data. For example, it considers the digestion time after a meal and studies at a time when concentration is highest. It also integrates learning data and lifestyle habits data to provide an individualized learning plan. For example, it proposes a plan to study after exercise by taking advantage of the refreshing effect of exercise. In this way, it is possible to provide a plan that maximizes learning efficiency by taking into account an individual's lifestyle habits.

[0035] The learning data analysis unit can compare an individual's learning performance with other learners of the same age and identify relative strengths and weaknesses. The learning data analysis unit, for example, builds a system that compares an individual's learning performance with learners of the same age based on learning data. For example, it compares test scores and study time to identify relative strengths and weaknesses. It also adjusts an individual's learning plan based on the comparison data with learners of the same age. For example, it provides a learning plan that focuses on areas where the individual is weaker than other learners. It also analyzes the learning data and visually displays the results of the comparison with learners of the same age. For example, it uses graphs and charts to make it possible to understand an individual's learning performance at a glance. This makes it possible to compare an individual's learning performance with other learners of the same age and identify relative strengths and weaknesses.

[0036] The learning data storage unit can improve the overall quality of education by anonymizing and aggregating learning data and providing it to educational institutions and research institutions. The learning data storage unit, for example, builds a system that anonymizes learning data, aggregates it, and provides it to educational institutions and research institutions. For example, it shares learning data while protecting personal information. It also conducts research to improve the overall quality of education based on the anonymized learning data. For example, it identifies teaching materials and teaching methods that are highly effective for learning. It also collaborates with educational institutions and research institutions to implement projects that utilize the anonymized learning data to improve the quality of education. For example, it proposes data-based educational reforms. In this way, it is possible to anonymize learning data and provide it to educational institutions and research institutions, thereby improving the overall quality of education.

[0037] The learning data analysis unit can predict an individual's career path based on the learning data and provide information useful for future career choices. The learning data analysis unit, for example, builds a system that predicts an individual's career path based on the learning data. For example, it suggests future career choices based on areas of expertise and areas of interest. It also analyzes the learning data and provides information useful for future career choices. For example, it suggests a study plan for learning the skills and knowledge required for a specific career. It also predicts an individual's career path based on the learning data and visually displays information useful for future career choices. For example, it simulates career paths and suggests the optimal course. In this way, it is possible to predict an individual's career path based on the learning data and provide information useful for future career choices.

[0038] The homework generation unit can automatically generate questions to review past mistakes based on an individual's learning history. For example, the homework generation unit uses a generation AI to analyze an individual's learning history and automatically generate questions to review questions that were previously incorrect. For example, questions that were incorrectly asked on a math test can be re-asked to deepen understanding. A system can also be built that generates questions to review past mistakes based on learning history. For example, questions are provided that focus on reviewing mistakes made in English grammar questions. The generation AI can also analyze past mistakes and automatically generate review questions. For example, questions related to dates or events that were incorrectly answered on a history test can be re-asked. This makes it possible to automatically generate questions to review past mistakes.

[0039] The homework generation unit can monitor an individual's learning progress in real time and provide review questions at the appropriate time. For example, the homework generation unit builds a system in which a generation AI monitors an individual's learning progress in real time and provides review questions at the appropriate time. For example, review questions are provided when learning progress is behind. Review questions are also provided at the appropriate time based on learning progress. For example, questions are provided to review past mistakes before a test. The generation AI also monitors learning progress in real time and identifies when review is necessary. For example, review questions for the previous unit are provided before learning a new unit. This makes it possible to monitor learning progress in real time and provide review questions at the appropriate time.

[0040] The homework generation unit can automatically generate comprehensive study questions by taking into account the relevance between different subjects. For example, the homework generation unit constructs a system in which a generation AI takes into account the relevance between different subjects and automatically generates comprehensive study questions. For example, it may ask questions that combine knowledge of mathematics and physics. It may also integrate knowledge from different subjects to provide comprehensive study questions. For example, it may ask questions that combine knowledge of history and geography. The generation AI may also analyze the relevance between subjects and automatically generate comprehensive study questions. For example, it may ask questions that explain scientific concepts using English sentences. This makes it possible to automatically generate comprehensive study questions that take into account the relevance between different subjects.

[0041] The homework generation unit generates questions that incorporate the learner's interests and hobbies, thereby increasing their motivation to learn. For example, the homework generation unit constructs a system in which a generation AI analyzes the learner's interests and hobbies and generates questions based on that. For example, a student who likes sports is given math questions related to sports. Questions that incorporate the learner's hobbies and interests are also provided. For example, a student who likes music is given English questions related to music. The generation AI also analyzes the learner's interests and hobbies and generates questions to increase their motivation. For example, a student who likes movies is given history questions that use movie scenes. In this way, questions that incorporate the learner's interests and hobbies are generated, increasing their motivation to learn.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The AI ​​tutoring system can also be equipped with a health management module that monitors the learner's health status and reflects it in the learning plan. For example, it can monitor the learner's heart rate and sleep patterns and provide a learning plan based on their health status. If the learner's heart rate is high, it can suggest a break to relax, and if the learner appears to be sleep deprived, it can shorten the learning time. The health management module can also take into account the learner's dietary and exercise habits and provide advice to promote a balanced lifestyle. This allows the system to provide an optimal learning plan that takes into account the learner's health status.

[0044] The AI ​​tutoring system can also include a communication training module to improve learners' social skills. For example, it can suggest group work for learners to work together with other students to solve problems. The communication training module can also provide practice for learners to improve their presentation skills. For example, it can have learners give presentations to virtual audiences and receive feedback. The communication training module can also provide opportunities for learners to practice logical thinking through debates and discussions. This can provide support for improving learners' social skills.

[0045] The AI ​​tutoring system can also be equipped with a creative task generator to stimulate learners' creativity. For example, it can suggest projects for learners to put their ideas into practice. The creative task generator can also provide tasks to help learners improve their art and design skills. For example, it can develop creativity through the creation of paintings or digital art. The creative task generator can also provide tasks to help learners improve their storytelling and writing skills. For example, it can enhance expressiveness through the creation of short stories or poems. This can provide support to stimulate learners' creativity.

[0046] The AI ​​tutoring system can also have an engagement section that uses the learner's learning data to draw out their interests. For example, it can provide tasks related to topics that interest the learner. The engagement section can also suggest projects that allow the learner to pursue their interests. For example, it can conduct a science experiment that the learner is interested in. The engagement section can also provide resources that allow the learner to deepen their interests. For example, it can introduce related books and websites. This can provide support to draw out the learner's interests.

[0047] The AI ​​tutoring system can also be equipped with a career support section that supports the learner's future career path based on the learner's learning data. For example, it can provide a plan for learning skills related to a job that the learner is interested in. The career support section can also provide assignments for the learner to acquire skills necessary for their future career. For example, it can provide assignments to improve programming skills. The career support section can also provide resources to help the learner prepare for their future career. For example, it can introduce opportunities for internships and work experience. In this way, it can provide support to support the learner's future career path.

[0048] The AI ​​tutoring system can also be equipped with a teaching material customization unit that provides customized teaching materials tailored to the learner's learning style based on the learner's learning data. For example, a student who is good at visual learning can be provided with teaching materials that make extensive use of videos and illustrations. The teaching material customization unit can also adjust the format of the teaching materials according to the learner's learning style. For example, a student who is good at auditory learning can be provided with teaching materials in the form of audio commentary or podcasts. The teaching material customization unit can also analyze the learner's learning style and build a system to provide the optimal teaching materials. For example, a student who is good at tactile learning can be provided with interactive simulations and experiments. This makes it possible to provide the optimal teaching materials tailored to the learner's learning style.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The learning data storage unit stores individual learning data. For example, it collects data such as test results, study time, and learning content and stores it in a database. The learning data storage unit can also record the learner's progress and learning history. Step 2: The learning data analysis unit analyzes the learning data accumulated by the learning data accumulation unit. For example, it uses statistical analysis and machine learning algorithms to identify the learner's strengths and weaknesses. The learning data analysis unit can also evaluate the learner's learning style and learning performance. Step 3: The homework generation unit generates homework based on the results of the analysis by the learning data analysis unit. For example, it may provide more difficult questions for areas where the learner is strong, and more basic questions for areas where the learner is weak. The homework generation unit can also provide review questions at appropriate times according to the learner's learning progress. Step 4: The hint generation unit generates hints for the homework questions generated by the homework generation unit. For example, it provides clues to the answer or reference materials. The hint generation unit can also use the emotion estimation function to analyze the stress level the learner feels about the questions and provide hints to reduce stress.

[0051] (Example 2) The AI ​​tutoring system according to an embodiment of the present invention is a system that generates an AI tutor that is fully customized for each individual, accumulates and analyzes learning data, and provides optimal learning support. As a result, the AI ​​tutoring system can accumulate and analyze individual learning data over a long period of time, thereby providing optimal support for individual learning needs.

[0052] The AI ​​tutoring system according to the embodiment includes a learning data storage unit, a learning data analysis unit, a homework generation unit, and a hint generation unit. The learning data storage unit stores individual learning data. For example, it collects data such as test results, study time, and learning content and stores it in a database. The learning data storage unit can also record the learner's progress and learning history. The learning data analysis unit analyzes the learning data stored by the learning data storage unit. For example, it identifies the learner's strengths and weaknesses using statistical analysis or machine learning algorithms. The learning data analysis unit can also evaluate the learner's learning style and learning performance. The homework generation unit generates homework based on the results of the analysis by the learning data analysis unit. For example, it may provide more difficult questions for areas of strength and more basic questions for areas of weakness. The homework generation unit can also provide review questions at appropriate times according to the learner's learning progress. The hint generation unit generates hints for questions in the homework generated by the homework generation unit that the learner does not understand. For example, it may provide clues to the answer or reference materials. The hint generation unit can also use the emotion estimation function to analyze the learner's stress level in response to the questions and provide hints to reduce stress. This allows the AI ​​tutoring system according to the embodiment to provide optimal learning support to individuals and improve learning efficiency. For example, it can help learners overcome their weaknesses and further develop their strengths. Furthermore, by monitoring the learner's emotional state and providing appropriate feedback, it is possible to maintain their motivation to learn.

[0053] The learning data analysis unit can analyze an individual's learning style and provide the optimal teaching material format based on that. For example, the learning data analysis unit uses generative AI to analyze an individual's learning style and provide materials that make extensive use of videos and illustrations to students who are good at visual learning. For example, when solving math problems, explanations can be provided using figures and graphs. Students who are good at auditory learning can be provided with materials in the form of audio commentary or podcasts. For example, in a history class, important events can be explained in audio to deepen understanding through listening. Students who are good at tactile learning can be provided with interactive simulations and experiments. For example, in a science class, students can experience chemical reactions through virtual experiments. This makes it possible to provide the optimal teaching materials according to each individual's learning style.

[0054] The learning data analysis unit can predict future learning needs based on an individual's learning history and propose advanced learning plans. In the learning data analysis unit, for example, the generative AI analyzes past learning history and predicts the skills and knowledge that will be required in the future. For example, a student with a solid foundation in mathematics can be given advanced instruction in the basics of calculus. A system can also be built that suggests what to learn next based on learning history. For example, a student who is good at English grammar can be suggested to practice English composition next. The generative AI can also analyze learning history and propose learning plans tailored to future career paths. For example, a student aiming for a career in the medical field can be suggested to advance their learning of biology or chemistry. This makes it possible to predict future learning needs and provide advanced learning plans.

[0055] The learning data analysis unit can use the emotion estimation function to monitor the learner's emotional state in real time and suggest break times to reduce stress and fatigue. For example, the learning data analysis unit uses the emotion estimation function to analyze the learner's facial expressions and voice to detect signs of stress and fatigue. For example, it can suggest a break if concentration is declining. In addition, a system can be constructed that monitors the learner's emotional state in real time and suggests appropriate break times. For example, it can suggest a break to relax after a long period of study. Furthermore, based on the emotion estimation data, it can suggest ways to refresh the learner to reduce stress and fatigue. For example, it can suggest short meditations or stretching. In this way, it is possible to monitor the learner's emotional state and suggest appropriate break times.

[0056] The system can be applied not only to private tutoring, but also to skill learning in sports, music, and other fields. For example, the generative AI can be applied to sports skill learning, providing training plans tailored to an individual's athletic ability and technical level. For example, it can suggest practice menus to improve soccer dribbling techniques. It can also be applied to music skill learning, providing lesson plans tailored to an individual's performance technique and understanding of music theory. For example, it can suggest practice pieces to improve piano playing techniques. It can also be applied to art and design skill learning, providing tasks tailored to an individual's creativity and technical level. For example, it can suggest sketching tasks to improve painting techniques. This allows the system to be applied to skill learning beyond private tutoring.

[0057] The system can work with other devices in the home to optimize the entire learning environment. For example, the generative AI can work with a smart speaker to provide a study plan via voice instructions. For example, it can provide study time reminders and answer questions via voice. It can also work with a tablet to provide interactive learning materials. For example, it can advance learning through animations that run on the tablet and interactive quizzes. It can also work with smart devices in the home to optimize the learning environment. For example, it can adjust lighting and temperature to create an environment that makes it easier to concentrate. In this way, the system can work with devices in the home to optimize the learning environment.

[0058] The system can use the emotion estimation function to provide a learning plan that incorporates game elements to increase learners' motivation. For example, the system can use the emotion estimation function to provide a learning plan that incorporates game elements to increase learners' motivation. For example, a system can be introduced that allows learners to earn points and badges according to their learning progress. The system can also monitor the learner's emotional state and provide tasks that incorporate game elements if their motivation is declining. For example, the system can advance learning through quiz-style questions or mini-games. The system can also build a reward system based on the emotion estimation data to increase learners' motivation. For example, the system can provide virtual items or rewards when learning goals are achieved. This makes it possible to provide a learning plan that incorporates game elements to increase learners' motivation.

[0059] The learning data accumulation unit can propose a plan that maximizes learning efficiency by taking into account an individual's lifestyle habits. The learning data accumulation unit, for example, collects and analyzes an individual's lifestyle habits data along with learning data. For example, it proposes the optimal study time by taking into account sleep time and eating patterns. It also proposes a plan to maximize learning efficiency based on the lifestyle habits data. For example, it considers the digestion time after a meal and studies at a time when concentration is highest. It also integrates learning data and lifestyle habits data to provide an individualized learning plan. For example, it proposes a plan to study after exercise by taking advantage of the refreshing effect of exercise. In this way, it is possible to provide a plan that maximizes learning efficiency by taking into account an individual's lifestyle habits.

[0060] The learning data analysis unit can compare an individual's learning performance with other learners of the same age and identify relative strengths and weaknesses. The learning data analysis unit, for example, builds a system that compares an individual's learning performance with learners of the same age based on learning data. For example, it compares test scores and study time to identify relative strengths and weaknesses. It also adjusts an individual's learning plan based on the comparison data with learners of the same age. For example, it provides a learning plan that focuses on areas where the individual is weaker than other learners. It also analyzes the learning data and visually displays the results of the comparison with learners of the same age. For example, it uses graphs and charts to make it possible to understand an individual's learning performance at a glance. This makes it possible to compare an individual's learning performance with other learners of the same age and identify relative strengths and weaknesses.

[0061] The learning data analysis unit can use the emotion estimation function to incorporate emotional fluctuations into the learning data and analyze fluctuations in learning performance influenced by emotions. The learning data analysis unit, for example, uses the emotion estimation function to build a system that incorporates emotional fluctuations into learning data. For example, it records emotional states during learning and analyzes the impact of emotional fluctuations on learning performance. It also integrates learning data and emotional data to analyze fluctuations in learning performance influenced by emotions. For example, it identifies the impact of stress and fatigue on learning efficiency. It also analyzes fluctuations in learning performance based on the emotion estimation data and suggests measures to reduce the impact of emotions. For example, it suggests taking a break to relax when stress increases. This makes it possible to analyze the impact of emotional fluctuations on learning performance.

[0062] The learning data storage unit can improve the overall quality of education by anonymizing and aggregating learning data and providing it to educational institutions and research institutions. The learning data storage unit, for example, builds a system that anonymizes learning data, aggregates it, and provides it to educational institutions and research institutions. For example, it shares learning data while protecting personal information. It also conducts research to improve the overall quality of education based on the anonymized learning data. For example, it identifies teaching materials and teaching methods that are highly effective for learning. It also collaborates with educational institutions and research institutions to implement projects that utilize the anonymized learning data to improve the quality of education. For example, it proposes data-based educational reforms. In this way, it is possible to anonymize learning data and provide it to educational institutions and research institutions, thereby improving the overall quality of education.

[0063] The learning data analysis unit can predict an individual's career path based on the learning data and provide information useful for future career choices. The learning data analysis unit, for example, builds a system that predicts an individual's career path based on the learning data. For example, it suggests future career choices based on areas of expertise and areas of interest. It also analyzes the learning data and provides information useful for future career choices. For example, it suggests a study plan for learning the skills and knowledge required for a specific career. It also predicts an individual's career path based on the learning data and visually displays information useful for future career choices. For example, it simulates career paths and suggests the optimal course. In this way, it is possible to predict an individual's career path based on the learning data and provide information useful for future career choices.

[0064] The learning data analysis unit can use the emotion estimation function to analyze emotional fluctuations based on the learning data and identify when emotional support is needed. The learning data analysis unit, for example, uses the emotion estimation function to build a system that analyzes emotional fluctuations based on the learning data. For example, it records emotional states during learning and identifies when emotional support is needed. It also integrates the learning data and emotion data to identify when emotional support is needed. For example, it suggests taking a break to relax when stress or fatigue increases. It also analyzes emotional fluctuations based on the learning data based on the emotion estimation data and identifies when emotional support is needed. For example, it sends an encouraging message if learning progress is lagging. This makes it possible to analyze emotional fluctuations and identify when emotional support is needed.

[0065] The homework generation unit can automatically generate questions to review past mistakes based on an individual's learning history. For example, the homework generation unit uses a generation AI to analyze an individual's learning history and automatically generate questions to review questions that were previously incorrect. For example, questions that were incorrectly asked on a math test can be re-asked to deepen understanding. A system can also be built that generates questions to review past mistakes based on learning history. For example, questions are provided that focus on reviewing mistakes made in English grammar questions. The generation AI can also analyze past mistakes and automatically generate review questions. For example, questions related to dates or events that were incorrectly answered on a history test can be re-asked. This makes it possible to automatically generate questions to review past mistakes.

[0066] The homework generation unit can monitor an individual's learning progress in real time and provide review questions at the appropriate time. For example, the homework generation unit builds a system in which a generation AI monitors an individual's learning progress in real time and provides review questions at the appropriate time. For example, review questions are provided when learning progress is behind. Review questions are also provided at the appropriate time based on learning progress. For example, questions are provided to review past mistakes before a test. The generation AI also monitors learning progress in real time and identifies when review is necessary. For example, review questions for the previous unit are provided before learning a new unit. This makes it possible to monitor learning progress in real time and provide review questions at the appropriate time.

[0067] The hint generation unit can use the emotion estimation function to analyze the stress level that a learner feels in response to a problem and provide hints to reduce stress. The hint generation unit, for example, uses the emotion estimation function to build a system that analyzes the stress level that a learner feels in response to a problem. For example, the hint generation unit measures the stress level by analyzing facial expressions and voice when solving a problem. Furthermore, based on the learner's stress level, it provides hints to reduce stress. For example, for difficult problems, it provides hints showing the steps to solve the problem. Furthermore, based on the emotion estimation data, it analyzes the stress level that a learner feels in response to a problem and suggests measures to reduce stress. For example, it suggests taking a short break to relax. In this way, it is possible to analyze the stress level that a learner feels in response to a problem and provide hints to reduce stress.

[0068] The homework generation unit can automatically generate comprehensive study questions by taking into account the relevance between different subjects. For example, the homework generation unit constructs a system in which a generation AI takes into account the relevance between different subjects and automatically generates comprehensive study questions. For example, it may ask questions that combine knowledge of mathematics and physics. It may also integrate knowledge from different subjects to provide comprehensive study questions. For example, it may ask questions that combine knowledge of history and geography. The generation AI may also analyze the relevance between subjects and automatically generate comprehensive study questions. For example, it may ask questions that explain scientific concepts using English sentences. This makes it possible to automatically generate comprehensive study questions that take into account the relevance between different subjects.

[0069] The homework generation unit generates questions that incorporate the learner's interests and hobbies, thereby increasing their motivation to learn. For example, the homework generation unit constructs a system in which a generation AI analyzes the learner's interests and hobbies and generates questions based on that. For example, a student who likes sports is given math questions related to sports. Questions that incorporate the learner's hobbies and interests are also provided. For example, a student who likes music is given English questions related to music. The generation AI also analyzes the learner's interests and hobbies and generates questions to increase their motivation. For example, a student who likes movies is given history questions that use movie scenes. In this way, questions that incorporate the learner's interests and hobbies are generated, increasing their motivation to learn.

[0070] The homework generation unit can use the emotion estimation function to adjust the difficulty level according to the learner's emotional state and provide an optimal learning experience. The homework generation unit, for example, uses the emotion estimation function to build a system that analyzes the learner's emotional state and adjusts the difficulty level of the questions. For example, if stress is high, questions with lower difficulty are provided. The difficulty level of the questions is also adjusted in real time according to the learner's emotional state. For example, if concentration is high, difficult questions are provided. The difficulty level is also adjusted according to the learner's emotional state based on the emotion estimation data, providing an optimal learning experience. For example, if fatigue is observed, easy questions are provided to refresh the learner. In this way, the difficulty level can be adjusted according to the learner's emotional state, providing an optimal learning experience.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The AI ​​tutoring system can also be equipped with a health management module that monitors the learner's health status and reflects it in the learning plan. For example, it can monitor the learner's heart rate and sleep patterns and provide a learning plan based on their health status. If the learner's heart rate is high, it can suggest a break to relax, and if the learner appears to be sleep deprived, it can shorten the learning time. The health management module can also take into account the learner's dietary and exercise habits and provide advice to promote a balanced lifestyle. This allows the system to provide an optimal learning plan that takes into account the learner's health status.

[0073] The AI ​​tutoring system can also include a communication training module to improve learners' social skills. For example, it can suggest group work for learners to work together with other students to solve problems. The communication training module can also provide practice for learners to improve their presentation skills. For example, it can have learners give presentations to virtual audiences and receive feedback. The communication training module can also provide opportunities for learners to practice logical thinking through debates and discussions. This can provide support for improving learners' social skills.

[0074] The AI ​​tutoring system can also be equipped with a creative task generator to stimulate learners' creativity. For example, it can suggest projects for learners to put their ideas into practice. The creative task generator can also provide tasks to help learners improve their art and design skills. For example, it can develop creativity through the creation of paintings or digital art. The creative task generator can also provide tasks to help learners improve their storytelling and writing skills. For example, it can enhance expressiveness through the creation of short stories or poems. This can provide support to stimulate learners' creativity.

[0075] The AI ​​tutoring system can also be equipped with an environmental adjustment unit that adjusts the learning environment taking into account the learner's emotional state. For example, if the learner is feeling stressed, it can play relaxing music. The environmental adjustment unit can also adjust the lighting and temperature to improve the learner's concentration. For example, if the learner's concentration is declining, it can brighten the lights and adjust the temperature appropriately. The environmental adjustment unit can also provide advice to customize the learning environment according to the learner's emotional state. For example, it can recommend studying in a quiet environment. This makes it possible to provide an optimal learning environment that takes into account the learner's emotional state.

[0076] The AI ​​tutoring system can also be equipped with a progress adjustment unit that adjusts learning progress by taking into account the learner's emotional state. For example, if the learner is losing motivation, the learning progress can be slowed down. The progress adjustment unit can also adjust the learning pace according to the learner's emotional state. For example, if the learner is concentrating, the learning progress can be accelerated. The progress adjustment unit can also monitor the learner's emotional state and provide appropriate feedback. For example, it can provide feedback that makes the learner feel a sense of accomplishment. This makes it possible to provide optimal learning progress that takes into account the learner's emotional state.

[0077] The AI ​​tutoring system can also have an engagement section that uses the learner's learning data to draw out their interests. For example, it can provide tasks related to topics that interest the learner. The engagement section can also suggest projects that allow the learner to pursue their interests. For example, it can conduct a science experiment that the learner is interested in. The engagement section can also provide resources that allow the learner to deepen their interests. For example, it can introduce related books and websites. This can provide support to draw out the learner's interests.

[0078] The AI ​​tutoring system can also be equipped with a motivation maintenance unit that takes into account the learner's emotional state to maintain their motivation to learn. For example, if the learner is tired, it can send an encouraging message. The motivation maintenance unit can also set learning goals based on the learner's emotional state. For example, it can set small goals that give the learner a sense of accomplishment. The motivation maintenance unit can also monitor the learner's emotional state and provide appropriate rewards. For example, it can provide a virtual item when the learner achieves a goal. This makes it possible to provide optimal motivation maintenance that takes into account the learner's emotional state.

[0079] The AI ​​tutoring system can also be equipped with a career support section that supports the learner's future career path based on the learner's learning data. For example, it can provide a plan for learning skills related to a job that the learner is interested in. The career support section can also provide assignments for the learner to acquire skills necessary for their future career. For example, it can provide assignments to improve programming skills. The career support section can also provide resources to help the learner prepare for their future career. For example, it can introduce opportunities for internships and work experience. In this way, it can provide support to support the learner's future career path.

[0080] The AI ​​tutoring system can also be equipped with a stress reduction unit that takes into account the learner's emotional state to reduce learning stress. For example, if the learner is feeling stressed, it can suggest a relaxation activity. The stress reduction unit can also provide advice to reduce stress depending on the learner's emotional state. For example, it can suggest deep breathing or meditation. The stress reduction unit can also monitor the learner's emotional state and suggest breaks to reduce stress at appropriate times. For example, it can suggest a break to refresh after a long period of study. This makes it possible to provide optimal stress reduction that takes into account the learner's emotional state.

[0081] The AI ​​tutoring system can also be equipped with a teaching material customization unit that provides customized teaching materials tailored to the learner's learning style based on the learner's learning data. For example, a student who is good at visual learning can be provided with teaching materials that make extensive use of videos and illustrations. The teaching material customization unit can also adjust the format of the teaching materials according to the learner's learning style. For example, a student who is good at auditory learning can be provided with teaching materials in the form of audio commentary or podcasts. The teaching material customization unit can also analyze the learner's learning style and build a system to provide the optimal teaching materials. For example, a student who is good at tactile learning can be provided with interactive simulations and experiments. This makes it possible to provide the optimal teaching materials tailored to the learner's learning style.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The learning data storage unit stores individual learning data. For example, it collects data such as test results, study time, and learning content and stores it in a database. The learning data storage unit can also record the learner's progress and learning history. Step 2: The learning data analysis unit analyzes the learning data accumulated by the learning data accumulation unit. For example, it uses statistical analysis and machine learning algorithms to identify the learner's strengths and weaknesses. The learning data analysis unit can also evaluate the learner's learning style and learning performance. Step 3: The homework generation unit generates homework based on the results of the analysis by the learning data analysis unit. For example, it may provide more difficult questions for areas where the learner is strong, and more basic questions for areas where the learner is weak. The homework generation unit can also provide review questions at appropriate times according to the learner's learning progress. Step 4: The hint generation unit generates hints for the homework questions generated by the homework generation unit. For example, it provides clues to the answer or reference materials. The hint generation unit can also use the emotion estimation function to analyze the stress level the learner feels about the questions and provide hints to reduce stress.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] 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.

[0099] 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.

[0100] 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 AI 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] 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 AI 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0120] The 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.

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

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] 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 AI 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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 learning data storage unit that stores individual learning data; a learning data analysis unit that analyzes the learning data stored by the learning data storage unit; a homework generation unit that generates homework based on the results of the analysis by the learning data analysis unit; a hint generating unit that generates hints for unclear questions in the homework generated by the homework generating unit; A system characterized by:

2. The learning data analysis unit Analyzes individual learning styles and provides optimal learning materials based on that 2. The system of claim 1.

3. The learning data analysis unit Based on an individual's learning history, predict future learning needs and propose proactive learning plans 2. The system of claim 1.

4. The learning data analysis unit Monitor learners' emotional state in real time and suggest break times to reduce stress and fatigue 2. The system of claim 1.

5. The system comprises: It is not only applicable to private tutoring, but also to skill learning such as sports and music.

2. The system of claim 1.

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