System

Abstract

An object of the system according to the embodiment is to deepen understanding and interest in a question of a child through an experimental simulation.SOLUTION: A system includes a reception part, a generation part, a display part, and an explanation part. The reception unit inputs a question of the child. The generation part simulates an experiment on the basis of the question input by the reception part. The display unit displays the simulation result generated by the generation unit. The explanation unit explains the simulation result generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not provide sufficient experimental simulations to satisfy children's curiosity and deepen their understanding and interest, and there is room for improvement.

[0005] The system according to the embodiment aims to deepen children's understanding and interest through experimental simulations in response to their questions. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a display unit, and an explanation unit. The reception unit inputs a child's question. The generation unit simulates an experiment based on the question input by the reception unit. The display unit displays the simulation results generated by the generation unit. The explanation unit provides explanations for the simulation results generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can deepen children's understanding and interest through experimental simulations in response to their questions. [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 system according to an embodiment of the present invention uses generative AI to simulate experiments such as chemical reactions, allowing children to participate mentally in real time. This system deepens children's understanding and interest by having the generative AI perform a simulation and display and explain the results, based on children's input questions. For example, if a child inputs, "What happens when you mix an acid and an alkali?", the generative AI simulates the chemical reaction and displays the results as videos and graphs. This allows children to visually understand the results of the experiment without actually performing the experiment. The generative AI also provides explanations about the experimental process and results, deepening children's understanding. For example, the generative AI might explain, "When an acid and an alkali are mixed, a neutralization reaction occurs, producing water and salt." This allows children to gain a deeper understanding of the principles and results of the experiment. This allows the system to resolve children's questions in real time and visually explain the results. For example, when a child inputs a question, the generative AI performs a simulation, displays the results, and explains them, deepening children's understanding and interest.

[0029] The experiment simulation system according to the embodiment includes a reception unit, a generation unit, a display unit, and an explanation unit. The reception unit inputs children's questions. The children's questions include, but are not limited to, scientific questions and questions about daily life. The reception unit can input children's questions using, for example, voice input or a touch screen. The generation unit uses a generation AI to simulate an experiment based on the questions input by the reception unit. The generation unit, for example, references a database of chemical reactions and performs a simulation using a specific algorithm. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and performs a simulation based on the database of chemical reactions. The display unit displays the simulation results generated by the generation unit. The display unit can visually display the results using, for example, videos or graphs. The explanation unit provides explanations about the simulation results generated by the generation unit. The explanation unit, for example, uses the generation AI to provide explanations about the process and results of the experiment. In this way, the experiment simulation system according to the embodiment can solve children's questions in real time and allow them to visually understand the results of the experiment. For example, when a child inputs a question, the generative AI will perform a simulation, display the results, and provide an explanation, deepening the child's understanding and interest.

[0030] The reception unit can input the child's question using voice input or a touch screen. The reception unit can input the child's question using voice input, for example. For example, the reception unit can convert the child's question into text data using voice recognition technology. The reception unit can also input the child's question using a touch screen. For example, the sensitivity of the touch panel and the screen size can be adjusted to allow the child to operate the device intuitively. This allows the child to input their question intuitively.

[0031] The generation unit can refer to the database of chemical reactions and simulate experiments using a specific algorithm. The generation unit, for example, refers to the database of chemical reactions and simulates experiments using a specific algorithm. For example, the generation unit performs simulations using algorithms such as the Monte Carlo method or molecular dynamics simulation. The generation unit can also perform accurate simulations based on the database of chemical reactions. For example, the generation unit performs simulations taking into account the types of chemical reactions included and the frequency of database updates. This makes it possible to perform accurate simulations by referring to the database of chemical reactions.

[0032] The display unit can display the generated simulation results as a video or a graph. The display unit can, for example, display the generated simulation results as a video. For example, the simulation results are visually displayed using animation or live-action footage. The display unit can also display the generated simulation results as a graph. For example, the simulation results are visually displayed using a line graph or a bar graph. In this way, visually displaying the results makes it easier for children to understand.

[0033] The commentary unit can provide commentary on the generated simulation results. For example, the commentary unit uses the generation AI to provide commentary on the experimental process and results. The commentary unit can provide commentary that is easy for children to understand by adjusting the language used and the level of detail of the commentary. For example, the generation AI might explain, "When an acid and an alkali are mixed, a neutralization reaction occurs, producing water and salt." This allows children to deepen their understanding by providing commentary on the simulation results.

[0034] The reception unit can analyze the child's past question history and select the optimal input method. The reception unit, for example, analyzes the child's past question history and selects the optimal input method. For example, it preferentially suggests input methods (voice, text, etc.) that the child has frequently used in the past. It also provides input options related to specific topics from the child's past question history. It also analyzes patterns of questions the child has entered in the past and suggests the optimal input method. In this way, it is possible to provide the optimal input method for the child by analyzing the past question history.

[0035] The reception unit can filter questions based on the child's current learning situation and areas of interest when the question is entered. For example, the reception unit filters questions based on the child's current learning situation and areas of interest when the question is entered. For example, questions related to the topic the child is currently studying can be preferentially entered. Related questions can also be suggested based on the child's areas of interest. Questions of an appropriate level of difficulty can also be entered according to the child's learning progress. In this way, by filtering questions based on the child's learning situation and areas of interest, more relevant questions can be entered.

[0036] The reception unit can select the optimal input means depending on the child's input method when inputting a question. For example, when inputting a question, the reception unit selects the optimal input means depending on the child's input method (voice, text, image, etc.). For example, if the child uses voice input, the question is input using voice recognition technology. If the child uses text input, a keyboard or touch screen is provided. If the child uses image input, the question is input using image recognition technology. This makes it easier for the child to input questions by selecting the optimal means depending on the child's input method.

[0037] When a question is input, the reception unit can prioritize input of highly relevant questions taking into account the child's geographical location information. For example, when a question is input, the reception unit prioritizes input of highly relevant questions taking into account the child's geographical location information. For example, if the child is in a specific location, questions related to that location are prioritized to be displayed. Also, if the child is traveling, questions related to the travel destination are prioritized to be displayed. Also, if the child is at school, questions related to the content of school lessons are prioritized to be displayed. In this way, highly relevant questions can be prioritized to be input by taking into account the geographical location information.

[0038] The reception unit can analyze the child's social media activity when a question is entered and input related questions. For example, the reception unit can analyze the child's social media activity when a question is entered and input related questions. For example, the reception unit can suggest related questions based on content shared by the child on social media. Also, the reception unit can suggest related questions based on the activity of the child's friends on social media. Also, the reception unit can suggest related questions by analyzing the content posted on the child's social media. In this way, related questions can be suggested by analyzing social media activity.

[0039] The reception unit can customize the input method by reflecting the child's past feedback when entering a question. For example, the reception unit customizes the input method by reflecting the child's past feedback when entering a question. For example, the reception unit preferentially provides an input method that the child has used favorably in the past. The input interface is also customized based on the child's past feedback. The reception unit also analyzes patterns of questions entered by the child in the past and suggests the optimal input method. In this way, by reflecting past feedback, the optimal input method for the child can be provided.

[0040] The generation unit can adjust the level of detail of the simulation based on the importance of the experiment when generating the simulation. For example, the generation unit adjusts the level of detail of the simulation based on the importance of the experiment when generating the simulation. For example, for an important experiment, a detailed simulation is generated. For a simple experiment, a simple simulation is generated. The level of detail of the simulation is also adjusted according to the child's level of understanding. In this way, an appropriate simulation can be provided by adjusting the level of detail of the simulation based on the importance of the experiment.

[0041] The generation unit can apply different simulation algorithms depending on the category of the experiment when generating a simulation. For example, the generation unit applies different simulation algorithms depending on the category of the experiment when generating a simulation. For example, a specific chemical algorithm is applied to a simulation of a chemical reaction. Also, an algorithm based on the laws of physics is applied to a simulation of a physical experiment. Also, an algorithm based on biological data is applied to a simulation of a biological experiment. In this way, by applying an appropriate algorithm depending on the category of the experiment, a highly accurate simulation can be provided.

[0042] The generation unit can improve the accuracy of the simulation by referring to the results of the child's past simulations when generating the simulation. For example, the generation unit improves the accuracy of the simulation by referring to the results of the child's past simulations when generating the simulation. For example, the generation unit generates a highly accurate simulation based on the results of the child's past simulations. The generation unit also analyzes the results of the child's past simulations and applies an optimal algorithm. The generation unit also adjusts the simulation parameters by referring to the results of the child's past simulations. In this way, a highly accurate simulation can be provided by referring to the results of the past simulations.

[0043] The generation unit can determine the priority of simulations based on the submission dates of experiments when generating simulations. The generation unit, for example, determines the priority of simulations based on the submission dates of experiments when generating simulations. For example, for experiments with an approaching deadline, the generation unit gives priority to generating simulations. Also, for experiments with a more distant submission date, the generation unit puts the generation of simulations on hold. Also, the generation order of simulations is adjusted according to the submission dates. In this way, by determining the priority of simulations based on the submission dates, simulations can be provided at the appropriate time.

[0044] The generation unit can adjust the order of simulations based on the relevance of the experiments when generating the simulations. The generation unit, for example, adjusts the order of simulations based on the relevance of the experiments when generating the simulations. For example, experiments with high relevance are simulated with priority. Also, experiments with low relevance are simulated later. Also, the order of simulations is adjusted according to the relevance of the experiments. In this way, by adjusting the order of simulations based on the relevance of the experiments, experiments with high relevance can be simulated with priority.

[0045] The generation unit can adjust the use of technical terminology in the simulation according to the child's level of expertise when generating the simulation. For example, the generation unit adjusts the use of technical terminology in the simulation according to the child's level of expertise when generating the simulation. For example, if the child's level of expertise is low, the generation unit generates a simulation using simple terminology. On the other hand, if the child's level of expertise is high, the generation unit generates a simulation using detailed technical terminology. Also, the generation unit adjusts the terminology in the simulation according to the child's level of expertise. In this way, by adjusting the use of technical terminology according to the child's level of expertise, it is possible to provide a simulation that is easy to understand.

[0046] The display unit can adjust the level of detail of the display based on the importance of the simulation result when displaying the result. For example, the display unit adjusts the level of detail of the display based on the importance of the simulation result when displaying the result. For example, for important simulation results, detailed information is displayed. For simple simulation results, simple information is displayed. The level of detail of the display is also adjusted according to the importance of the simulation result. In this way, by adjusting the level of detail of the display based on the importance of the simulation result, appropriate information can be provided.

[0047] The display unit can apply different display algorithms depending on the category of the simulation results when displaying the results. For example, the display unit applies different display algorithms depending on the category of the simulation results when displaying the results. For example, a specific display algorithm is applied to the simulation results of a chemical reaction. A display algorithm based on physical laws is applied to the simulation results of a physical experiment. A display algorithm based on biological data is applied to the simulation results of a biological experiment. In this way, by applying an appropriate display algorithm depending on the category of the simulation results, it is possible to provide an easy-to-understand display.

[0048] The display unit can determine the display priority based on the submission time of the simulation results when displaying them. The display unit, for example, determines the display priority based on the submission time of the simulation results when displaying them. For example, simulation results with an approaching deadline are displayed preferentially. Simulation results with a more distant submission time are displayed later. The display order is also adjusted depending on the submission time. In this way, by determining the display priority based on the submission time, it is possible to provide display at an appropriate time.

[0049] The display unit can adjust the display order based on the relevance of the simulation results when displaying them. The display unit, for example, adjusts the display order based on the relevance of the simulation results when displaying them. For example, highly relevant simulation results are displayed preferentially. Also, less relevant simulation results are displayed later. Also, the display order is adjusted according to the relevance of the simulation results. In this way, by adjusting the display order based on the relevance of the simulation results, highly relevant results can be displayed preferentially.

[0050] The display unit can adjust the use of technical terms in the display according to the child's level of expertise when displaying. For example, the display unit adjusts the use of technical terms in the display according to the child's level of expertise when displaying. For example, if the child's level of expertise is low, simple terms are used for display. On the other hand, if the child's level of expertise is high, detailed technical terms are used for display. Also, the display terms are adjusted according to the child's level of expertise. In this way, by adjusting the use of technical terms according to the child's level of expertise, it is possible to provide a display that is easy to understand.

[0051] The commentary unit can adjust the level of detail of the commentary based on the importance of the simulation result when providing the commentary. For example, the commentary unit adjusts the level of detail of the commentary based on the importance of the simulation result when providing the commentary. For example, in the case of an important simulation result, a detailed commentary is provided. In addition, in the case of a simple simulation result, a simple commentary is provided. In addition, the level of detail of the commentary is adjusted according to the importance of the simulation result. In this way, by adjusting the level of detail of the commentary based on the importance of the simulation result, appropriate information can be provided.

[0052] The commentary unit can apply different commentary algorithms depending on the category of the simulation results when providing commentary. For example, the commentary unit applies different commentary algorithms depending on the category of the simulation results when providing commentary. For example, a specific commentary algorithm is applied to the simulation results of a chemical reaction. Also, an commentary algorithm based on physical laws is applied to the simulation results of a physical experiment. Also, an commentary algorithm based on biological data is applied to the simulation results of a biological experiment. In this way, by applying an appropriate commentary algorithm depending on the category of the simulation results, it is possible to provide easy-to-understand commentary.

[0053] The commentary unit can improve the accuracy of the commentary by referring to the child's past commentary results when providing commentary. For example, the commentary unit improves the accuracy of the commentary by referring to the child's past commentary results when providing commentary. For example, the commentary unit provides a highly accurate commentary based on the commentary results the child received in the past. The commentary unit also analyzes the child's past commentary results and applies an optimal commentary algorithm. The commentary parameters are also adjusted by referring to the child's past commentary results. In this way, a highly accurate commentary can be provided by referring to the past commentary results.

[0054] The commentary unit can determine the priority of commentary based on the submission time of the simulation results when providing commentary. For example, the commentary unit determines the priority of commentary based on the submission time of the simulation results when providing commentary. For example, in the case of simulation results whose deadline is approaching, commentary is provided preferentially. In addition, in the case of simulation results whose submission time is further away, commentary is provided later. In addition, the order of commentary is adjusted depending on the submission time. In this way, by determining the priority of commentary based on the submission time, commentary can be provided at the appropriate time.

[0055] The commentary unit can adjust the order of commentary based on the relevance of the simulation results when providing commentary. For example, the commentary unit adjusts the order of commentary based on the relevance of the simulation results when providing commentary. For example, highly relevant simulation results are given priority in commentary. Also, less relevant simulation results are given priority in commentary. Also, the order of commentary is adjusted according to the relevance of the simulation results. In this way, by adjusting the order of commentary based on the relevance of the simulation results, highly relevant results can be given priority in commentary.

[0056] The explanation unit can adjust the use of technical terms in the explanation according to the child's level of expertise when providing explanations. For example, the explanation unit adjusts the use of technical terms in the explanation according to the child's level of expertise when providing explanations. For example, if the child's level of expertise is low, simple terms are used for the explanation. On the other hand, if the child's level of expertise is high, detailed technical terms are used for the explanation. Also, the terms in the explanation are adjusted according to the child's level of expertise. In this way, by adjusting the use of technical terms according to the child's level of expertise, explanations that are easy to understand can be provided.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] When a child enters a question, the reception unit can provide an appropriate input interface based on the child's age and grade level. For example, it provides large icons and simple menus for young children, while providing detailed options and text input for elementary school children. The reception unit can also select visual input methods or voice input depending on the child's learning style. This makes entering questions more intuitive and easier by providing an appropriate input interface based on the child's age and grade level.

[0059] When generating simulation results, the generation unit can refer to the child's past learning history and provide a simulation that matches the child's level of understanding. For example, if a simulation on the same topic has been conducted in the past, a more advanced simulation can be provided based on that result. Also, if a child is interested in a particular field, simulations related to that field can be provided preferentially. This makes it possible to provide more effective simulations based on the child's learning history.

[0060] The display unit can also provide a customized display method according to the child's visual preferences when displaying the simulation results. For example, if the child likes animations, the simulation results can be displayed in animation format. Alternatively, if the child likes graphs and charts, the simulation results can be displayed in graphs and charts. Furthermore, the display unit can adjust colors according to the child's color vision characteristics. This allows the child to better understand the simulation results by providing a display method according to the child's visual preferences.

[0061] The explanation section can incorporate storytelling elements to attract children's interest when explaining the simulation results. For example, when explaining the results of a chemical reaction simulation, characters can be used to explain in a story format. The explanation section can also provide children with points to note and advice when conducting experiments on their own. Furthermore, the explanation section can use diagrams and illustrations to make the explanation easier for children to understand. This can attract children's interest and deepen their understanding of the simulation results.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The reception unit inputs the child's question. The child's question can be a scientific question or a question about daily life. The reception unit can input the child's question using voice input or a touch screen. Step 2: The generation unit uses a generation AI to simulate an experiment based on the question entered by the reception unit. The generation unit references a database of chemical reactions and performs the simulation using a specific algorithm. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and performs the simulation based on the database of chemical reactions. Step 3: The display unit displays the simulation results generated by the generation unit. The display unit can visually display the results using videos and graphs. Step 4: The commentary section explains the simulation results generated by the generation section. The commentary section uses the generation AI to explain the process and results of the experiment.

[0064] (Example 2) A system according to an embodiment of the present invention uses generative AI to simulate experiments such as chemical reactions, allowing children to participate mentally in real time. This system deepens children's understanding and interest by having the generative AI perform a simulation and display and explain the results, based on children's input questions. For example, if a child inputs, "What happens when you mix an acid and an alkali?", the generative AI simulates the chemical reaction and displays the results as videos and graphs. This allows children to visually understand the results of the experiment without actually performing the experiment. The generative AI also provides explanations about the experimental process and results, deepening children's understanding. For example, the generative AI might explain, "When an acid and an alkali are mixed, a neutralization reaction occurs, producing water and salt." This allows children to gain a deeper understanding of the principles and results of the experiment. This allows the system to resolve children's questions in real time and visually explain the results. For example, when a child inputs a question, the generative AI performs a simulation, displays the results, and explains them, deepening children's understanding and interest.

[0065] The experiment simulation system according to the embodiment includes a reception unit, a generation unit, a display unit, and an explanation unit. The reception unit inputs children's questions. The children's questions include, but are not limited to, scientific questions and questions about daily life. The reception unit can input children's questions using, for example, voice input or a touch screen. The generation unit uses a generation AI to simulate an experiment based on the questions input by the reception unit. The generation unit, for example, references a database of chemical reactions and performs a simulation using a specific algorithm. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and performs a simulation based on the database of chemical reactions. The display unit displays the simulation results generated by the generation unit. The display unit can visually display the results using, for example, videos or graphs. The explanation unit provides explanations about the simulation results generated by the generation unit. The explanation unit, for example, uses the generation AI to provide explanations about the process and results of the experiment. In this way, the experiment simulation system according to the embodiment can solve children's questions in real time and allow them to visually understand the results of the experiment. For example, when a child inputs a question, the generative AI will perform a simulation, display the results, and provide an explanation, deepening the child's understanding and interest.

[0066] The reception unit can input the child's question using voice input or a touch screen. The reception unit can input the child's question using voice input, for example. For example, the reception unit can convert the child's question into text data using voice recognition technology. The reception unit can also input the child's question using a touch screen. For example, the sensitivity of the touch panel and the screen size can be adjusted to allow the child to operate the device intuitively. This allows the child to input their question intuitively.

[0067] The generation unit can refer to the database of chemical reactions and simulate experiments using a specific algorithm. The generation unit, for example, refers to the database of chemical reactions and simulates experiments using a specific algorithm. For example, the generation unit performs simulations using algorithms such as the Monte Carlo method or molecular dynamics simulation. The generation unit can also perform accurate simulations based on the database of chemical reactions. For example, the generation unit performs simulations taking into account the types of chemical reactions included and the frequency of database updates. This makes it possible to perform accurate simulations by referring to the database of chemical reactions.

[0068] The display unit can display the generated simulation results as a video or a graph. The display unit can, for example, display the generated simulation results as a video. For example, the simulation results are visually displayed using animation or live-action footage. The display unit can also display the generated simulation results as a graph. For example, the simulation results are visually displayed using a line graph or a bar graph. In this way, visually displaying the results makes it easier for children to understand.

[0069] The commentary unit can provide commentary on the generated simulation results. For example, the commentary unit uses the generation AI to provide commentary on the experimental process and results. The commentary unit can provide commentary that is easy for children to understand by adjusting the language used and the level of detail of the commentary. For example, the generation AI might explain, "When an acid and an alkali are mixed, a neutralization reaction occurs, producing water and salt." This allows children to deepen their understanding by providing commentary on the simulation results.

[0070] The reception unit can estimate the child's emotions and adjust the timing of question input based on the estimated child's emotions. The reception unit, for example, estimates the child's emotions and adjusts the timing of question input based on the estimated child's emotions. For example, if the child is excited, the interface is simplified so that the question can be input quickly. If the child is calm, detailed input options are provided so that the question can be input specifically. If the child is tired, voice input is prioritized so that the question can be input easily. This allows the question to be input at a more appropriate time by adjusting the timing of question input according to the child's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0071] The reception unit can analyze the child's past question history and select the optimal input method. The reception unit, for example, analyzes the child's past question history and selects the optimal input method. For example, it preferentially suggests input methods (voice, text, etc.) that the child has frequently used in the past. It also provides input options related to specific topics from the child's past question history. It also analyzes patterns of questions the child has entered in the past and suggests the optimal input method. In this way, it is possible to provide the optimal input method for the child by analyzing the past question history.

[0072] The reception unit can filter questions based on the child's current learning situation and areas of interest when the question is entered. For example, the reception unit filters questions based on the child's current learning situation and areas of interest when the question is entered. For example, questions related to the topic the child is currently studying can be preferentially entered. Related questions can also be suggested based on the child's areas of interest. Questions of an appropriate level of difficulty can also be entered according to the child's learning progress. In this way, by filtering questions based on the child's learning situation and areas of interest, more relevant questions can be entered.

[0073] The reception unit can select the optimal input means depending on the child's input method when inputting a question. For example, when inputting a question, the reception unit selects the optimal input means depending on the child's input method (voice, text, image, etc.). For example, if the child uses voice input, the question is input using voice recognition technology. If the child uses text input, a keyboard or touch screen is provided. If the child uses image input, the question is input using image recognition technology. This makes it easier for the child to input questions by selecting the optimal means depending on the child's input method.

[0074] The reception unit can estimate the child's emotions and determine the priority of questions to be input based on the estimated child's emotions. The reception unit, for example, estimates the child's emotions and determines the priority of questions to be input based on the estimated child's emotions. For example, if the child is excited, questions that can be input intuitively are displayed preferentially. If the child is calm, questions that require detailed input are displayed preferentially. If the child is tired, questions that can be input easily are displayed preferentially. In this way, by determining the priority of questions according to the child's emotions, more appropriate questions can be input preferentially. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] When a question is input, the reception unit can prioritize input of highly relevant questions taking into account the child's geographical location information. For example, when a question is input, the reception unit prioritizes input of highly relevant questions taking into account the child's geographical location information. For example, if the child is in a specific location, questions related to that location are prioritized to be displayed. Also, if the child is traveling, questions related to the travel destination are prioritized to be displayed. Also, if the child is at school, questions related to the content of school lessons are prioritized to be displayed. In this way, highly relevant questions can be prioritized to be input by taking into account the geographical location information.

[0076] The reception unit can analyze the child's social media activity when a question is entered and input related questions. For example, the reception unit can analyze the child's social media activity when a question is entered and input related questions. For example, the reception unit can suggest related questions based on content shared by the child on social media. Also, the reception unit can suggest related questions based on the activity of the child's friends on social media. Also, the reception unit can suggest related questions by analyzing the content posted on the child's social media. In this way, related questions can be suggested by analyzing social media activity.

[0077] The reception unit can customize the input method by reflecting the child's past feedback when entering a question. For example, the reception unit customizes the input method by reflecting the child's past feedback when entering a question. For example, the reception unit preferentially provides an input method that the child has used favorably in the past. The input interface is also customized based on the child's past feedback. The reception unit also analyzes patterns of questions entered by the child in the past and suggests the optimal input method. In this way, by reflecting past feedback, the optimal input method for the child can be provided.

[0078] The generation unit can estimate the child's emotion and adjust the way the simulation is presented based on the estimated emotion. The generation unit, for example, estimates the child's emotion and adjusts the way the simulation is presented based on the estimated emotion. For example, if the child is excited, a visually stimulating simulation is generated. If the child is calm, a simulation including detailed explanations is generated. If the child is tired, a simple and intuitive simulation is generated. This allows for a more appropriate simulation to be provided by adjusting the way the simulation is presented according to the child's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] The generation unit can adjust the level of detail of the simulation based on the importance of the experiment when generating the simulation. For example, the generation unit adjusts the level of detail of the simulation based on the importance of the experiment when generating the simulation. For example, for an important experiment, a detailed simulation is generated. For a simple experiment, a simple simulation is generated. The level of detail of the simulation is also adjusted according to the child's level of understanding. In this way, an appropriate simulation can be provided by adjusting the level of detail of the simulation based on the importance of the experiment.

[0080] The generation unit can apply different simulation algorithms depending on the category of the experiment when generating a simulation. For example, the generation unit applies different simulation algorithms depending on the category of the experiment when generating a simulation. For example, a specific chemical algorithm is applied to a simulation of a chemical reaction. Also, an algorithm based on the laws of physics is applied to a simulation of a physical experiment. Also, an algorithm based on biological data is applied to a simulation of a biological experiment. In this way, by applying an appropriate algorithm depending on the category of the experiment, a highly accurate simulation can be provided.

[0081] The generation unit can improve the accuracy of the simulation by referring to the results of the child's past simulations when generating the simulation. For example, the generation unit improves the accuracy of the simulation by referring to the results of the child's past simulations when generating the simulation. For example, the generation unit generates a highly accurate simulation based on the results of the child's past simulations. The generation unit also analyzes the results of the child's past simulations and applies an optimal algorithm. The generation unit also adjusts the simulation parameters by referring to the results of the child's past simulations. In this way, a highly accurate simulation can be provided by referring to the results of the past simulations.

[0082] The generation unit can estimate the child's emotions and adjust the length of the simulation based on the estimated child's emotions. For example, the generation unit estimates the child's emotions and adjusts the length of the simulation based on the estimated child's emotions. For example, if the child is excited, a short, visually stimulating simulation is generated. If the child is calm, a longer simulation with detailed explanations is generated. If the child is tired, a simple, short simulation is generated. This allows for adjusting the length of the simulation according to the child's emotions to provide a more appropriate simulation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The generation unit can determine the priority of simulations based on the submission dates of experiments when generating simulations. The generation unit, for example, determines the priority of simulations based on the submission dates of experiments when generating simulations. For example, for experiments with an approaching deadline, the generation unit gives priority to generating simulations. Also, for experiments with a more distant submission date, the generation unit puts the generation of simulations on hold. Also, the generation order of simulations is adjusted according to the submission dates. In this way, by determining the priority of simulations based on the submission dates, simulations can be provided at the appropriate time.

[0084] The generation unit can adjust the order of simulations based on the relevance of the experiments when generating the simulations. The generation unit, for example, adjusts the order of simulations based on the relevance of the experiments when generating the simulations. For example, experiments with high relevance are simulated with priority. Also, experiments with low relevance are simulated later. Also, the order of simulations is adjusted according to the relevance of the experiments. In this way, by adjusting the order of simulations based on the relevance of the experiments, experiments with high relevance can be simulated with priority.

[0085] The generation unit can adjust the use of technical terminology in the simulation according to the child's level of expertise when generating the simulation. For example, the generation unit adjusts the use of technical terminology in the simulation according to the child's level of expertise when generating the simulation. For example, if the child's level of expertise is low, the generation unit generates a simulation using simple terminology. On the other hand, if the child's level of expertise is high, the generation unit generates a simulation using detailed technical terminology. Also, the generation unit adjusts the terminology in the simulation according to the child's level of expertise. In this way, by adjusting the use of technical terminology according to the child's level of expertise, it is possible to provide a simulation that is easy to understand.

[0086] The display unit can estimate the child's emotion and adjust the display method based on the estimated emotion. For example, the display unit estimates the child's emotion and adjusts the display method based on the estimated emotion. For example, if the child is excited, a visually stimulating display method is provided. If the child is calm, a display method including detailed information is provided. If the child is tired, a simple and highly visible display method is provided. This allows a more appropriate display to be provided by adjusting the display method according to the child's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0087] The display unit can adjust the level of detail of the display based on the importance of the simulation result when displaying the result. For example, the display unit adjusts the level of detail of the display based on the importance of the simulation result when displaying the result. For example, for important simulation results, detailed information is displayed. For simple simulation results, simple information is displayed. The level of detail of the display is also adjusted according to the importance of the simulation result. In this way, by adjusting the level of detail of the display based on the importance of the simulation result, appropriate information can be provided.

[0088] The display unit can apply different display algorithms depending on the category of the simulation results when displaying the results. For example, the display unit applies different display algorithms depending on the category of the simulation results when displaying the results. For example, a specific display algorithm is applied to the simulation results of a chemical reaction. A display algorithm based on physical laws is applied to the simulation results of a physical experiment. A display algorithm based on biological data is applied to the simulation results of a biological experiment. In this way, by applying an appropriate display algorithm depending on the category of the simulation results, it is possible to provide an easy-to-understand display.

[0089] The display unit can estimate the child's emotion and adjust the length of the display based on the estimated emotion. For example, the display unit estimates the child's emotion and adjusts the length of the display based on the estimated emotion. For example, if the child is excited, a short, visually stimulating display is provided. If the child is calm, a longer display including detailed information is provided. If the child is tired, a simple, short display is provided. In this way, by adjusting the length of the display according to the child's emotion, a more appropriate display can be provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] The display unit can determine the display priority based on the submission time of the simulation results when displaying them. The display unit, for example, determines the display priority based on the submission time of the simulation results when displaying them. For example, simulation results with an approaching deadline are displayed preferentially. Simulation results with a more distant submission time are displayed later. The display order is also adjusted depending on the submission time. In this way, by determining the display priority based on the submission time, it is possible to provide display at an appropriate time.

[0091] The display unit can adjust the display order based on the relevance of the simulation results when displaying them. The display unit, for example, adjusts the display order based on the relevance of the simulation results when displaying them. For example, highly relevant simulation results are displayed preferentially. Also, less relevant simulation results are displayed later. Also, the display order is adjusted according to the relevance of the simulation results. In this way, by adjusting the display order based on the relevance of the simulation results, highly relevant results can be displayed preferentially.

[0092] The display unit can adjust the use of technical terms in the display according to the child's level of expertise when displaying. For example, the display unit adjusts the use of technical terms in the display according to the child's level of expertise when displaying. For example, if the child's level of expertise is low, simple terms are used for display. On the other hand, if the child's level of expertise is high, detailed technical terms are used for display. Also, the display terms are adjusted according to the child's level of expertise. In this way, by adjusting the use of technical terms according to the child's level of expertise, it is possible to provide a display that is easy to understand.

[0093] The commentary unit can estimate the child's emotions and adjust the method of commentary based on the estimated child's emotions. The commentary unit, for example, estimates the child's emotions and adjusts the method of commentary based on the estimated child's emotions. For example, if the child is excited, a visually stimulating commentary is provided. If the child is calm, a detailed commentary is provided. If the child is tired, a simple, highly visible commentary is provided. This allows the method of commentary to be adjusted according to the child's emotions, thereby providing more appropriate commentary. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0094] The commentary unit can adjust the level of detail of the commentary based on the importance of the simulation result when providing the commentary. For example, the commentary unit adjusts the level of detail of the commentary based on the importance of the simulation result when providing the commentary. For example, in the case of an important simulation result, a detailed commentary is provided. In addition, in the case of a simple simulation result, a simple commentary is provided. In addition, the level of detail of the commentary is adjusted according to the importance of the simulation result. In this way, by adjusting the level of detail of the commentary based on the importance of the simulation result, appropriate information can be provided.

[0095] The commentary unit can apply different commentary algorithms depending on the category of the simulation results when providing commentary. For example, the commentary unit applies different commentary algorithms depending on the category of the simulation results when providing commentary. For example, a specific commentary algorithm is applied to the simulation results of a chemical reaction. Also, an commentary algorithm based on physical laws is applied to the simulation results of a physical experiment. Also, an commentary algorithm based on biological data is applied to the simulation results of a biological experiment. In this way, by applying an appropriate commentary algorithm depending on the category of the simulation results, it is possible to provide easy-to-understand commentary.

[0096] The commentary unit can improve the accuracy of the commentary by referring to the child's past commentary results when providing commentary. For example, the commentary unit improves the accuracy of the commentary by referring to the child's past commentary results when providing commentary. For example, the commentary unit provides a highly accurate commentary based on the commentary results the child received in the past. The commentary unit also analyzes the child's past commentary results and applies an optimal commentary algorithm. The commentary parameters are also adjusted by referring to the child's past commentary results. In this way, a highly accurate commentary can be provided by referring to the past commentary results.

[0097] The commentary unit can estimate the child's emotions and adjust the length of the commentary based on the estimated child's emotions. For example, the commentary unit estimates the child's emotions and adjusts the length of the commentary based on the estimated child's emotions. For example, if the child is excited, a short, visually stimulating commentary is provided. If the child is calm, a longer commentary including detailed information is provided. If the child is tired, a simple, short commentary is provided. This allows for adjusting the length of the commentary according to the child's emotions, thereby providing a more appropriate commentary. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0098] The commentary unit can determine the priority of commentary based on the submission time of the simulation results when providing commentary. For example, the commentary unit determines the priority of commentary based on the submission time of the simulation results when providing commentary. For example, in the case of simulation results whose deadline is approaching, commentary is provided preferentially. In addition, in the case of simulation results whose submission time is further away, commentary is provided later. In addition, the order of commentary is adjusted depending on the submission time. In this way, by determining the priority of commentary based on the submission time, commentary can be provided at the appropriate time.

[0099] The commentary unit can adjust the order of commentary based on the relevance of the simulation results when providing commentary. For example, the commentary unit adjusts the order of commentary based on the relevance of the simulation results when providing commentary. For example, highly relevant simulation results are given priority in commentary. Also, less relevant simulation results are given priority in commentary. Also, the order of commentary is adjusted according to the relevance of the simulation results. In this way, by adjusting the order of commentary based on the relevance of the simulation results, highly relevant results can be given priority in commentary.

[0100] The explanation unit can adjust the use of technical terms in the explanation according to the child's level of expertise when providing explanations. For example, the explanation unit adjusts the use of technical terms in the explanation according to the child's level of expertise when providing explanations. For example, if the child's level of expertise is low, simple terms are used for the explanation. On the other hand, if the child's level of expertise is high, detailed technical terms are used for the explanation. Also, the terms in the explanation are adjusted according to the child's level of expertise. In this way, by adjusting the use of technical terms according to the child's level of expertise, explanations that are easy to understand can be provided. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, display unit, and commentary unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can input a child's question using the touch panel 38A or microphone 38B of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and simulates an experiment using a generation AI. The display unit displays the simulation results using the display 40A of the smart device 14. The commentary unit is realized by the specific processing unit 290 of the data processing device 12 and explains the process and results of the experiment using a generation AI. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, display unit, and commentary unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can input a child's question using the microphone 238 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and simulates an experiment using a generation AI. The display unit displays the simulation results using the display of the smart glasses 214. The commentary unit is realized by the specific processing unit 290 of the data processing device 12 and provides commentary on the process and results of the experiment using a generation AI. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, display unit, and commentary unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can input a child's question using the microphone 238 of the headset-type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and simulates an experiment using a generation AI. The display unit displays the simulation results using the display 343 of the headset-type terminal 314. The commentary unit is realized by the specific processing unit 290 of the data processing device 12, and uses a generation AI to provide commentary on the process and results of the experiment. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, display unit, and commentary unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input a child's question using the microphone 238 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and simulates an experiment using a generation AI. The display unit displays the simulation results using the display of the robot 414. The commentary unit is realized by the specific processing unit 290 of the data processing device 12, and uses a generation AI to provide commentary on the process and results of the experiment.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] When a child enters a question, the reception unit can provide an appropriate input interface based on the child's age and grade level. For example, it provides large icons and simple menus for young children, while providing detailed options and text input for elementary school children. The reception unit can also select visual input methods or voice input depending on the child's learning style. This makes entering questions more intuitive and easier by providing an appropriate input interface based on the child's age and grade level.

[0103] When generating simulation results, the generation unit can refer to the child's past learning history and provide a simulation that matches the child's level of understanding. For example, if a simulation on the same topic has been conducted in the past, a more advanced simulation can be provided based on that result. Also, if a child is interested in a particular field, simulations related to that field can be provided preferentially. This makes it possible to provide more effective simulations based on the child's learning history.

[0104] The display unit can also provide a customized display method according to the child's visual preferences when displaying the simulation results. For example, if the child likes animations, the simulation results can be displayed in animation format. Alternatively, if the child likes graphs and charts, the simulation results can be displayed in graphs and charts. Furthermore, the display unit can adjust colors according to the child's color vision characteristics. This allows the child to better understand the simulation results by providing a display method according to the child's visual preferences.

[0105] The explanation section can incorporate storytelling elements to attract children's interest when explaining the simulation results. For example, when explaining the results of a chemical reaction simulation, characters can be used to explain in a story format. The explanation section can also provide children with points to note and advice when conducting experiments on their own. Furthermore, the explanation section can use diagrams and illustrations to make the explanation easier for children to understand. This can attract children's interest and deepen their understanding of the simulation results.

[0106] The reception unit can also estimate the child's emotions and suggest ways to input questions based on the estimated emotions. For example, if the child is excited, an interface is provided that allows the child to input questions with simple tap operations. If the child is calm, more detailed input options are provided, allowing the child to input specific questions. Furthermore, if the child is tired, voice input is prioritized, allowing the child to easily input questions. This allows the child to smoothly input questions by providing an appropriate input method according to their emotions.

[0107] When generating the simulation results, the generation unit can estimate the child's emotions and adjust the difficulty of the simulation based on the estimated emotions. For example, if the child is excited, a more difficult simulation can be provided to allow the child to enjoy challenging content. If the child is calm, a simulation with detailed explanations can be provided to encourage deeper understanding. Furthermore, if the child is tired, a simple and intuitive simulation can be provided to reduce the child's burden. This makes it possible to provide an appropriate simulation according to the child's emotions.

[0108] When displaying the simulation results, the display unit can estimate the child's emotions and adjust the display tempo based on the estimated emotions. For example, if the child is excited, a fast-paced display is provided to maintain interest. If the child is calm, detailed information is provided at a slower pace. Furthermore, if the child is tired, a simple display is provided so that the child can understand the results in a short time. This allows the child to better understand the simulation results by providing an appropriate display tempo according to the child's emotions.

[0109] When explaining the simulation results, the commentary unit can estimate the child's emotions and adjust the tone of the commentary based on the estimated emotions. For example, if the child is excited, the commentary can be given in a lively and cheerful tone. If the child is calm, the commentary can be given in a gentle and polite tone. Furthermore, if the child is tired, the commentary can be given in a gentle and encouraging tone. This allows the child to better understand the simulation results by providing an appropriate commentary tone according to their emotions.

[0110] The reception unit can also estimate the child's emotions and suggest questions to input based on the estimated emotions. For example, if the child is excited, it will suggest questions that will pique the child's interest. If the child is calm, it will suggest questions that will encourage deeper thought. Furthermore, if the child is tired, it will suggest simple and intuitive questions. This allows the system to suggest appropriate questions based on the child's emotions, making it easier for the child to input questions.

[0111] When generating the simulation results, the generation unit can also estimate the child's emotions and adjust the simulation feedback based on the estimated emotions. For example, if the child is excited, positive and encouraging feedback is provided. If the child is calm, detailed feedback is provided to deepen understanding. Furthermore, if the child is tired, brief and gentle feedback is provided. In this way, appropriate feedback according to the child's emotions can be provided to deepen understanding of the simulation results.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The reception unit inputs the child's question. The child's question can be a scientific question or a question about daily life. The reception unit can input the child's question using voice input or a touch screen. Step 2: The generation unit uses a generation AI to simulate an experiment based on the question entered by the reception unit. The generation unit references a database of chemical reactions and performs the simulation using a specific algorithm. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and performs the simulation based on the database of chemical reactions. Step 3: The display unit displays the simulation results generated by the generation unit. The display unit can visually display the results using videos and graphs. Step 4: The commentary section explains the simulation results generated by the generation section. The commentary section uses the generation AI to explain the process and results of the experiment.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[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 headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[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 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.

[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 AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0162] 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.

[0163] 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.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk where children can input their questions, a generating unit that performs a simulation of an experiment based on the question input by the receiving unit; a display unit that displays the simulation results generated by the generation unit; a commentary unit that provides commentary on the simulation results generated by the generation unit; A system characterized by:

2. The reception unit Enter your child's question using voice input or the touchscreen 2. The system of claim 1.

3. The generation unit Referencing a database of chemical reactions and simulating experiments using specific algorithms 2. The system of claim 1.

4. The display unit Display generated simulation results as videos or graphs 2. The system of claim 1.

5. The commentary section Comment on the generated simulation results 2. The system of claim 1.

6. The reception unit Estimate the child's emotions and adjust the timing of question input based on the estimated emotions.

2. The system of claim 1.

7. The reception unit Analyze the child's past question history and select the appropriate input method 2. The system of claim 1.

8. The reception unit As you type your question, it will be filtered based on your child's current learning status and areas of interest.

2. The system of claim 1.

Images