System

Abstract

An object of the system according to the embodiment is to appropriately respond to questions of children and widen interests and concerns.SOLUTION: A system includes a question collection unit, an analysis unit, an answer generation unit, and an advice provision unit. The question collection unit collects questions of children. The analysis unit analyzes the questions collected by the question collection unit. The answer generation unit generates an appropriate answer to the question analyzed by the analysis unit. The advice providing unit provides advice for expanding interest on the basis of the answer generated by the answer 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 has had the problem of making it difficult to respond appropriately to children's diverse questions and broaden their interests.

[0005] The system according to the embodiment aims to provide appropriate responses to children's questions and broaden their interests. [Means for solving the problem]

[0006] The system according to the embodiment includes a question collection unit, an analysis unit, an answer generation unit, and an advice provision unit. The question collection unit collects questions from children. The analysis unit analyzes the questions collected by the question collection unit. The answer generation unit generates appropriate answers to the questions analyzed by the analysis unit. The advice provision unit provides advice to broaden children's interests based on the answers generated by the answer generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately respond to children's questions and broaden their interests. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses generative AI to provide appropriate answers and advice to children's various questions and concerns as they grow. This system collects and analyzes children's questions, generates appropriate answers, and provides advice to broaden their interests. This allows the system to quickly and appropriately answer children's questions and broaden their interests. It also reduces the burden on adults and improves the learning environment for children.

[0029] The system according to the embodiment includes a question collection unit, an analysis unit, an answer generation unit, and an advice provision unit. The question collection unit collects questions from children. For example, questions can be collected by voice input, text input, image input, or other methods. The analysis unit analyzes the collected questions. For example, it analyzes the content of the questions using natural language processing technology or machine learning algorithms. The answer generation unit generates appropriate answers to the analyzed questions. For example, a generation AI generates answers in simple language tailored to the children's level of understanding. The advice provision unit provides advice to broaden their interests based on the generated answers. For example, it introduces related topics or suggests experiments or observations. This enables the system to quickly and appropriately answer children's questions and broaden their interests.

[0030] The question collection unit can refer to the children's past question history and provide prompts to elicit more specific questions. For example, the question collection unit uses a generation AI to refer to the children's past question history and provide specific prompts based on related questions. For example, a prompt could be presented in the form of, "You asked about dinosaurs before, but which dinosaur would you like to know about this time?" This makes it possible to elicit specific questions based on the children's past question history.

[0031] The question collection unit can accept not only voice input, but also visual input using pictures and photos. For example, the question collection unit will build a system in which the generative AI can accept not only voice input, but also visual input using pictures and photos. For example, children can upload drawings or photos they have taken, and the system will analyze questions based on these. This will allow it to accept not only voice input, but also visual input.

[0032] The question collection unit can integrate observation data from school and home and identify situations in which questions are likely to arise. For example, the question collection unit uses a generative AI to integrate observation data from school and home and build a system to identify situations in which questions are likely to arise. For example, it analyzes observation data during class and study time at home to identify times when questions are likely to arise. This makes it possible to identify situations in which questions are likely to arise.

[0033] When analyzing children's questions, the analysis unit can refer to their learning history and topics of interest to encourage deeper understanding. For example, when the generation AI analyzes children's questions, the analysis unit can refer to related learning history to build a system that encourages deeper understanding. For example, it can suggest related topics based on what has been learned in the past. This makes it possible to encourage deeper understanding by referring to learning history and topics of interest.

[0034] When analyzing children's questions, the analysis unit can refer to scientific data and experimental results and provide specific examples. For example, when the generation AI analyzes children's questions, the analysis unit builds a system that refers to relevant scientific data and provides specific examples. For example, in response to a question about the color of the sky, data on light scattering is provided. This makes it possible to provide specific examples by referring to scientific data and experimental results.

[0035] The analysis unit can refer to video data and audio data and generate answers using multimedia. For example, when the generation AI analyzes children's questions, the analysis unit references related video data and builds a system that generates answers using multimedia. For example, when explaining the movements of dinosaurs, it provides animated videos of dinosaurs. This makes it possible to generate answers using multimedia.

[0036] The answer generation unit can provide answers at multiple levels according to the children's level of understanding and present options to choose from. The answer generation unit, for example, builds a system in which the generation AI provides answers at multiple levels according to the children's level of understanding. For example, it presents multiple options, from simple explanations to detailed explanations. This makes it possible to provide answers at multiple levels according to the children's level of understanding.

[0037] The answer generation unit can suggest methods of experimentation and observation and encourage users to try them out. For example, when the generation AI generates an answer, the answer generation unit builds a system that suggests related methods of experimentation and observation. For example, in response to a question about plant growth, it can suggest an experiment to actually grow plants. This makes it possible to suggest methods of experimentation and observation and encourage users to try them out.

[0038] The answer generation unit can integrate knowledge from different academic fields and provide answers from multiple perspectives. For example, the answer generation unit builds a system in which the generation AI integrates knowledge from different academic fields and provides answers from multiple perspectives. For example, it can integrate knowledge of history and science to provide an answer about the extinction of dinosaurs. This makes it possible to provide answers from multiple perspectives.

[0039] The answer generation unit can incorporate elements of art and music to provide creative answers. For example, the answer generation unit builds a system that incorporates elements of related art and music when the generative AI generates an answer. For example, in response to a question about the history of music, it can provide samples of related music. This makes it possible to provide creative answers that incorporate elements of art and music.

[0040] The advice-providing unit can refer to children's past learning history and topics of interest and suggest new related topics. For example, the advice-providing unit will build a system in which a generative AI refers to children's past learning history and suggests new related topics. For example, for a child who is interested in dinosaurs, a topic about ancient creatures in general can be suggested. This makes it possible to suggest new related topics.

[0041] The advice-providing unit can suggest methods of experimentation and observation and encourage users to try them out. For example, when the generative AI provides advice, the advice-providing unit builds a system that suggests related methods of experimentation and observation. For example, in response to advice about plant growth, the unit can suggest an experiment to actually grow plants. This makes it possible to suggest methods of experimentation and observation and encourage users to try them out.

[0042] The advice providing unit can refer to knowledge of different cultures and regions and provide advice from a global perspective. For example, the advice providing unit constructs a system in which the generating AI refers to knowledge of different cultures and regions and provides advice from a global perspective. For example, in response to advice about food culture, the system introduces traditional dishes from each country. This allows advice to be provided from a global perspective.

[0043] The advice providing unit can refer to video data and audio data and generate advice using multimedia. For example, when the generation AI provides advice, the advice providing unit references related video data and builds a system that generates advice using multimedia. For example, in response to advice about dinosaurs, an animated video of a dinosaur is provided. This makes it possible to generate advice using multimedia.

[0044] The system can automatically record answers to children's questions so that they can be referenced later. For example, the system will build a system in which a generative AI automatically records answers to children's questions so that they can be referenced later. For example, the questions and answers are saved in a database and a search function is provided. This allows answers to be automatically recorded so that they can be referenced later.

[0045] The system can periodically compile answers to children's questions and provide them as a report. For example, the system builds a system in which a generative AI periodically compiles answers to children's questions and provides them as a report. For example, a report summarizing questions and answers is generated weekly and provided to parents and teachers. This allows answers to be periodically compiled and provided as a report.

[0046] The system can provide answers to children's questions in different languages, achieving multilingual support. For example, the system builds a system in which a generative AI provides answers to children's questions in different languages. For example, answers are generated in multiple languages, such as English, French, and Chinese. This allows answers to be provided in different languages, achieving multilingual support.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] When children input questions, the question collection unit uses voice recognition technology to automatically identify dialects and accents and convert them into standard Japanese. For example, a question input in Kansai dialect can be converted into standard Japanese, making it easier for the analysis unit to process. This enables accurate question analysis regardless of differences in dialect or accent.

[0049] When analyzing children's questions, the analysis unit can provide relevant historical and cultural background depending on the content of the question. For example, in response to a question about dinosaurs, it can provide the environment and cultural perspective of the Earth at the time when dinosaurs lived, thereby deepening children's understanding.

[0050] The answer generation unit can provide answers to children's questions that incorporate game elements. For example, it can present answers in the form of a quiz, and introduce a system where students can earn points for correct answers. This can increase their motivation to learn.

[0051] The advice department can respond to children's questions by suggesting related fieldwork or museum visits. For example, if a child asks about dinosaurs, it can introduce them to a dinosaur exhibition at a nearby museum. This allows children to deepen their learning through hands-on experience.

[0052] The system can provide features that allow parents and teachers to customize answers to children's questions, for example by allowing them to enter additional information or explanations on specific topics, allowing for personalized learning support for each child.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The question collection unit collects questions from children. For example, questions can be collected by voice input, text input, image input, etc. Step 2: The analysis unit analyzes the collected questions, for example, by using natural language processing technology or machine learning algorithms. Step 3: The answer generation unit generates appropriate answers to the analyzed questions. For example, the AI ​​generates answers in simple terms that are tailored to the children's level of understanding. Step 4: The advice provider provides advice to broaden the user's interests based on the generated answers, such as introducing related topics or suggesting experiments or observations.

[0055] (Example 2) A system according to an embodiment of the present invention uses generative AI to provide appropriate answers and advice to children's various questions and concerns as they grow. This system collects and analyzes children's questions, generates appropriate answers, and provides advice to broaden their interests. This allows the system to quickly and appropriately answer children's questions and broaden their interests. It also reduces the burden on adults and improves the learning environment for children.

[0056] The system according to the embodiment includes a question collection unit, an analysis unit, an answer generation unit, and an advice provision unit. The question collection unit collects questions from children. For example, questions can be collected by voice input, text input, image input, or other methods. The analysis unit analyzes the collected questions. For example, it analyzes the content of the questions using natural language processing technology or machine learning algorithms. The answer generation unit generates appropriate answers to the analyzed questions. For example, a generation AI generates answers in simple language tailored to the children's level of understanding. The advice provision unit provides advice to broaden their interests based on the generated answers. For example, it introduces related topics or suggests experiments or observations. This enables the system to quickly and appropriately answer children's questions and broaden their interests.

[0057] The question collection unit analyzes children's tone of voice and facial expressions to understand their emotional state and evaluate the importance of the question. For example, when a child inputs a question, the generation AI uses a microphone and camera to analyze the tone of voice and facial expressions in real time to understand the emotional state. For example, if an excited voice or a smile is detected, the question is rated as high priority. This makes it possible to evaluate the importance of questions based on the child's emotional state.

[0058] The question collection unit can refer to the children's past question history and provide prompts to elicit more specific questions. For example, the question collection unit uses a generation AI to refer to the children's past question history and provide specific prompts based on related questions. For example, a prompt could be presented in the form of, "You asked about dinosaurs before, but which dinosaur would you like to know about this time?" This makes it possible to elicit specific questions based on the children's past question history.

[0059] The question collection unit uses the emotion estimation function to analyze the emotions children are feeling when they enter their questions in real time, and can suggest ways of asking questions that will elicit positive emotions. For example, when a child enters a question, the question collection unit uses the emotion estimation function to analyze their emotions in real time and suggests ways of asking questions that will elicit positive emotions. For example, it might suggest something like, "That's an interesting question! Can you tell me more about it?" This makes it possible to suggest ways of asking questions that will elicit positive emotions.

[0060] The question collection unit can accept not only voice input, but also visual input using pictures and photos. For example, the question collection unit will build a system in which the generative AI can accept not only voice input, but also visual input using pictures and photos. For example, children can upload drawings or photos they have taken, and the system will analyze questions based on these. This will allow it to accept not only voice input, but also visual input.

[0061] The question collection unit can integrate observation data from school and home and identify situations in which questions are likely to arise. For example, the question collection unit uses a generative AI to integrate observation data from school and home and build a system to identify situations in which questions are likely to arise. For example, it analyzes observation data during class and study time at home to identify times when questions are likely to arise. This makes it possible to identify situations in which questions are likely to arise.

[0062] The question collection unit uses the emotion estimation function to analyze the emotions children have when they input their questions, and can prompt them to ask questions at the appropriate time depending on the content of their questions. For example, when children input their questions, the question collection unit uses the emotion estimation function to analyze their emotions, and prompts them to ask questions at the appropriate time depending on the content of their questions. For example, prompting them to ask questions when they are excited will elicit more specific questions. This makes it possible to prompt them to ask questions at the appropriate time.

[0063] When analyzing children's questions, the analysis unit can refer to their learning history and topics of interest to encourage deeper understanding. For example, when the generation AI analyzes children's questions, the analysis unit can refer to related learning history to build a system that encourages deeper understanding. For example, it can suggest related topics based on what has been learned in the past. This makes it possible to encourage deeper understanding by referring to learning history and topics of interest.

[0064] When analyzing children's questions, the analysis unit can refer to scientific data and experimental results and provide specific examples. For example, when the generation AI analyzes children's questions, the analysis unit builds a system that refers to relevant scientific data and provides specific examples. For example, in response to a question about the color of the sky, data on light scattering is provided. This makes it possible to provide specific examples by referring to scientific data and experimental results.

[0065] The analysis unit uses the emotion estimation function to analyze the emotions behind children's questions and identify the direction of their interests based on their emotions. For example, the analysis unit builds a system in which the generative AI uses the emotion estimation function to analyze the emotions behind children's questions and identify the direction of their interests based on their emotions. For example, if excited emotions are detected, the system will dig deeper into those interests. This makes it possible to identify the direction of their interests based on their emotions.

[0066] The analysis unit can refer to video data and audio data and generate answers using multimedia. For example, when the generation AI analyzes children's questions, the analysis unit references related video data and builds a system that generates answers using multimedia. For example, when explaining the movements of dinosaurs, it provides animated videos of dinosaurs. This makes it possible to generate answers using multimedia.

[0067] The analysis unit uses the emotion estimation function to analyze the emotions behind children's questions and can suggest related topics based on those emotions. For example, the analysis unit builds a system in which the generative AI uses the emotion estimation function to analyze the emotions behind children's questions and suggests related topics based on those emotions. For example, if excited emotions are detected, topics that further elicit that excitement are suggested. This makes it possible to suggest related topics based on emotions.

[0068] The answer generation unit can provide answers at multiple levels according to the children's level of understanding and present options to choose from. The answer generation unit, for example, builds a system in which the generation AI provides answers at multiple levels according to the children's level of understanding. For example, it presents multiple options, from simple explanations to detailed explanations. This makes it possible to provide answers at multiple levels according to the children's level of understanding.

[0069] The answer generation unit can suggest methods of experimentation and observation and encourage users to try them out. For example, when the generation AI generates an answer, the answer generation unit builds a system that suggests related methods of experimentation and observation. For example, in response to a question about plant growth, it can suggest an experiment to actually grow plants. This makes it possible to suggest methods of experimentation and observation and encourage users to try them out.

[0070] The answer generation unit uses the emotion estimation function to generate answers that correspond to the children's emotions and can use expressions that elicit positive emotions. For example, the answer generation unit builds a system in which the generation AI uses the emotion estimation function to generate answers that correspond to the children's emotions. For example, if excited emotions are detected, expressions that further elicit that excitement are used. This makes it possible to generate answers that elicit positive emotions.

[0071] The answer generation unit can integrate knowledge from different academic fields and provide answers from multiple perspectives. For example, the answer generation unit builds a system in which the generation AI integrates knowledge from different academic fields and provides answers from multiple perspectives. For example, it can integrate knowledge of history and science to provide an answer about the extinction of dinosaurs. This makes it possible to provide answers from multiple perspectives.

[0072] The answer generation unit can incorporate elements of art and music to provide creative answers. For example, the answer generation unit builds a system that incorporates elements of related art and music when the generative AI generates an answer. For example, in response to a question about the history of music, it can provide samples of related music. This makes it possible to provide creative answers that incorporate elements of art and music.

[0073] The answer generation unit uses the emotion estimation function to generate answers that correspond to the children's emotions and can prompt them to ask follow-up questions based on their emotions. For example, the answer generation unit builds a system in which the generation AI uses the emotion estimation function to generate answers that correspond to the children's emotions. For example, if an excited emotion is detected, the answer generation unit prompts them to ask follow-up questions that further draw out that excitement. This makes it possible to prompt follow-up questions based on emotions.

[0074] The advice-providing unit can refer to children's past learning history and topics of interest and suggest new related topics. For example, the advice-providing unit will build a system in which a generative AI refers to children's past learning history and suggests new related topics. For example, for a child who is interested in dinosaurs, a topic about ancient creatures in general can be suggested. This makes it possible to suggest new related topics.

[0075] The advice-providing unit can suggest methods of experimentation and observation and encourage users to try them out. For example, when the generative AI provides advice, the advice-providing unit builds a system that suggests related methods of experimentation and observation. For example, in response to advice about plant growth, the unit can suggest an experiment to actually grow plants. This makes it possible to suggest methods of experimentation and observation and encourage users to try them out.

[0076] The advice providing unit uses the emotion estimation function to provide advice based on children's emotions and make suggestions that will elicit positive emotions. For example, the advice providing unit builds a system in which the generative AI uses the emotion estimation function to provide advice based on children's emotions. For example, if excited emotions are detected, advice that will further elicit that excitement can be provided. This makes it possible to provide advice that will elicit positive emotions.

[0077] The advice providing unit can refer to knowledge of different cultures and regions and provide advice from a global perspective. For example, the advice providing unit constructs a system in which the generating AI refers to knowledge of different cultures and regions and provides advice from a global perspective. For example, in response to advice about food culture, the system introduces traditional dishes from each country. This allows advice to be provided from a global perspective.

[0078] The advice providing unit can refer to video data and audio data and generate advice using multimedia. For example, when the generation AI provides advice, the advice providing unit references related video data and builds a system that generates advice using multimedia. For example, in response to advice about dinosaurs, an animated video of a dinosaur is provided. This makes it possible to generate advice using multimedia.

[0079] The advice providing unit can use the emotion estimation function to provide advice based on children's emotions and suggest related topics based on their emotions. For example, the advice providing unit builds a system in which the generation AI uses the emotion estimation function to provide advice based on children's emotions. For example, if an excited emotion is detected, related topics that will further stimulate that excitement are suggested. This makes it possible to suggest related topics based on emotions.

[0080] The system can automatically record answers to children's questions so that they can be referenced later. For example, the system will build a system in which a generative AI automatically records answers to children's questions so that they can be referenced later. For example, the questions and answers are saved in a database and a search function is provided. This allows answers to be automatically recorded so that they can be referenced later.

[0081] The system can periodically compile answers to children's questions and provide them as a report. For example, the system builds a system in which a generative AI periodically compiles answers to children's questions and provides them as a report. For example, a report summarizing questions and answers is generated weekly and provided to parents and teachers. This allows answers to be periodically compiled and provided as a report.

[0082] The system uses emotion estimation to generate answers based on children's emotions and uses expressions that elicit positive emotions, thereby reducing the burden on adults. For example, the system constructs a system in which the generative AI uses emotion estimation to generate answers based on children's emotions. For example, if it detects an excited emotion, it uses expressions that further elicit that excitement. This generates answers that elicit positive emotions, reducing the burden on adults.

[0083] The system can provide answers to children's questions in different languages, achieving multilingual support. For example, the system builds a system in which a generative AI provides answers to children's questions in different languages. For example, answers are generated in multiple languages, such as English, French, and Chinese. This allows answers to be provided in different languages, achieving multilingual support.

[0084] The system uses emotion estimation to generate answers based on children's emotions and prompts them to ask follow-up questions based on those emotions, thereby reducing the burden on adults. For example, the system builds a system in which the generative AI uses emotion estimation to generate answers based on children's emotions. For example, if it detects an excited emotion, it prompts them to ask follow-up questions that further elicit that excitement. This prompts follow-up questions based on emotions, reducing the burden on adults.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] When children input questions, the question collection unit uses voice recognition technology to automatically identify dialects and accents and convert them into standard Japanese. For example, a question input in Kansai dialect can be converted into standard Japanese, making it easier for the analysis unit to process. This enables accurate question analysis regardless of differences in dialect or accent.

[0087] When analyzing children's questions, the analysis unit can provide relevant historical and cultural background depending on the content of the question. For example, in response to a question about dinosaurs, it can provide the environment and cultural perspective of the Earth at the time when dinosaurs lived, thereby deepening children's understanding.

[0088] The answer generation unit can provide answers to children's questions that incorporate game elements. For example, it can present answers in the form of a quiz, and introduce a system where students can earn points for correct answers. This can increase their motivation to learn.

[0089] The advice department can respond to children's questions by suggesting related fieldwork or museum visits. For example, if a child asks about dinosaurs, it can introduce them to a dinosaur exhibition at a nearby museum. This allows children to deepen their learning through hands-on experience.

[0090] The system can provide features that allow parents and teachers to customize answers to children's questions, for example by allowing them to enter additional information or explanations on specific topics, allowing for personalized learning support for each child.

[0091] The question collection unit uses its emotion estimation function to analyze the emotions children express when they enter their questions, and can suggest ways of asking questions that will alleviate negative emotions. For example, if it detects feelings of anxiety, it will suggest something like, "Don't worry, can you explain it slowly?" This makes it possible to suggest ways of asking questions that will alleviate negative emotions.

[0092] The analysis unit uses emotion estimation to analyze the emotions behind children's questions and can suggest learning styles based on their emotions. For example, if excited emotions are detected, an active learning method will be suggested. This allows the system to suggest learning styles based on emotions.

[0093] The answer generation unit uses the emotion estimation function to generate answers that correspond to the children's emotions and provide feedback based on their emotions. For example, if it detects excitement, it will provide feedback in the form of "That's great! Let me know if you want to know more!" This allows it to provide feedback based on emotions.

[0094] The advice providing unit can use the emotion estimation function to provide advice based on the children's emotions and set learning goals based on their emotions. For example, if excited emotions are detected, learning goals can be set to maintain that excitement. This makes it possible to set learning goals based on emotions.

[0095] Using its emotion estimation function, the system can provide a lesson plan based on the child's emotions and create a learning environment that elicits positive emotions. For example, if the system detects excitement, it can provide a lesson plan to maintain that excitement. This allows the system to create a learning environment that elicits positive emotions.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The question collection unit collects questions from children. For example, questions can be collected by voice input, text input, image input, etc. Step 2: The analysis unit analyzes the collected questions, for example, by using natural language processing technology or machine learning algorithms. Step 3: The answer generation unit generates appropriate answers to the analyzed questions. For example, the AI ​​generates answers in simple terms that are tailored to the children's level of understanding. Step 4: The advice provider provides advice to broaden the user's interests based on the generated answers, such as introducing related topics or suggesting experiments or observations.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0142] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0143] 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.

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0165] 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 question collection department that collects questions from children, an analysis unit that analyzes the questions collected by the question collection unit; an answer generation unit that generates an appropriate answer to the question analyzed by the analysis unit; an advice providing unit that provides advice to broaden interests based on the answers generated by the answer generating unit; A system characterized by:

2. The question collection unit It accepts not only voice input but also visual input using pictures and photos.

2. The system of claim 1.

3. The analysis unit When analyzing children's questions, reference their learning history and topics of interest to promote deeper understanding.

2. The system of claim 1.

4. The answer generation unit Provide multiple levels of answers according to children's level of understanding and give them options to choose from 2. The system of claim 1.

5. The advice providing unit Refer to children's learning history and topics of interest and suggest new related topics 2. The system of claim 1.

6. The question collection unit Analyze the children's tone of voice and facial expressions to understand their emotional state and assess the importance of the question.

2. The system of claim 1.

7. The analysis unit Analyze the emotions behind children's questions and identify the direction of their interests based on those emotions 2. The system of claim 1.

8. The answer generation unit Generate responses that reflect the children's emotions and use expressions that elicit positive emotions 2. The system of claim 1.

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