System

Abstract

An object of a system according to an embodiment is to make it possible to interactively and practically experience learning content using AR technology.SOLUTION: A system includes a multiplication learning part, a display part, a chemical reaction experiment part, and an occupation training part. The multiplication learner provides multiplication learning using the AR technology. The display unit displays the multiplication learning content provided by the multiplication learning unit. The chemical reaction experiment section provides experiments of chemical reactions using the AR technology. The display unit displays the experiment content of the chemical reaction provided by the experiment unit of the chemical reaction. The vocational training portion provides vocational training using AR technology. The display unit displays the occupational training content provided by the occupational training 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 technology has had the problem of making it difficult to experience learning content in an interactive and practical way.

[0005] The system according to the embodiment aims to enable learning content to be experienced interactively and practically using AR technology. [Means for solving the problem]

[0006] The system according to the embodiment includes a multiplication learning unit, a display unit, a chemical reaction experiment unit, and a vocational training unit. The multiplication learning unit provides multiplication learning using AR technology. The display unit displays the multiplication learning content provided by the multiplication learning unit. The chemical reaction experiment unit provides chemical reaction experiments using AR technology. The display unit displays the chemical reaction experiment content provided by the chemical reaction experiment unit. The vocational training unit provides vocational training using AR technology. The display unit displays the vocational training content provided by the vocational training unit. [Effects of the Invention]

[0007] The system according to the embodiment can use AR technology to enable learning content to be experienced interactively and practically. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A learning support system according to an embodiment of the present invention is a system that provides learners with an interactive and practical learning experience using AR technology via smartphones, allowing learners to understand the learning content more concretely and intuitively.

[0029] A learning support system according to an embodiment includes a multiplication learning unit using AR technology, a display unit displaying the multiplication learning content, a chemical reaction experiment unit using AR technology, a display unit displaying the chemical reaction experiment content, a vocational training unit using AR technology, and a display unit displaying the vocational training content. For example, when a smartphone is held over the multiplication learning unit, four digital marbles appear as a 2 x 2 calculation result, and nine digital marbles appear as a 3 x 3 calculation result. For example, when a smartphone is held over the multiplication learning unit, 16 digital marbles appear as a 4 x 4 calculation result. For example, when a smartphone is held over the chemical reaction experiment unit, test tubes and beakers appear in front of the user, and a neutralization reaction experiment begins. As the reaction progresses, the color of the liquid changes and the gas produced is displayed in real time. For example, when a combustion reaction experiment is conducted, the chemical reaction experiment unit can display the color of the flame and the smoke produced in real time as the reaction progresses. For example, when a student holds up a smartphone, the vocational training department can display the internal structure of a distribution board and clearly indicate which breaker to operate. The vocational training department can also display, for example, plumbing work procedures and clearly indicate which pipes to connect. This allows the learning support system to help students understand the learning content more concretely and intuitively.

[0030] When you hold your smartphone over the multiplication learning section, four digital marbles will appear as the result of calculating 2 x 2, and nine digital marbles will appear as the result of calculating 3 x 3. For example, when you hold your smartphone over the multiplication learning section, four digital marbles will appear as the result of calculating 2 x 2. For example, when you hold your smartphone over the multiplication learning section, nine digital marbles will appear as the result of calculating 3 x 3. For example, when you hold your smartphone over the multiplication learning section, 16 digital marbles will appear as the result of calculating 4 x 4. This allows you to visually understand the multiplication of large numbers.

[0031] In the chemical reaction experiment section, when you hold your smartphone over the device, test tubes and beakers appear in your hand, and a neutralization reaction experiment begins. As the reaction progresses, the color of the liquid changes and the gas produced is displayed in real time. In the chemical reaction experiment section, for example, when you hold your smartphone over the device, test tubes and beakers appear in your hand, and a neutralization reaction experiment begins. As the reaction progresses, the color of the liquid changes and the gas produced is displayed in real time. In addition, in the chemical reaction experiment section, for example, a combustion reaction experiment can be performed, and the color of the flame and the smoke produced can be displayed in real time as the reaction progresses. In addition, in the chemical reaction experiment section, for example, an oxidation-reduction reaction experiment can be performed, and the color of the metal changes and the gas produced can be displayed in real time as the reaction progresses. This allows children to visually learn about the progress of chemical reactions.

[0032] When the vocational training department holds up a smartphone, the internal structure of the distribution board is displayed and it is clear which breaker should be operated. When the vocational training department holds up a smartphone, for example, the internal structure of the distribution board is displayed and it is clear which breaker should be operated. The vocational training department can also display, for example, plumbing work procedures and clearly show which pipes should be connected. The vocational training department can also display, for example, electrical work procedures and clearly show which cables should be connected. This allows the work procedures to be learned safely.

[0033] The learning support system includes a progress tracking unit that tracks a learner's progress in real time and automatically generates an individually optimized learning plan. The progress tracking unit, for example, builds a system that tracks a learner's progress in real time as they study on their smartphone. For example, it records study time and correct answer rate and automatically generates an individual learning plan based on that. The progress tracking unit also, for example, analyzes a learner's progress data and develops an algorithm that identifies areas of weakness and strength. For example, if the correct answer rate for a particular problem is low, it proposes a plan to focus on that area. The progress tracking unit also builds a system that tracks progress in real time and dynamically adjusts the learning plan to match the learner's pace. For example, if the learner is tired, it presents more easy questions. This makes it possible to provide an optimal learning plan based on the learner's progress.

[0034] The learning support system includes a history analysis unit that analyzes a learner's past learning history, identifies weak areas, and provides focused support. The history analysis unit, for example, develops a system that stores a learner's past learning history in a database and analyzes that data to identify weak areas. For example, it extracts weak areas based on past test results and study time. Furthermore, the history analysis unit, for example, adds a function that automatically provides learning content specialized for a weak area after identifying the weak area. For example, it recommends videos or problem sets related to a specific area. Furthermore, the history analysis unit, for example, builds a system that generates an individual learning plan to overcome weak areas based on the learning history. For example, it presents many questions related to weak areas to improve comprehension. This makes it possible to identify a learner's weak areas and provide focused support.

[0035] The learning support system includes a voice assistant unit, which works in conjunction with the learning support service and can respond to instructions and questions using the voice assistant. The voice assistant unit, for example, integrates a voice assistant function into the learning support service to build a system that responds to voice instructions and questions. For example, when a learner asks a question by voice, the voice assistant responds. The voice assistant unit also adds, for example, a function that uses the voice assistant to notify the learner of their progress and learning plan by voice. For example, it notifies the learner by voice when the learning time is up. The voice assistant unit also develops, for example, a system that allows the learner to operate learning content by giving voice instructions. For example, when a voice instruction to proceed to the next question is given, the next question is displayed. This makes it possible to respond to voice instructions and questions.

[0036] The learning support system includes a chat unit, which can introduce a chat function that allows learners to communicate with each other in real time, thereby promoting collaborative learning. The chat unit, for example, adds a chat function to a learning support service and builds a system that allows learners to communicate with each other in real time. For example, learners can ask questions and exchange opinions while learning. The chat unit also uses the chat function to promote group learning in which learners work together to solve problems. For example, a group chat can be created for a specific issue and the learners can work together to solve the problem. The chat unit also adds stamps and emoticons to the chat function to support communication between learners. For example, stamps can be used when reporting learning progress. This can promote collaborative learning between learners.

[0037] The learning support system includes an animation display unit, which uses AR technology to display dynamic animations during multiplication learning, allowing a learner to visually understand the calculation process. The animation display unit is configured to build a system that displays dynamic animations using AR technology during multiplication learning, for example. For example, the 2 x 3 calculation process is displayed in animation to allow a learner to visually understand. The animation display unit also adds a function that displays the multiplication calculation process in animation when a learner holds a smartphone over the system. For example, the 2 x 3 calculation process is displayed step by step. The animation display unit is also configured to develop a system that uses dynamic animations to allow a learner to visually understand the multiplication calculation process. For example, the calculation process is expressed using colors and shapes to allow a learner to intuitively understand. This allows a learner to visually understand the multiplication calculation process.

[0038] The learning support system includes a problem creation unit that allows learners to create their own problems and check the answers to those problems using AR. The problem creation unit, for example, builds a system that allows learners to create their own multiplication problems and check the answers to those problems using AR. For example, a learner creates a 2 x 3 problem and holds up a smartphone to display the answer. The problem creation unit also adds, for example, a function to display the answers to problems created by learners using AR. For example, answers to problems created by learners are displayed as animations. The problem creation unit also develops, for example, a system that allows learners to create their own problems and check the answers to those problems using AR. For example, answers to problems created by learners are displayed in colors and shapes. This allows learners to check the answers to problems they created using AR.

[0039] The learning support system includes a mathematics application unit, which applies AR technology to mathematics fields other than multiplication to provide visually easy-to-understand learning content. The mathematics application unit applies AR technology to mathematics fields other than multiplication, for example, to build a system that provides visually easy-to-understand learning content. For example, in learning fractions, the concept of fractions is visually displayed using AR. The mathematics application unit also adds a function that uses AR technology to provide visually easy-to-understand learning content in mathematics fields such as fractions and shapes. For example, the area and perimeter of a shape are displayed using AR. The mathematics application unit also applies AR technology to mathematics fields other than multiplication to develop a system that provides visually easy-to-understand learning content. For example, the fraction calculation process is displayed using AR. This allows AR technology to be applied to mathematics fields other than multiplication to provide visually easy-to-understand learning content.

[0040] The learning support system includes a shared evaluation unit, which allows a learner to share calculation results created using AR with other learners and mutually evaluate them. The shared evaluation unit, for example, builds a system in which a learner shares calculation results created using AR with other learners and allows them to mutually evaluate each other. For example, calculation results created by a learner are shared online and evaluated by other learners. The shared evaluation unit also adds a function in which calculation results created using AR are shared with other learners and mutually evaluated. For example, other learners comment on and evaluate calculation results created by a learner. The shared evaluation unit also develops a system in which a learner shares calculation results created using AR with other learners and allows them to mutually evaluate each other. For example, calculation results created by a learner are shared on social media and evaluated. This allows calculation results created by a learner to be shared with other learners and mutually evaluated.

[0041] The learning support system includes a molecular display unit, which can visually display the reaction process at the molecular level using AR technology in chemical reaction experiments. The molecular display unit is constructed to visually display the reaction process at the molecular level in chemical reaction experiments, for example. For example, the reaction of an acid and a base is displayed at the molecular level, allowing the progress of the reaction to be visually understood. The molecular display unit also adds a function that displays the chemical reaction process at the molecular level when a learner holds their smartphone over the device. For example, the process of the reaction of an acid and a base is displayed in an animation. The molecular display unit is also developed to deepen understanding of chemical reactions by visually displaying the reaction process at the molecular level. For example, the molecular structures of reactants and products are displayed using AR. This makes it possible to visually display the reaction process of a chemical reaction at the molecular level.

[0042] The learning support system includes an experimental condition setting unit, which allows learners to set their own experimental conditions and check the reaction results based on those conditions using AR. The experimental condition setting unit, for example, builds a system that allows learners to set their own experimental conditions and check the reaction results based on those conditions using AR. For example, it sets the temperature and concentration and displays the reaction results under those conditions. The experimental condition setting unit also adds a function that, after setting the experimental conditions, displays the reaction results based on those conditions using AR. For example, it displays an animation of the reaction process at the set temperature and concentration. The experimental condition setting unit also develops a system that allows learners to set their own experimental conditions and check the reaction results based on those conditions using AR. For example, it visually displays the changes in reactants and products under the set conditions. This allows learners to check the reaction results based on the experimental conditions they set using AR.

[0043] The learning support system includes a science application unit, which applies AR technology to scientific fields other than chemical reactions to provide visually easy-to-understand learning content. The science application unit applies AR technology to scientific fields other than chemical reactions, for example, to build a system that provides visually easy-to-understand learning content. For example, in the study of physical phenomena, force vectors are displayed using AR. The science application unit also adds a function to provide visually easy-to-understand learning content using AR technology in scientific fields such as physical phenomena and biological processes. For example, the process of cell division is displayed using AR. The science application unit also applies AR technology to scientific fields other than chemical reactions to develop a system that provides visually easy-to-understand learning content. For example, the operation of electrical circuits is displayed using AR. This makes it possible to apply AR technology to scientific fields other than chemical reactions to provide visually easy-to-understand learning content.

[0044] The learning support system includes a shared evaluation unit, which allows a learner to share the results of an experiment created using AR with other learners and mutually evaluate them. The shared evaluation unit, for example, builds a system in which a learner can share the results of an experiment created using AR with other learners and mutually evaluate them. For example, the results of an experiment created by a learner are shared online and evaluated by other learners. The shared evaluation unit also adds a function in which the results of an experiment created using AR are shared with other learners and mutually evaluated. For example, other learners comment on and evaluate the results of an experiment created by a learner. The shared evaluation unit also develops a system in which a learner can share the results of an experiment created using AR with other learners and mutually evaluate them. For example, the results of an experiment created by a learner can be shared on a social networking site and evaluated. This allows the results of an experiment created by a learner to be shared with other learners and mutually evaluated.

[0045] The learning support system includes a work procedure display unit, which uses AR technology to display work procedures in detail during vocational training, allowing trainees to accurately understand the procedures. The work procedure display unit, for example, is used to build a system for displaying work procedures in detail during vocational training using AR technology. For example, the operation procedures for a distribution board are displayed using AR, allowing trainees to accurately understand the procedures. The work procedure display unit also adds a function that displays the work procedures in detail when the trainee holds their smartphone over the screen. For example, the operation procedures for a distribution board are displayed step by step. The work procedure display unit also develops a system that displays the work procedures in detail, allowing trainees to accurately understand the procedures. For example, the operation procedures are represented by color and shape, allowing trainees to intuitively understand them. This allows trainees to accurately understand the work procedures during vocational training.

[0046] The learning support system includes a work procedure setting unit, which allows trainees to set work procedures themselves and check the work results based on those procedures using AR. The work procedure setting unit, for example, builds a system in which trainees can set work procedures themselves and check the work results based on those procedures using AR. For example, it displays the work results based on the procedures set by the trainee. The work procedure setting unit also adds a function to display the work results based on those procedures using AR after setting the work procedures. For example, it displays the work process based on the set procedures using animation. The work procedure setting unit also develops a system in which trainees can set work procedures themselves and check the work results based on those procedures using AR. For example, it displays the work results based on the set procedures using colors and shapes. This allows trainees to check the work results based on the work procedures they set using AR.

[0047] The learning support system includes a field application unit, which applies AR technology to fields other than vocational training to provide training content that is visually easy to understand. The field application unit, for example, applies AR technology to fields other than vocational training to build a system that provides training content that is visually easy to understand. For example, in medical training, surgical procedures are displayed using AR. The field application unit also adds a function that uses AR technology to provide training content that is visually easy to understand in fields such as medical training and emergency response. For example, cardiopulmonary resuscitation procedures are displayed using AR. The field application unit also applies AR technology to fields other than vocational training to develop a system that provides training content that is visually easy to understand. For example, emergency response procedures are displayed using AR. This makes it possible to apply AR technology to fields other than vocational training to provide training content that is visually easy to understand.

[0048] The learning support system includes a shared evaluation unit, which allows trainees to share work results created using AR with other trainees and mutually evaluate them. The shared evaluation unit, for example, builds a system in which trainees share work results created using AR with other trainees and mutually evaluate them. For example, trainees share work results created online and other trainees evaluate them. The shared evaluation unit also adds a function in which trainees share work results created using AR with other trainees and mutually evaluate them. For example, other trainees comment on and evaluate the work results created by trainees. The shared evaluation unit also develops a system in which trainees share work results created using AR with other trainees and mutually evaluate them. For example, trainees share work results created on social media and receive evaluations. This allows trainees to share work results created by trainees with other trainees and mutually evaluate them.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The learning support system further includes a voice recognition unit that can analyze the learner's speech and respond to voice instructions and questions. For example, when a learner asks a multiplication problem by voice, the voice recognition unit analyzes the problem and displays the appropriate answer. Furthermore, when a learner asks a question by voice about the procedure for a chemical reaction experiment, the voice recognition unit can explain the procedure by voice. Furthermore, when a learner asks a question by voice about an operation procedure in vocational training, for example, the voice recognition unit can guide the procedure by voice. This allows the learner to respond to voice instructions and questions, making the learning experience more interactive.

[0051] The learning support system further includes a feedback unit, which can provide instant feedback to the learner's answers. For example, if the learner answers a multiplication problem correctly, the feedback unit displays "That's correct!", and if the learner answers incorrectly, it displays "Please think about it again." In addition, for example, in a chemical reaction experiment, the feedback unit can display "That's the correct procedure" if the experiment was performed using the correct procedure, and display "Please review the procedure" if the procedure was incorrect. In addition, for example, in vocational training, the feedback unit can display "That's the correct procedure" if the operation was performed correctly, and display "Please review the operation" if the operation was incorrect. This allows the learner to receive instant feedback, improving learning effectiveness.

[0052] The learning support system further includes a virtual assistant unit that can monitor the learner's progress and provide advice and support at the appropriate time. For example, in vocational training, if a learner is taking a long time to perform a particular operation, the virtual assistant unit can provide advice such as, "This is how you should do it." Also, if a learner makes a mistake in a particular procedure, the virtual assistant unit can provide support such as, "That procedure is incorrect. Here's the correct procedure." Furthermore, the virtual assistant unit can suggest the next content the learner should study based on their progress, for example. This allows learners to receive advice and support in real time, improving their learning effectiveness.

[0053] The learning support system further includes a gamification section, which can provide interactive learning content incorporating game elements to allow learners to enjoy learning. For example, solving multiplication problems earns points, which can be used to purchase virtual items. The gamification section can also allow learners to earn badges by successfully completing chemical reaction experiments, which can then be collected. The gamification section can also allow learners to level up and advance to the next stage by performing correct operations in vocational training, for example. This allows learners to progress through learning in a game-like manner, increasing their motivation to learn.

[0054] The learning support system further includes a virtual tour unit, which can provide a function that allows learners to virtually visit various places. For example, in a history class, when a learner holds up their smartphone, an ancient Roman cityscape is displayed, allowing them to virtually visit the place. In a science class, for example, when a learner holds up their smartphone, the virtual tour unit displays outer space, allowing them to virtually explore the universe. In a geography class, for example, when a learner holds up their smartphone, the virtual tour unit displays famous places around the world, allowing them to virtually visit the place. This allows learners to virtually visit various places, enriching their learning experience.

[0055] The learning support system further includes a customization unit, which can provide a function that allows a learner to customize their own learning environment. For example, the learner can select their favorite background image or theme color. The customization unit also adds a function that allows a learner to customize their own learning plan, for example. For example, the learner can progress through the learning content at their own pace. The customization unit also develops a system that allows a learner to set their own learning goals and customize their learning plan based on those goals. For example, the learning content and progress status can be adjusted according to the goals set by the learner. This allows the learner to customize their own learning environment and progress more effectively.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The multiplication learning section uses AR technology to provide multiplication learning content. For example, when you hold your smartphone over the device, four digital marbles will appear as the result of calculating 2 x 2, and nine digital marbles will appear as the result of calculating 3 x 3. You can also use it to calculate 4 x 4 and see 16 digital marbles appear. Step 2: The display unit displays the multiplication learning content provided by the multiplication learning unit, allowing the learner to visually check the multiplication results through AR technology. Step 3: The chemical reaction experiment section uses AR technology to provide chemical reaction experiment content. For example, when you hold your smartphone over the device, test tubes and beakers appear in front of you, and a neutralization reaction experiment begins. As the reaction progresses, the color of the liquid changes and the amount of gas produced is displayed in real time. You can also conduct a combustion reaction experiment, and the color of the flame and the amount of smoke produced can be displayed in real time as the reaction progresses. Step 4: The display unit displays the chemical reaction experiment content provided by the chemical reaction experiment unit, allowing learners to visually check the progress of the chemical reaction through AR technology. Step 5: The vocational training department uses AR technology to provide vocational training content. For example, by holding a smartphone over the device, the internal structure of a distribution board is displayed, showing which breaker to operate. It can also display plumbing work procedures and clearly showing which pipes to connect. Step 6: The display unit displays the vocational training content provided by the vocational training department, allowing learners to visually check the vocational training procedures and operation methods through AR technology.

[0058] (Example 2) A learning support system according to an embodiment of the present invention is a system that provides learners with an interactive and practical learning experience using AR technology via smartphones, allowing learners to understand the learning content more concretely and intuitively.

[0059] A learning support system according to an embodiment includes a multiplication learning unit using AR technology, a display unit displaying the multiplication learning content, a chemical reaction experiment unit using AR technology, a display unit displaying the chemical reaction experiment content, a vocational training unit using AR technology, and a display unit displaying the vocational training content. For example, when a smartphone is held over the multiplication learning unit, four digital marbles appear as a 2 x 2 calculation result, and nine digital marbles appear as a 3 x 3 calculation result. For example, when a smartphone is held over the multiplication learning unit, 16 digital marbles appear as a 4 x 4 calculation result. For example, when a smartphone is held over the chemical reaction experiment unit, test tubes and beakers appear in front of the user, and a neutralization reaction experiment begins. As the reaction progresses, the color of the liquid changes and the gas produced is displayed in real time. For example, when a combustion reaction experiment is conducted, the chemical reaction experiment unit can display the color of the flame and the smoke produced in real time as the reaction progresses. For example, when a student holds up a smartphone, the vocational training department can display the internal structure of a distribution board and clearly indicate which breaker to operate. The vocational training department can also display, for example, plumbing work procedures and clearly indicate which pipes to connect. This allows the learning support system to help students understand the learning content more concretely and intuitively.

[0060] When you hold your smartphone over the multiplication learning section, four digital marbles will appear as the result of calculating 2 x 2, and nine digital marbles will appear as the result of calculating 3 x 3. For example, when you hold your smartphone over the multiplication learning section, four digital marbles will appear as the result of calculating 2 x 2. For example, when you hold your smartphone over the multiplication learning section, nine digital marbles will appear as the result of calculating 3 x 3. For example, when you hold your smartphone over the multiplication learning section, 16 digital marbles will appear as the result of calculating 4 x 4. This allows you to visually understand the multiplication of large numbers.

[0061] In the chemical reaction experiment section, when you hold your smartphone over the device, test tubes and beakers appear in your hand, and a neutralization reaction experiment begins. As the reaction progresses, the color of the liquid changes and the gas produced is displayed in real time. In the chemical reaction experiment section, for example, when you hold your smartphone over the device, test tubes and beakers appear in your hand, and a neutralization reaction experiment begins. As the reaction progresses, the color of the liquid changes and the gas produced is displayed in real time. In addition, in the chemical reaction experiment section, for example, a combustion reaction experiment can be performed, and the color of the flame and the smoke produced can be displayed in real time as the reaction progresses. In addition, in the chemical reaction experiment section, for example, an oxidation-reduction reaction experiment can be performed, and the color of the metal changes and the gas produced can be displayed in real time as the reaction progresses. This allows children to visually learn about the progress of chemical reactions.

[0062] When the vocational training department holds up a smartphone, the internal structure of the distribution board is displayed and it is clear which breaker should be operated. When the vocational training department holds up a smartphone, for example, the internal structure of the distribution board is displayed and it is clear which breaker should be operated. The vocational training department can also display, for example, plumbing work procedures and clearly show which pipes should be connected. The vocational training department can also display, for example, electrical work procedures and clearly show which cables should be connected. This allows the work procedures to be learned safely.

[0063] The learning support system includes a progress tracking unit that tracks a learner's progress in real time and automatically generates an individually optimized learning plan. The progress tracking unit, for example, builds a system that tracks a learner's progress in real time as they study on their smartphone. For example, it records study time and correct answer rate and automatically generates an individual learning plan based on that. The progress tracking unit also, for example, analyzes a learner's progress data and develops an algorithm that identifies areas of weakness and strength. For example, if the correct answer rate for a particular problem is low, it proposes a plan to focus on that area. The progress tracking unit also builds a system that tracks progress in real time and dynamically adjusts the learning plan to match the learner's pace. For example, if the learner is tired, it presents more easy questions. This makes it possible to provide an optimal learning plan based on the learner's progress.

[0064] The learning support system includes a history analysis unit that analyzes a learner's past learning history, identifies weak areas, and provides focused support. The history analysis unit, for example, develops a system that stores a learner's past learning history in a database and analyzes that data to identify weak areas. For example, it extracts weak areas based on past test results and study time. Furthermore, the history analysis unit, for example, adds a function that automatically provides learning content specialized for a weak area after identifying the weak area. For example, it recommends videos or problem sets related to a specific area. Furthermore, the history analysis unit, for example, builds a system that generates an individual learning plan to overcome weak areas based on the learning history. For example, it presents many questions related to weak areas to improve comprehension. This makes it possible to identify a learner's weak areas and provide focused support.

[0065] The learning support system includes an emotion monitoring unit that monitors the learner's emotional state and can provide content for relaxation when the learner feels stressed or fatigued. The emotion monitoring unit, for example, analyzes the learner's facial expressions and voice to build a system that monitors the learner's emotional state in real time. For example, a camera or microphone is used to measure the learner's stress level. The emotion monitoring unit also automatically provides content for relaxation when the learner feels stressed or fatigued, for example, based on emotion estimation data. For example, relaxing music or short break videos are recommended. The emotion monitoring unit also monitors the learner's emotional state and develops a system that temporarily reduces learning content when stress increases. For example, easy questions are posed or a message encouraging the learner to take a break is displayed. This makes it possible to provide appropriate relaxation content according to the learner's emotional state.

[0066] The learning support system includes a voice assistant unit, which works in conjunction with the learning support service and can respond to instructions and questions using the voice assistant. The voice assistant unit, for example, integrates a voice assistant function into the learning support service to build a system that responds to voice instructions and questions. For example, when a learner asks a question by voice, the voice assistant responds. The voice assistant unit also adds, for example, a function that uses the voice assistant to notify the learner of their progress and learning plan by voice. For example, it notifies the learner by voice when the learning time is up. The voice assistant unit also develops, for example, a system that allows the learner to operate learning content by giving voice instructions. For example, when a voice instruction to proceed to the next question is given, the next question is displayed. This makes it possible to respond to voice instructions and questions.

[0067] The learning support system includes a chat unit, which can introduce a chat function that allows learners to communicate with each other in real time, thereby promoting collaborative learning. The chat unit, for example, adds a chat function to a learning support service and builds a system that allows learners to communicate with each other in real time. For example, learners can ask questions and exchange opinions while learning. The chat unit also uses the chat function to promote group learning in which learners work together to solve problems. For example, a group chat can be created for a specific issue and the learners can work together to solve the problem. The chat unit also adds stamps and emoticons to the chat function to support communication between learners. For example, stamps can be used when reporting learning progress. This can promote collaborative learning between learners.

[0068] The learning support system includes an interest identification unit that can identify the topic in which a learner is most interested and provide additional learning content related to that topic. The interest identification unit, for example, analyzes the emotional state of a learner and builds a system that identifies the topic in which the learner is most interested. For example, the interest identification unit measures the level of interest by analyzing facial expressions and voice during learning. The interest identification unit also automatically provides additional learning content related to the topic in which the learner is interested, for example, based on emotion estimation data. For example, it recommends videos and articles related to the topic of interest. The interest identification unit also develops a system that analyzes the learner's interests in real time and dynamically adjusts the learning plan based on the results. For example, it presents many questions related to the topic of interest. This makes it possible to provide additional learning content that matches the learner's interests.

[0069] The learning support system includes an animation display unit, which uses AR technology to display dynamic animations during multiplication learning, allowing a learner to visually understand the calculation process. The animation display unit is configured to build a system that displays dynamic animations using AR technology during multiplication learning, for example. For example, the 2 x 3 calculation process is displayed in animation to allow a learner to visually understand. The animation display unit also adds a function that displays the multiplication calculation process in animation when a learner holds a smartphone over the system. For example, the 2 x 3 calculation process is displayed step by step. The animation display unit is also configured to develop a system that uses dynamic animations to allow a learner to visually understand the multiplication calculation process. For example, the calculation process is expressed using colors and shapes to allow a learner to intuitively understand. This allows a learner to visually understand the multiplication calculation process.

[0070] The learning support system includes a problem creation unit that allows learners to create their own problems and check the answers to those problems using AR. The problem creation unit, for example, builds a system that allows learners to create their own multiplication problems and check the answers to those problems using AR. For example, a learner creates a 2 x 3 problem and holds up a smartphone to display the answer. The problem creation unit also adds, for example, a function to display the answers to problems created by learners using AR. For example, answers to problems created by learners are displayed as animations. The problem creation unit also develops, for example, a system that allows learners to create their own problems and check the answers to those problems using AR. For example, answers to problems created by learners are displayed in colors and shapes. This allows learners to check the answers to problems they created using AR.

[0071] The learning support system includes a game element unit, which can use an emotion estimation function to provide interactive problems incorporating game elements so that learners can enjoy learning. The game element unit, for example, uses the emotion estimation function to build a system that provides interactive problems incorporating game elements so that learners can enjoy learning. For example, it analyzes the learner's facial expressions to determine whether the learner is enjoying themselves. The game element unit also adds, for example, a function to provide interactive problems incorporating game elements so that learners can enjoy learning. For example, it provides a game in which points are accumulated by solving multiplication problems. The game element unit also develops, for example, a system that provides interactive problems incorporating game elements so that learners can enjoy learning based on emotion estimation data. For example, it adjusts the difficulty of the problems depending on the learner's emotional state. This allows learners to enjoy learning.

[0072] The learning support system includes a mathematics application unit, which applies AR technology to mathematics fields other than multiplication to provide visually easy-to-understand learning content. The mathematics application unit applies AR technology to mathematics fields other than multiplication, for example, to build a system that provides visually easy-to-understand learning content. For example, in learning fractions, the concept of fractions is visually displayed using AR. The mathematics application unit also adds a function that uses AR technology to provide visually easy-to-understand learning content in mathematics fields such as fractions and shapes. For example, the area and perimeter of a shape are displayed using AR. The mathematics application unit also applies AR technology to mathematics fields other than multiplication to develop a system that provides visually easy-to-understand learning content. For example, the fraction calculation process is displayed using AR. This allows AR technology to be applied to mathematics fields other than multiplication to provide visually easy-to-understand learning content.

[0073] The learning support system includes a shared evaluation unit, which allows a learner to share calculation results created using AR with other learners and mutually evaluate them. The shared evaluation unit, for example, builds a system in which a learner shares calculation results created using AR with other learners and allows them to mutually evaluate each other. For example, calculation results created by a learner are shared online and evaluated by other learners. The shared evaluation unit also adds a function in which calculation results created using AR are shared with other learners and mutually evaluated. For example, other learners comment on and evaluate calculation results created by a learner. The shared evaluation unit also develops a system in which a learner shares calculation results created using AR with other learners and allows them to mutually evaluate each other. For example, calculation results created by a learner are shared on social media and evaluated. This allows calculation results created by a learner to be shared with other learners and mutually evaluated.

[0074] The learning support system includes an interest identification unit, which uses an emotion estimation function to identify the mathematics topic in which a learner is most interested and can provide additional learning content related to that topic. The interest identification unit, for example, uses the emotion estimation function to build a system that identifies the mathematics topic in which a learner is most interested. For example, it measures the learner's level of interest by analyzing the learner's facial expressions and voice. The interest identification unit also automatically provides additional learning content related to the mathematics topic in which the learner is interested, for example, based on the emotion estimation data. For example, it recommends videos and articles related to the topic of interest. The interest identification unit also develops a system that analyzes the learner's interests in real time and dynamically adjusts the learning plan based on the results. For example, it presents many questions related to the topic of interest. This makes it possible to provide additional learning content in mathematics that matches the learner's interests.

[0075] The learning support system includes a molecular display unit, which can visually display the reaction process at the molecular level using AR technology in chemical reaction experiments. The molecular display unit is constructed to visually display the reaction process at the molecular level in chemical reaction experiments, for example. For example, the reaction of an acid and a base is displayed at the molecular level, allowing the progress of the reaction to be visually understood. The molecular display unit also adds a function that displays the chemical reaction process at the molecular level when a learner holds their smartphone over the device. For example, the process of the reaction of an acid and a base is displayed in an animation. The molecular display unit is also developed to deepen understanding of chemical reactions by visually displaying the reaction process at the molecular level. For example, the molecular structures of reactants and products are displayed using AR. This makes it possible to visually display the reaction process of a chemical reaction at the molecular level.

[0076] The learning support system includes an experimental condition setting unit, which allows learners to set their own experimental conditions and check the reaction results based on those conditions using AR. The experimental condition setting unit, for example, builds a system that allows learners to set their own experimental conditions and check the reaction results based on those conditions using AR. For example, it sets the temperature and concentration and displays the reaction results under those conditions. The experimental condition setting unit also adds a function that, after setting the experimental conditions, displays the reaction results based on those conditions using AR. For example, it displays an animation of the reaction process at the set temperature and concentration. The experimental condition setting unit also develops a system that allows learners to set their own experimental conditions and check the reaction results based on those conditions using AR. For example, it visually displays the changes in reactants and products under the set conditions. This allows learners to check the reaction results based on the experimental conditions they set using AR.

[0077] The learning support system includes an interest identification unit, which uses an emotion estimation function to identify chemical reactions in which a learner is interested and can provide additional experiment content related to those reactions. The interest identification unit, for example, uses the emotion estimation function to build a system that identifies chemical reactions in which a learner is interested. For example, it measures the learner's level of interest by analyzing the learner's facial expressions and voice. The interest identification unit also automatically provides additional experiment content related to chemical reactions in which the learner is interested, for example, based on the emotion estimation data. For example, it recommends videos and articles related to reactions in which the learner is interested. The interest identification unit also develops a system that analyzes the learner's interests in real time and dynamically adjusts the experiment plan based on the results. For example, it provides many experiments related to reactions in which the learner is interested. This makes it possible to provide additional experiment content for chemical reactions that match the learner's interests.

[0078] The learning support system includes a science application unit, which applies AR technology to scientific fields other than chemical reactions to provide visually easy-to-understand learning content. The science application unit applies AR technology to scientific fields other than chemical reactions, for example, to build a system that provides visually easy-to-understand learning content. For example, in the study of physical phenomena, force vectors are displayed using AR. The science application unit also adds a function to provide visually easy-to-understand learning content using AR technology in scientific fields such as physical phenomena and biological processes. For example, the process of cell division is displayed using AR. The science application unit also applies AR technology to scientific fields other than chemical reactions to develop a system that provides visually easy-to-understand learning content. For example, the operation of electrical circuits is displayed using AR. This makes it possible to apply AR technology to scientific fields other than chemical reactions to provide visually easy-to-understand learning content.

[0079] The learning support system includes a shared evaluation unit, which allows a learner to share the results of an experiment created using AR with other learners and mutually evaluate them. The shared evaluation unit, for example, builds a system in which a learner can share the results of an experiment created using AR with other learners and mutually evaluate them. For example, the results of an experiment created by a learner are shared online and evaluated by other learners. The shared evaluation unit also adds a function in which the results of an experiment created using AR are shared with other learners and mutually evaluated. For example, other learners comment on and evaluate the results of an experiment created by a learner. The shared evaluation unit also develops a system in which a learner can share the results of an experiment created using AR with other learners and mutually evaluate them. For example, the results of an experiment created by a learner can be shared on a social networking site and evaluated. This allows the results of an experiment created by a learner to be shared with other learners and mutually evaluated.

[0080] The learning support system includes an interest identification unit, which uses an emotion estimation function to identify the science topic in which the learner is most interested and can provide additional learning content related to that topic. The interest identification unit, for example, uses the emotion estimation function to build a system that identifies the science topic in which the learner is most interested. For example, it measures the learner's level of interest by analyzing the learner's facial expressions and voice. The interest identification unit also automatically provides additional learning content related to the science topic in which the learner is interested, for example, based on the emotion estimation data. For example, it recommends videos and articles related to the topic of interest. The interest identification unit also develops a system that analyzes the learner's interests in real time and dynamically adjusts the learning plan based on the results. For example, it presents many questions related to the topic of interest. This makes it possible to provide additional science learning content that matches the learner's interests.

[0081] The learning support system includes a work procedure display unit, which uses AR technology to display work procedures in detail during vocational training, allowing trainees to accurately understand the procedures. The work procedure display unit, for example, is used to build a system for displaying work procedures in detail during vocational training using AR technology. For example, the operation procedures for a distribution board are displayed using AR, allowing trainees to accurately understand the procedures. The work procedure display unit also adds a function that displays the work procedures in detail when the trainee holds their smartphone over the screen. For example, the operation procedures for a distribution board are displayed step by step. The work procedure display unit also develops a system that displays the work procedures in detail, allowing trainees to accurately understand the procedures. For example, the operation procedures are represented by color and shape, allowing trainees to intuitively understand them. This allows trainees to accurately understand the work procedures during vocational training.

[0082] The learning support system includes a work procedure setting unit, which allows trainees to set work procedures themselves and check the work results based on those procedures using AR. The work procedure setting unit, for example, builds a system in which trainees can set work procedures themselves and check the work results based on those procedures using AR. For example, it displays the work results based on the procedures set by the trainee. The work procedure setting unit also adds a function to display the work results based on those procedures using AR after setting the work procedures. For example, it displays the work process based on the set procedures using animation. The work procedure setting unit also develops a system in which trainees can set work procedures themselves and check the work results based on those procedures using AR. For example, it displays the work results based on the set procedures using colors and shapes. This allows trainees to check the work results based on the work procedures they set using AR.

[0083] The learning support system includes an emotion monitoring unit, which can use an emotion estimation function to provide content to help trainees relax when they feel stressed. The emotion monitoring unit, for example, analyzes the facial expressions and voice of trainees to build a system that monitors their emotional state in real time. For example, it measures the trainee's stress level using a camera or microphone. The emotion monitoring unit also automatically provides content to help trainees relax when they feel stressed, for example, based on the emotion estimation data. For example, it recommends relaxing music or short break videos. The emotion monitoring unit also monitors the trainee's emotional state, for example, and develops a system that temporarily reduces work load when stress increases. For example, it could assign many easy tasks or display a message encouraging trainees to take a break. This makes it possible to provide content to help trainees relax when they feel stressed.

[0084] The learning support system includes a field application unit, which applies AR technology to fields other than vocational training to provide training content that is visually easy to understand. The field application unit, for example, applies AR technology to fields other than vocational training to build a system that provides training content that is visually easy to understand. For example, in medical training, surgical procedures are displayed using AR. The field application unit also adds a function that uses AR technology to provide training content that is visually easy to understand in fields such as medical training and emergency response. For example, cardiopulmonary resuscitation procedures are displayed using AR. The field application unit also applies AR technology to fields other than vocational training to develop a system that provides training content that is visually easy to understand. For example, emergency response procedures are displayed using AR. This makes it possible to apply AR technology to fields other than vocational training to provide training content that is visually easy to understand.

[0085] The learning support system includes a shared evaluation unit, which allows trainees to share work results created using AR with other trainees and mutually evaluate them. The shared evaluation unit, for example, builds a system in which trainees share work results created using AR with other trainees and mutually evaluate them. For example, trainees share work results created online and other trainees evaluate them. The shared evaluation unit also adds a function in which trainees share work results created using AR with other trainees and mutually evaluate them. For example, other trainees comment on and evaluate the work results created by trainees. The shared evaluation unit also develops a system in which trainees share work results created using AR with other trainees and mutually evaluate them. For example, trainees share work results created on social media and receive evaluations. This allows trainees to share work results created by trainees with other trainees and mutually evaluate them.

[0086] The learning support system includes an interest identification unit, which uses an emotion estimation function to identify the training content in which the trainee is most interested and can provide additional training content related to that content. The interest identification unit, for example, uses the emotion estimation function to build a system that identifies the training content in which the trainee is most interested. For example, it measures the level of interest by analyzing the trainee's facial expressions and voice. The interest identification unit also automatically provides additional training content related to the training content in which the trainee is interested, for example, based on the emotion estimation data. For example, it recommends videos and articles related to the training content in which the trainee is interested. The interest identification unit also develops a system that analyzes the trainee's interests in real time and dynamically adjusts the training plan based on the results. For example, it provides many practical exercises related to the training content in which the trainee is interested. This makes it possible to provide additional training content according to the trainee's interests.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The learning support system further includes a voice recognition unit that can analyze the learner's speech and respond to voice instructions and questions. For example, when a learner asks a multiplication problem by voice, the voice recognition unit analyzes the problem and displays the appropriate answer. Furthermore, when a learner asks a question by voice about the procedure for a chemical reaction experiment, the voice recognition unit can explain the procedure by voice. Furthermore, when a learner asks a question by voice about an operation procedure in vocational training, for example, the voice recognition unit can guide the procedure by voice. This allows the learner to respond to voice instructions and questions, making the learning experience more interactive.

[0089] The learning support system further includes a feedback unit, which can provide instant feedback to the learner's answers. For example, if the learner answers a multiplication problem correctly, the feedback unit displays "That's correct!", and if the learner answers incorrectly, it displays "Please think about it again." In addition, for example, in a chemical reaction experiment, the feedback unit can display "That's the correct procedure" if the experiment was performed using the correct procedure, and display "Please review the procedure" if the procedure was incorrect. In addition, for example, in vocational training, the feedback unit can display "That's the correct procedure" if the operation was performed correctly, and display "Please review the operation" if the operation was incorrect. This allows the learner to receive instant feedback, improving learning effectiveness.

[0090] The learning support system can further use the emotion estimation function to adjust learning content based on the learner's emotional state. For example, if a learner is feeling stressed, the chemical reaction experiment section can provide many easy experiments to help the learner relax. The emotion estimation function can also be used to identify chemical reactions that the learner is interested in and provide additional experimental content related to those reactions. For example, videos or articles about reactions that interest the learner can be recommended. The emotion estimation function can also be used to adjust the difficulty of experiments based on the learner's emotional state. For example, if the learner is having fun, more difficult experiments can be provided. This makes it possible to provide an optimal learning experience according to the learner's emotional state.

[0091] The learning support system further includes a virtual assistant unit that can monitor the learner's progress and provide advice and support at the appropriate time. For example, in vocational training, if a learner is taking a long time to perform a particular operation, the virtual assistant unit can provide advice such as, "This is how you should do it." Also, if a learner makes a mistake in a particular procedure, the virtual assistant unit can provide support such as, "That procedure is incorrect. Here's the correct procedure." Furthermore, the virtual assistant unit can suggest the next content the learner should study based on their progress, for example. This allows learners to receive advice and support in real time, improving their learning effectiveness.

[0092] The learning support system can further use the emotion estimation function to dynamically adjust the learning plan based on the learner's emotional state. For example, if the learner is tired, the progress tracking unit can present many easy questions to help the learner relax. The emotion estimation function can also be used to identify topics that the learner is interested in and provide additional learning content related to those topics. For example, videos or articles on topics that interest the learner can be recommended. The emotion estimation function can also be used to adjust the learning pace based on the learner's emotional state. For example, if the learner is having fun, the learning pace can be increased. This makes it possible to provide an optimal learning plan according to the learner's emotional state.

[0093] The learning support system further includes a gamification section, which can provide interactive learning content incorporating game elements to allow learners to enjoy learning. For example, solving multiplication problems earns points, which can be used to purchase virtual items. The gamification section can also allow learners to earn badges by successfully completing chemical reaction experiments, which can then be collected. The gamification section can also allow learners to level up and advance to the next stage by performing correct operations in vocational training, for example. This allows learners to progress through learning in a game-like manner, increasing their motivation to learn.

[0094] The learning support system can further use an emotion estimation function to provide relaxation content based on the learner's emotional state. For example, if a learner is feeling stressed, the emotion monitoring unit can recommend relaxing music or short break videos. Also, if a learner is feeling tired, the emotion monitoring unit can provide many easy questions to help the learner relax. Also, the emotion estimation function can temporarily reduce the learning content based on the learner's emotional state. For example, if a learner is feeling stressed, the learning content can be temporarily reduced to help the learner relax. In this way, appropriate relaxation content can be provided according to the learner's emotional state.

[0095] The learning support system further includes a virtual tour unit, which can provide a function that allows learners to virtually visit various places. For example, in a history class, when a learner holds up their smartphone, an ancient Roman cityscape is displayed, allowing them to virtually visit the place. In a science class, for example, when a learner holds up their smartphone, the virtual tour unit displays outer space, allowing them to virtually explore the universe. In a geography class, for example, when a learner holds up their smartphone, the virtual tour unit displays famous places around the world, allowing them to virtually visit the place. This allows learners to virtually visit various places, enriching their learning experience.

[0096] The learning support system can further use an emotion estimation function to promote collaborative learning based on the emotional state of learners. For example, if learners are having fun, the chat section can utilize the chat function, which allows learners to communicate with each other in real time, to promote group learning for collaborative problem solving. Furthermore, if learners are feeling stressed, the emotion estimation function can be used to display messages for learners to encourage each other. Furthermore, the emotion estimation function can be used to add stamps and emojis to the chat function to support communication between learners based on the learners' emotional state. This promotes collaborative learning among learners and increases their motivation to learn.

[0097] The learning support system further includes a customization unit, which can provide a function that allows a learner to customize their own learning environment. For example, the learner can select their favorite background image or theme color. The customization unit also adds a function that allows a learner to customize their own learning plan, for example. For example, the learner can progress through the learning content at their own pace. The customization unit also develops a system that allows a learner to set their own learning goals and customize their learning plan based on those goals. For example, the learning content and progress status can be adjusted according to the goals set by the learner. This allows the learner to customize their own learning environment and progress more effectively.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The multiplication learning section uses AR technology to provide multiplication learning content. For example, when you hold your smartphone over the device, four digital marbles will appear as the result of calculating 2 x 2, and nine digital marbles will appear as the result of calculating 3 x 3. You can also use it to calculate 4 x 4 and see 16 digital marbles appear. Step 2: The display unit displays the multiplication learning content provided by the multiplication learning unit, allowing the learner to visually check the multiplication results through AR technology. Step 3: The chemical reaction experiment section uses AR technology to provide chemical reaction experiment content. For example, when you hold your smartphone over the device, test tubes and beakers appear in front of you, and a neutralization reaction experiment begins. As the reaction progresses, the color of the liquid changes and the amount of gas produced is displayed in real time. You can also conduct a combustion reaction experiment, and the color of the flame and the amount of smoke produced can be displayed in real time as the reaction progresses. Step 4: The display unit displays the chemical reaction experiment content provided by the chemical reaction experiment unit, allowing learners to visually check the progress of the chemical reaction through AR technology. Step 5: The vocational training department uses AR technology to provide vocational training content. For example, by holding a smartphone over the device, the internal structure of a distribution board is displayed, showing which breaker to operate. It can also display plumbing work procedures and clearly showing which pipes to connect. Step 6: The display unit displays the vocational training content provided by the vocational training department, allowing learners to visually check the vocational training procedures and operation methods through AR technology.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[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 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.

[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 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.

[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 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.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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 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.

[0145] 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.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] 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]

[0167] 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. Multiplication learning section using AR technology, a display unit that displays the multiplication learning content provided by the multiplication learning unit; A chemical reaction experiment using AR technology, a display unit that displays the content of the chemical reaction experiment provided by the chemical reaction experiment unit; Vocational training department using AR technology, a display unit that displays the vocational training content provided by the vocational training unit; A system characterized by:

2. The multiplication learning unit When you hold up your smartphone, four of the digital marbles will appear as a result of a 2 x 2 calculation, and nine of the digital marbles will appear as a result of a 3 x 3 calculation.

2. The system of claim 1.

3. The chemical reaction experimental part When you hold your smartphone over the device, test tubes and beakers appear in front of you, and a neutralization reaction experiment begins. As the reaction progresses, the color of the liquid changes and the gas that is generated is displayed in real time.

2. The system of claim 1.

4. The vocational training department By holding a smartphone over the device, the internal structure of the distribution board is displayed, and it is clear which breaker should be operated.

2. The system of claim 1.

5. It has a progress tracking section, The progress tracking unit Track learner progress in real time and automatically generate personalized learning plans 2. The system of claim 1.

Images