System

Abstract

An object of a system according to an embodiment is to draw out curiosity of a child and to share and deepen the curiosity among all family members.SOLUTION: A system includes a curiosity drawing part, an explanation part, and a communication promotion part. The curiosity drawer draws out the child's curiosity. The explanation unit easily explains the curiosity extracted by the curiosity extraction unit. The communication promotion unit promotes communication between the family members based on the explanation provided by the explanation 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 adequately stimulate children's curiosity and allow it to be shared and deepened by the whole family, so there is room for improvement.

[0005] The system according to the embodiment aims to stimulate children's curiosity and deepen it by sharing it with the whole family. [Means for solving the problem]

[0006] The system according to the embodiment includes a curiosity eliciting unit, an explanation unit, and a communication promoting unit. The curiosity eliciting unit elicits a child's curiosity. The explanation unit provides an easy-to-understand explanation for the curiosity elicited by the curiosity eliciting unit. The communication promoting unit promotes communication between family members based on the explanation provided by the explanation unit. [Effects of the Invention]

[0007] The system according to the embodiment can stimulate children's curiosity and deepen it by sharing it with the whole family. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The knowledge co-creation campus according to an embodiment of the present invention is a platform that promotes learning and growth for the whole family. This platform stimulates children's curiosity and provides an experience where they can explore it in depth together with their parents. In this way, the knowledge co-creation campus can promote learning and growth for the whole family and strengthen communication and bonds between family members.

[0029] A knowledge co-creation campus according to an embodiment includes a curiosity elicitation unit, an explanation unit, and a communication promotion unit. The curiosity elicitation unit elicits children's curiosity. For example, the generation AI provides appropriate answers to various questions and doubts that children have. For example, in response to the question, "Why is the sky blue?", the generation AI provides a simple explanation such as, "The sky is blue because sunlight is scattered by molecules in the atmosphere. Blue light in particular is scattered more strongly than other colors, which is why the sky appears blue to us." The explanation unit provides simple explanations to the curiosity elicited by the curiosity elicitation unit. For example, the generation AI explains specialized knowledge in simple terms to make it easy for children to understand. For example, when explaining "why the sky is blue," the generation AI avoids technical terms and uses concrete examples and metaphors. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to provide explanations that are easy for children to understand. The communication promotion unit promotes communication between family members based on the explanations provided by the explanation unit. For example, after explaining "why the sky is blue," the generative AI can ask a follow-up question such as "why is a sunset red?", allowing the whole family to pursue a deeper understanding. In this way, the knowledge co-creation campus according to the embodiment can promote the learning and growth of the whole family and strengthen communication and bonds between family members.

[0030] The curiosity elicitation unit analyzes a child's past question history, identifies individual interests based on the question history, and can suggest new questions based on those interests. For example, the curiosity elicitation unit uses a generative AI to analyze a child's past question history and identify interests in specific topics. For example, if a child has asked many questions about "dinosaurs" in the past, it can suggest a related new question such as "How did dinosaurs become extinct?" This can pique a child's interest and provide an opportunity for learning.

[0031] The Curiosity Elicitation Unit suggests related experiments and observations in response to children's questions, allowing them to deepen their understanding through actual experience. For example, in the Curiosity Elicitation Unit, the generative AI suggests simple experiments related to children's questions. For example, in response to the question, "Why is the sky blue?", it suggests a simple experiment to demonstrate the scattering of light and allows children to actually try it. This allows children to deepen their understanding through actual experience.

[0032] The explanation unit can automatically adjust the difficulty of the explanation according to the child's level of understanding. For example, the generation AI analyzes the child's level of understanding in real time and automatically adjusts the difficulty of the explanation. For example, if the child's understanding is not advanced, the explanation will be given in simpler terms. This makes it possible to provide an appropriate explanation according to the child's level of understanding.

[0033] The explanation section can incorporate interactive quizzes and games when giving an explanation to check the child's level of understanding. For example, when the generation AI gives an explanation, the explanation section can incorporate interactive quizzes to check the child's level of understanding. For example, after explaining "Why is the sky blue?", a related quiz can be asked. This allows the child's level of understanding to be checked as they progress through their learning.

[0034] The explanation unit can use visual animations and illustrations when providing an explanation to help children understand more intuitively. For example, when the generative AI provides an explanation, it can use visual animations to help children understand more intuitively. For example, in response to the explanation "Why is the sky blue?", it can provide an animation showing the scattering of light. This can help children understand using a visual approach.

[0035] The explanation section can introduce relevant historical background and cultural examples when providing an explanation, thereby stimulating children's interest. For example, when the generative AI provides an explanation, it can introduce relevant historical background to stimulate children's interest. For example, in response to the question, "Why is the sky blue?", it can introduce how ancient astronomers explained the color of the sky. This can stimulate children's interest through historical background and cultural examples.

[0036] The communication promotion unit takes into account the interests of all family members and can suggest common themes to delve deeper into. For example, the generation AI takes into account the interests of all family members and suggests common themes. For example, if all family members are interested in nature, it can suggest a theme to delve deeper into "forest ecosystems." This will draw out the interests of all family members and allow them to delve deeper into a common theme.

[0037] The communication promotion unit can provide discussion topics that all family members can participate in and promote the exchange of opinions. For example, the generative AI can suggest a discussion topic on "the impacts of climate change." This can promote the exchange of opinions among all family members and stimulate communication.

[0038] The communication promotion department can propose online workshops and seminars that the whole family can participate in. For example, the generative AI can propose an online workshop that the whole family can participate in. For example, it can propose a "science experiment workshop" and have the whole family participate. This makes it possible to propose online workshops and seminars that the whole family can participate in and provide common learning.

[0039] The Communication Promotion Department proposes experiment kits and activities that the whole family can enjoy, allowing them to deepen their understanding through actual experience. For example, the Generative AI proposes experiment kits that the whole family can enjoy. For example, it proposes a "chemical reaction experiment kit" and has the whole family conduct the experiment. This allows them to propose experiment kits and activities that the whole family can enjoy, allowing them to deepen their understanding through actual experience.

[0040] The communication promotion unit can provide a list of questions to promote communication between family members and stimulate conversation. For example, the generative AI in the communication promotion unit provides a list of questions to promote communication between family members. For example, it suggests questions such as, "What was the most fun thing you did today?" This can stimulate conversation between family members and promote communication.

[0041] The Communication Promotion Department proposes joint projects that all family members can participate in, strengthening bonds by working together. For example, the Generative AI proposes joint projects that all family members can participate in. For example, it proposes a "home vegetable garden project" where the whole family grows vegetables. This allows all family members to work together and strengthen bonds.

[0042] The communication promotion unit can suggest games and quizzes that the whole family can enjoy, thereby promoting communication. For example, the generation AI can suggest games that the whole family can enjoy. For example, it can suggest a "family quiz competition" so that the whole family can enjoy the quiz. This makes it possible to suggest games and quizzes that the whole family can enjoy, thereby promoting communication.

[0043] The communication promotion unit suggests events and activities that the whole family can participate in, strengthening bonds through shared experiences. For example, the generation AI suggests events that the whole family can participate in. For example, it suggests "family camping" and enjoying nature together as a family. This suggests events and activities that the whole family can participate in, strengthening bonds through shared experiences.

[0044] When providing specific learning examples, the explanation section suggests related experiments and observations, allowing children to deepen their understanding through actual experience. For example, when the generative AI provides a specific learning example, it suggests related experiments. For example, in response to the explanation "Why is the sky blue?", it suggests an experiment that shows the scattering of light and allows children to actually experience it. This allows children to deepen their understanding through actual experience through specific learning examples.

[0045] The explanation section can arouse children's interest by introducing relevant historical background and cultural examples when providing concrete learning examples. For example, when the generative AI provides concrete learning examples, it can introduce relevant historical background and arouse children's interest. For example, in response to the question, "Why is the sky blue?", it can introduce how ancient astronomers explained the color of the sky. This can arouse children's interest through historical background and cultural examples.

[0046] The explanation unit can use visual animations and illustrations when providing specific learning examples to help children understand more intuitively. For example, when the generative AI provides specific learning examples, it can use visual animations to help children understand more intuitively. For example, in response to the explanation "Why is the sky blue?", it can provide an animation showing the scattering of light. This can help children understand using a visual approach.

[0047] The explanation unit can recommend related books and videos when providing specific learning examples, thereby promoting deeper learning. For example, when the generative AI provides specific learning examples, the explanation unit can recommend related books. For example, in response to the explanation "Why is the sky blue?", it can recommend books on light scattering. This can promote deeper learning through related books and videos.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The Knowledge Co-Creation Campus can also be equipped with an "Experience Sharing Area." This provides a place for all family members to share their individual learnings and experiences. For example, it can provide an opportunity for children to give presentations to their families about what they learned at school. It is also possible for families to share photos and videos of trips or events they have taken together and hold discussions based on these. It is also possible to set up an online forum in which all family members can participate, promoting interaction with other families. This allows all family members to share their learnings and experiences and deepen mutual understanding.

[0050] The knowledge co-creation campus can also be equipped with a "feedback section." This section provides a forum for all family members to provide feedback on their child's learning progress and results. For example, parents can provide feedback on experiments or projects conducted by their children. Evaluation meetings can also be held in which all family members participate, allowing them to evaluate each other's achievements. It is also possible to provide opportunities for members to receive feedback from external experts. This allows all family members to evaluate each other's learning and promote further growth.

[0051] The Knowledge Co-Creation Campus can also be equipped with a "goal setting department." This provides a place for each family member to set individual goals for learning and growth and work toward them. For example, a child can choose a topic they want to study and create a study plan based on that. It is also possible to hold goal-setting meetings in which the whole family can participate and share their goals with each other. It is also possible to monitor progress toward achieving goals and provide advice as needed. This allows the whole family to work toward their goals and promote growth.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The curiosity elicitation section draws out a child's curiosity. For example, the generative AI provides appropriate answers to various doubts and questions that a child may have. For example, in response to the question, "Why is the sky blue?", the generative AI provides a simple explanation such as, "The sky is blue because sunlight is scattered by molecules in the atmosphere. Blue light in particular is scattered more strongly than other colors, which is why the sky appears blue to us." Step 2: The explanation unit provides an easy-to-understand explanation for the curiosity elicited by the curiosity elicitation unit. For example, the generation AI explains specialized knowledge in simple terms so that children can easily understand. For example, when explaining "why the sky is blue," it avoids technical terms and uses concrete examples and metaphors. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to provide an explanation that is easy for children to understand. Step 3: The communication promotion unit promotes communication between family members based on the explanation provided by the explanation unit. For example, after explaining "why the sky is blue," the generation AI can ask a follow-up question such as "why is a sunset red?", allowing the whole family to pursue a deeper understanding. In this way, the knowledge co-creation campus according to the embodiment can promote the learning and growth of all family members and strengthen communication and bonds between family members.

[0054] (Example 2) The knowledge co-creation campus according to an embodiment of the present invention is a platform that promotes learning and growth for the whole family. This platform stimulates children's curiosity and provides an experience where they can explore it in depth together with their parents. In this way, the knowledge co-creation campus can promote learning and growth for the whole family and strengthen communication and bonds between family members.

[0055] A knowledge co-creation campus according to an embodiment includes a curiosity elicitation unit, an explanation unit, and a communication promotion unit. The curiosity elicitation unit elicits children's curiosity. For example, the generation AI provides appropriate answers to various questions and doubts that children have. For example, in response to the question, "Why is the sky blue?", the generation AI provides a simple explanation such as, "The sky is blue because sunlight is scattered by molecules in the atmosphere. Blue light in particular is scattered more strongly than other colors, which is why the sky appears blue to us." The explanation unit provides simple explanations to the curiosity elicited by the curiosity elicitation unit. For example, the generation AI explains specialized knowledge in simple terms to make it easy for children to understand. For example, when explaining "why the sky is blue," the generation AI avoids technical terms and uses concrete examples and metaphors. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to provide explanations that are easy for children to understand. The communication promotion unit promotes communication between family members based on the explanations provided by the explanation unit. For example, after explaining "why the sky is blue," the generative AI can ask a follow-up question such as "why is a sunset red?", allowing the whole family to pursue a deeper understanding. In this way, the knowledge co-creation campus according to the embodiment can promote the learning and growth of the whole family and strengthen communication and bonds between family members.

[0056] The curiosity elicitation unit analyzes a child's past question history, identifies individual interests based on the question history, and can suggest new questions based on those interests. For example, the curiosity elicitation unit uses a generative AI to analyze a child's past question history and identify interests in specific topics. For example, if a child has asked many questions about "dinosaurs" in the past, it can suggest a related new question such as "How did dinosaurs become extinct?" This can pique a child's interest and provide an opportunity for learning.

[0057] The Curiosity Elicitation Unit suggests related experiments and observations in response to children's questions, allowing them to deepen their understanding through actual experience. For example, in the Curiosity Elicitation Unit, the generative AI suggests simple experiments related to children's questions. For example, in response to the question, "Why is the sky blue?", it suggests a simple experiment to demonstrate the scattering of light and allows children to actually try it. This allows children to deepen their understanding through actual experience.

[0058] The curiosity elicitation unit can use the emotion estimation function to analyze the emotions a child has when asking a question and generate questions that elicit positive emotions. For example, the curiosity elicitation unit can use the emotion estimation function to analyze the emotions a child has when asking a question in real time and generate questions that elicit positive emotions. For example, for an excited child, it can suggest a question such as "What do you want to know more about?" This can elicit positive emotions in children and increase their motivation to learn.

[0059] The explanation unit can automatically adjust the difficulty of the explanation according to the child's level of understanding. For example, the generation AI analyzes the child's level of understanding in real time and automatically adjusts the difficulty of the explanation. For example, if the child's understanding is not advanced, the explanation will be given in simpler terms. This makes it possible to provide an appropriate explanation according to the child's level of understanding.

[0060] The explanation section can incorporate interactive quizzes and games when giving an explanation to check the child's level of understanding. For example, when the generation AI gives an explanation, the explanation section can incorporate interactive quizzes to check the child's level of understanding. For example, after explaining "Why is the sky blue?", a related quiz can be asked. This allows the child's level of understanding to be checked as they progress through their learning.

[0061] The explanation unit uses the emotion estimation function to analyze the child's emotions during the explanation, and if the child's understanding is poor, the explanation unit can use a different approach. For example, the explanation unit uses the emotion estimation function to analyze the child's emotions during the explanation in real time, and if the child's understanding is poor, the explanation unit can use a different approach. For example, if the child is losing interest, the explanation can be given using specific examples. This makes it possible to provide flexible explanations that are tailored to the child's level of understanding.

[0062] The explanation unit can use visual animations and illustrations when providing an explanation to help children understand more intuitively. For example, when the generative AI provides an explanation, it can use visual animations to help children understand more intuitively. For example, in response to the explanation "Why is the sky blue?", it can provide an animation showing the scattering of light. This can help children understand using a visual approach.

[0063] The explanation section can introduce relevant historical background and cultural examples when providing an explanation, thereby stimulating children's interest. For example, when the generative AI provides an explanation, it can introduce relevant historical background to stimulate children's interest. For example, in response to the question, "Why is the sky blue?", it can introduce how ancient astronomers explained the color of the sky. This can stimulate children's interest through historical background and cultural examples.

[0064] The explanation unit can use the emotion estimation function to monitor the child's emotions in real time while giving the explanation, and adjust the explanation to elicit positive emotions. For example, the explanation unit can use the emotion estimation function to monitor the child's emotions in real time while giving the explanation, and adjust the explanation to elicit positive emotions. For example, if a child is excited, more interesting information can be provided. This makes it possible to provide flexible explanations that correspond to the child's emotions.

[0065] The communication promotion unit takes into account the interests of all family members and can suggest common themes to delve deeper into. For example, the generation AI takes into account the interests of all family members and suggests common themes. For example, if all family members are interested in nature, it can suggest a theme to delve deeper into "forest ecosystems." This will draw out the interests of all family members and allow them to delve deeper into a common theme.

[0066] The communication promotion unit can provide discussion topics that all family members can participate in and promote the exchange of opinions. For example, the generative AI can suggest a discussion topic on "the impacts of climate change." This can promote the exchange of opinions among all family members and stimulate communication.

[0067] The communication promotion unit can use the emotion estimation function to analyze the emotions of all family members and suggest discussions that will elicit positive emotions. For example, the communication promotion unit can use the emotion estimation function to analyze the emotions of all family members in real time and suggest discussions that will elicit positive emotions. For example, it can select a topic that everyone can enjoy. This makes it possible to suggest discussions that are in line with the emotions of all family members and elicit positive emotions.

[0068] The communication promotion department can propose online workshops and seminars that the whole family can participate in. For example, the generative AI can propose an online workshop that the whole family can participate in. For example, it can propose a "science experiment workshop" and have the whole family participate. This makes it possible to propose online workshops and seminars that the whole family can participate in and provide common learning.

[0069] The Communication Promotion Department proposes experiment kits and activities that the whole family can enjoy, allowing them to deepen their understanding through actual experience. For example, the Generative AI proposes experiment kits that the whole family can enjoy. For example, it proposes a "chemical reaction experiment kit" and has the whole family conduct the experiment. This allows them to propose experiment kits and activities that the whole family can enjoy, allowing them to deepen their understanding through actual experience.

[0070] The communication promotion unit can use the emotion estimation function to monitor the emotions of all family members in real time and suggest activities that will elicit positive emotions. For example, the communication promotion unit can use the emotion estimation function to monitor the emotions of all family members in real time and suggest activities that will elicit positive emotions. For example, it can suggest games that everyone can enjoy. In this way, it is possible to suggest activities that are in line with the emotions of all family members and elicit positive emotions.

[0071] The communication promotion unit can provide a list of questions to promote communication between family members and stimulate conversation. For example, the generative AI in the communication promotion unit provides a list of questions to promote communication between family members. For example, it suggests questions such as, "What was the most fun thing you did today?" This can stimulate conversation between family members and promote communication.

[0072] The Communication Promotion Department proposes joint projects that all family members can participate in, strengthening bonds by working together. For example, the Generative AI proposes joint projects that all family members can participate in. For example, it proposes a "home vegetable garden project" where the whole family grows vegetables. This allows all family members to work together and strengthen bonds.

[0073] The communication promotion unit can use the emotion estimation function to analyze the emotions of all family members and suggest communication that will elicit positive emotions. For example, the communication promotion unit can use the emotion estimation function to analyze the emotions of all family members in real time and suggest communication that will elicit positive emotions. For example, it can select topics that everyone can enjoy. This makes it possible to suggest communication that is appropriate for the emotions of all family members and elicit positive emotions.

[0074] The communication promotion unit can suggest games and quizzes that the whole family can enjoy, thereby promoting communication. For example, the generation AI can suggest games that the whole family can enjoy. For example, it can suggest a "family quiz competition" so that the whole family can enjoy the quiz. This makes it possible to suggest games and quizzes that the whole family can enjoy, thereby promoting communication.

[0075] The communication promotion unit suggests events and activities that the whole family can participate in, strengthening bonds through shared experiences. For example, the generation AI suggests events that the whole family can participate in. For example, it suggests "family camping" and enjoying nature together as a family. This suggests events and activities that the whole family can participate in, strengthening bonds through shared experiences.

[0076] The communication promotion unit can use the emotion estimation function to monitor the emotions of all family members in real time and suggest events that will elicit positive emotions. For example, the communication promotion unit can use the emotion estimation function to monitor the emotions of all family members in real time and suggest events that will elicit positive emotions. For example, it can select events that everyone can enjoy. In this way, it is possible to suggest events that are in line with the emotions of all family members and elicit positive emotions.

[0077] When providing specific learning examples, the explanation section suggests related experiments and observations, allowing children to deepen their understanding through actual experience. For example, when the generative AI provides a specific learning example, it suggests related experiments. For example, in response to the explanation "Why is the sky blue?", it suggests an experiment that shows the scattering of light and allows children to actually experience it. This allows children to deepen their understanding through actual experience through specific learning examples.

[0078] The explanation section can arouse children's interest by introducing relevant historical background and cultural examples when providing concrete learning examples. For example, when the generative AI provides concrete learning examples, it can introduce relevant historical background and arouse children's interest. For example, in response to the question, "Why is the sky blue?", it can introduce how ancient astronomers explained the color of the sky. This can arouse children's interest through historical background and cultural examples.

[0079] The explanation unit can use the emotion estimation function to analyze a child's emotions regarding specific learning examples and provide examples that elicit positive emotions. For example, the explanation unit can use the emotion estimation function to analyze a child's emotions regarding specific learning examples in real time and provide examples that elicit positive emotions. For example, an excited child can be provided with more interesting information. This makes it possible to provide learning examples that correspond to the child's emotions and elicit positive emotions.

[0080] The explanation unit can use visual animations and illustrations when providing specific learning examples to help children understand more intuitively. For example, when the generative AI provides specific learning examples, it can use visual animations to help children understand more intuitively. For example, in response to the explanation "Why is the sky blue?", it can provide an animation showing the scattering of light. This can help children understand using a visual approach.

[0081] The explanation unit can recommend related books and videos when providing specific learning examples, thereby promoting deeper learning. For example, when the generative AI provides specific learning examples, the explanation unit can recommend related books. For example, in response to the explanation "Why is the sky blue?", it can recommend books on light scattering. This can promote deeper learning through related books and videos.

[0082] The explanation unit can use the emotion estimation function to monitor the child's emotions regarding specific learning examples in real time and provide examples that elicit positive emotions. For example, the explanation unit can use the emotion estimation function to monitor the child's emotions regarding specific learning examples in real time and provide examples that elicit positive emotions. For example, an excited child can be provided with more interesting information. This makes it possible to provide learning examples that correspond to the child's emotions and elicit positive emotions.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The Knowledge Co-Creation Campus can also be equipped with an "Experience Sharing Area." This provides a place for all family members to share their individual learnings and experiences. For example, it can provide an opportunity for children to give presentations to their families about what they learned at school. It is also possible for families to share photos and videos of trips or events they have taken together and hold discussions based on these. It is also possible to set up an online forum in which all family members can participate, promoting interaction with other families. This allows all family members to share their learnings and experiences and deepen mutual understanding.

[0085] The knowledge co-creation campus can also be equipped with a "feedback section." This section provides a forum for all family members to provide feedback on their child's learning progress and results. For example, parents can provide feedback on experiments or projects conducted by their children. Evaluation meetings can also be held in which all family members participate, allowing them to evaluate each other's achievements. It is also possible to provide opportunities for members to receive feedback from external experts. This allows all family members to evaluate each other's learning and promote further growth.

[0086] The Knowledge Co-Creation Campus can also be equipped with an "Emotion Sharing Area." This area provides a place for all family members to share their feelings about learning and experiences. For example, a child can share with family members the joy and surprise they feel when they learn something new. It is also possible to hold an emotion sharing session in which all family members can participate and share their emotions with each other. Furthermore, the emotion sharing area can use emotion estimation functionality to monitor the emotions of all family members in real time and suggest activities that will elicit positive emotions. This allows all family members to understand each other's emotions and deepen their bonds.

[0087] The Knowledge Co-Creation Campus can also be equipped with a "goal setting department." This provides a place for each family member to set individual goals for learning and growth and work toward them. For example, a child can choose a topic they want to study and create a study plan based on that. It is also possible to hold goal-setting meetings in which the whole family can participate and share their goals with each other. It is also possible to monitor progress toward achieving goals and provide advice as needed. This allows the whole family to work toward their goals and promote growth.

[0088] The Knowledge Co-Creation Campus can also be equipped with an "emotion analysis unit." This unit analyzes the emotions of all family members in real time and monitors changes in emotions in response to learning and experiences. For example, it can analyze the emotions felt when a child learns new knowledge and provide feedback that elicits positive emotions. It can also hold emotion analysis sessions in which all family members can participate and share their emotions. Furthermore, the emotion analysis unit can use its emotion estimation function to monitor the emotions of all family members in real time and suggest activities that elicit positive emotions. This allows all family members to understand each other's emotions and deepen their bonds.

[0089] The Knowledge Co-Creation Campus can also be equipped with a "Reflection Club." This provides a place for all family members to reflect on their learning and experiences and conduct self-evaluations. For example, children can self-evaluate experiments or projects they have conducted and reflect on the results of their learning. It is also possible to hold reflection sessions in which all family members can participate and share what they have learned. Furthermore, the Reflection Club can use an emotion estimation function to monitor the emotions of all family members in real time and provide feedback that will elicit positive emotions. This allows all family members to reflect on their learning and promote further growth.

[0090] The Knowledge Co-Creation Campus can also be equipped with an "Inspiration Club." This provides a place for the whole family to gain new ideas and discoveries. For example, it could introduce the latest research and discoveries related to topics that interest children. It could also hold inspiration sessions in which the whole family can participate and share their ideas. Furthermore, the Inspiration Club can use an emotion estimation function to monitor the emotions of all family members in real time and provide information that will elicit positive emotions. This allows the whole family to gain new ideas and discoveries and deepen their learning.

[0091] The knowledge co-creation campus can further be equipped with a "resource provision unit." The resource provision unit provides a place to provide the resources necessary for learning and experience for all family members. For example, it can provide books and teaching materials related to topics that children want to learn. It can also hold resource sharing sessions in which all family members can participate and share useful resources with each other. Furthermore, the resource provision unit can use emotion estimation functions to monitor the emotions of all family members in real time and provide resources that will elicit positive emotions. This allows all family members to obtain the resources they need to learn and deepen their learning.

[0092] The Knowledge Co-Creation Campus can also be equipped with a "Collaboration Club." This provides a place for all family members to work on projects together. For example, the family can propose a science experiment project and work together to carry out the experiment. It is also possible to hold collaboration sessions in which all family members participate and share their ideas. Furthermore, the Collaboration Club can use emotion estimation functionality to monitor the emotions of all family members in real time and suggest projects that will elicit positive emotions. This allows the whole family to work together and deepen their bonds.

[0093] The knowledge co-creation campus can also be equipped with a "relaxation club." This provides a place where the whole family can relax and enjoy learning. For example, a relaxation session could be held for the whole family to participate in, with yoga or meditation. The relaxation club could also use emotion estimation functionality to monitor the emotions of all family members in real time and suggest activities to help them maintain a relaxed state. This allows the whole family to enjoy learning while relaxing.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The curiosity elicitation section draws out a child's curiosity. For example, the generative AI provides appropriate answers to various doubts and questions that a child may have. For example, in response to the question, "Why is the sky blue?", the generative AI provides a simple explanation such as, "The sky is blue because sunlight is scattered by molecules in the atmosphere. Blue light in particular is scattered more strongly than other colors, which is why the sky appears blue to us." Step 2: The explanation unit provides an easy-to-understand explanation for the curiosity elicited by the curiosity elicitation unit. For example, the generation AI explains specialized knowledge in simple terms so that children can easily understand. For example, when explaining "why the sky is blue," it avoids technical terms and uses concrete examples and metaphors. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to provide an explanation that is easy for children to understand. Step 3: The communication promotion unit promotes communication between family members based on the explanation provided by the explanation unit. For example, after explaining "why the sky is blue," the generation AI can ask a follow-up question such as "why is a sunset red?", allowing the whole family to pursue a deeper understanding. In this way, the knowledge co-creation campus according to the embodiment can promote the learning and growth of all family members and strengthen communication and bonds between family members.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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. Note that 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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]

[0163] 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 curiosity drawer that draws out children's curiosity, an explanation unit that provides an easy explanation to the curiosity elicited by the curiosity eliciting unit; a communication promotion unit that promotes communication between family members based on the explanation provided by the explanation unit. A system characterized by:

2. The curiosity eliciting unit Analyzing a child's past question history, identifying individual interests based on said question history, and suggesting new questions based on said interests 2. The system of claim 1.

3. The explanation section Automatically adjusts the difficulty of explanations according to the child's level of understanding 2. The system of claim 1.

4. The communication promotion unit Consider the interests of all family members and suggest common themes to explore further 2. The system of claim 1.

5. The curiosity eliciting unit Analyzes the emotions children feel when asking questions and generates questions that elicit positive emotions 2. The system of claim 1.

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