System

Abstract

An object of a system according to an embodiment is to provide an appropriate conversation and video according to the age of a child.SOLUTION: A system according to an embodiment includes an age setting unit, a speech generation unit, and a video projection unit. The age setting unit sets the age of the child. The conversation generation unit generates a conversation based on the age set by the age setting unit. The image projection unit projects an image related to the conversation content generated by the speech 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 challenge of making it difficult to provide appropriate conversations and images for children of different ages.

[0005] The system according to the embodiment aims to provide appropriate conversations and images according to the age of the child. [Means for solving the problem]

[0006] The system according to the embodiment includes an age setting unit, a conversation generation unit, and an image projection unit. The age setting unit sets the age of the child. The conversation generation unit generates a conversation based on the age set by the age setting unit. The image projection unit projects an image related to the content of the conversation generated by the conversation generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate conversations and images according to the age of the child. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A smart speaker system according to an embodiment of the present invention is a system that sets a child's age, generates conversations appropriate for that age, and projects related images onto a television or mobile phone, thereby providing educational conversations and images appropriate for the child's age.

[0029] A smart speaker system according to an embodiment includes an age setting unit, a conversation generation unit, and an image projection unit. The age setting unit sets the child's age. For example, the age setting unit allows a parent to input the child's age through a smartphone app. The age setting unit also allows the child to set the child's age using voice input. The age setting unit can set multiple age categories and adjust the conversation level based on the age category. For example, the age setting unit can set age categories such as 0-3 years old, 4-6 years old, and 7-10 years old, and provide a conversation level corresponding to the age category. The conversation generation unit generates conversation based on the age set by the age setting unit. For example, the conversation generation unit generates conversation content appropriate for the child's age using a generation AI (e.g., a text generation AI or a multimodal generation AI). The conversation generation unit can also adjust the difficulty of vocabulary and grammar according to the child's age. For example, a 3-year-old child is spoken to using simple words and short sentences, while a 10-year-old child is spoken to using more complex words and longer sentences. The image projection unit projects an image related to the conversation content generated by the conversation generation unit. For example, the image projection unit projects the image selected by the generation AI onto a television or mobile phone. The image projection unit can also be compatible with projectors and VR headsets. For example, the image projection unit connects to a projector via Wi-Fi or Bluetooth and projects the image. This allows the smart speaker system according to the embodiment to provide educational conversations and images appropriate for the child's age. For example, if a child asks, "Tell me about dinosaurs," the generation AI selects an image related to dinosaurs and projects it onto a television or mobile phone. This also supports visual learning for children.

[0030] The age setting unit can automatically adjust the conversation level according to the child's learning progress. For example, the age setting unit periodically evaluates the child's learning progress and builds a system that automatically adjusts the conversation level based on the results. For example, it may work in conjunction with a learning app to obtain progress data. The age setting unit also develops an algorithm that gradually increases the difficulty of conversation content according to the child's learning progress. For example, it may start with simple questions and gradually move on to more complex questions. The age setting unit also builds a system that automatically adjusts conversation topics and vocabulary based on the child's learning progress. For example, it may incorporate technical terms and complex concepts as the child's learning progress progresses. This supports effective learning by providing a conversation level that matches the child's learning progress.

[0031] The conversation generation unit can personalize the conversation content by incorporating the child's name or favorite character. The conversation generation unit, for example, frequently incorporates the child's name into the conversation to increase familiarity. For example, it generates conversations such as, "Hey, what did you do today?" The conversation generation unit also incorporates the child's favorite character into the conversation to provide an experience as if the character is talking to them. For example, it generates conversations such as, "Mickey is asking you a question." The conversation generation unit also builds a system that customizes the conversation content based on the child's interests and concerns. For example, for a child who likes animals, it incorporates many animal-related topics. In this way, by incorporating the child's name and favorite character, it provides a familiar conversation experience.

[0032] The image projection unit can project images onto a projector or VR headset. For example, the image projection unit extends the image output function of a smart speaker to enable it to project images onto a projector. For example, it connects to a projector via Wi-Fi or Bluetooth and projects images. The image projection unit also builds a system that converts the smart speaker's image output into a VR format to support VR headsets. For example, it generates 360-degree images or 3D images and sends them to the VR headset. The image projection unit also enables the smart speaker's image output to be projected onto multiple devices simultaneously. For example, it can simultaneously send images to a TV, mobile phone, projector, and VR headset. This allows it to support projectors and VR headsets, providing a more diverse video experience.

[0033] The age setting unit can customize the content of the conversation based on the child's interests and concerns. For example, the age setting unit periodically evaluates the child's interests and concerns and builds a system that customizes the content of the conversation based on the results. For example, it could incorporate many topics related to favorite animals or hobbies. The age setting unit also develops an algorithm that automatically selects conversation topics based on the child's interests and concerns. For example, it could provide new topics based on recent interests. The age setting unit also builds a system that dynamically changes the content of the conversation based on the child's interests and concerns. For example, it could switch to a new topic if their interests change. This improves learning effectiveness by providing conversations that are tailored to the child's interests and concerns.

[0034] The conversation generation unit can provide conversation content in multiple languages ​​to support bilingual education. For example, the conversation generation unit builds a system that provides conversation content in multiple languages, allowing children to receive bilingual education naturally. For example, conversations are held in both English and Japanese. The conversation generation unit also develops an algorithm that provides conversations at an appropriate language level depending on the child's age and learning progress. For example, starting with simple English and gradually increasing the difficulty level. The conversation generation unit also builds a system that incorporates cultural elements into the conversation content to support bilingual education. For example, topics related to the culture and customs of English-speaking countries are provided. In this way, bilingual education is supported by providing conversations in multiple languages.

[0035] The video projection unit can customize the content of the video according to the child's learning progress and interests. The video projection unit, for example, builds a system that customizes the content of the video based on the child's learning progress. For example, as the child's learning progress advances, the video projection unit provides videos with more advanced content. The video projection unit also develops an algorithm that automatically selects video topics based on the child's interests. For example, a child who is interested in animals is provided with videos related to animals. The video projection unit also builds a system that dynamically changes the difficulty and content of the video according to the child's learning progress and interests. For example, videos starting with easy content and gradually increasing in difficulty are provided. This improves the learning effect by providing videos that match the child's learning progress and interests.

[0036] The image projection unit makes the content of the image interactive, allowing children to react to the image and thereby enhance learning effectiveness. The image projection unit, for example, builds a system that makes the content of the image interactive and allows children to react to the image and thereby enhance learning effectiveness. For example, the image projection unit can ask questions to characters in the image. The image projection unit also provides interactive images and enhances learning effectiveness by allowing children to manipulate objects in the image. For example, the image projection unit can provide images in the form of puzzles or quizzes. The image projection unit also develops an algorithm that makes the content of the image interactive and enhances learning effectiveness by allowing children to react to the image. For example, the image projection unit can interact with characters in the image. In this way, the provision of interactive images enhances learning effectiveness for children.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The smart speaker system may further include a voice recognition unit. The voice recognition unit may analyze a child's pronunciation and word usage to support pronunciation improvement and vocabulary expansion. For example, the voice recognition unit may analyze words pronounced by a child and provide feedback on correct pronunciation. The voice recognition unit may also record and periodically evaluate the child's pronunciation progress. For example, the voice recognition unit may evaluate the accuracy and fluency of pronunciation and suggest areas for improvement. The voice recognition unit may also provide appropriate pronunciation practice content based on the child's pronunciation. For example, the voice recognition unit may suggest pronunciation practice for specific sounds or words. This may support the child's pronunciation and vocabulary improvement.

[0039] The smart speaker system can further include a sensor unit. The sensor unit can detect a child's movements and provide exercise or gymnastics guidance. For example, the sensor unit can analyze a child's movements in real time and provide feedback on the correct exercise method. The sensor unit can also record the child's exercise progress and periodically evaluate it. For example, it can evaluate the accuracy and endurance of the exercise and suggest areas for improvement. The sensor unit can also provide an appropriate exercise program based on the child's movements. For example, it can suggest specific exercise or gymnastics instruction. This can support the improvement of a child's motor skills.

[0040] The smart speaker system can further include a health management unit. The health management unit can monitor a child's health condition and provide appropriate advice. For example, the health management unit can measure a child's body temperature and heart rate and issue an alert if any abnormalities are detected. The health management unit can also manage a child's diet and sleep records to support a balanced lifestyle. For example, it can evaluate the nutritional balance of a child's diet and suggest areas for improvement. The health management unit can also provide appropriate advice on exercise and rest based on the child's health condition. For example, if the child is not getting enough exercise, it can provide advice to encourage exercise. This allows for comprehensive management of a child's health condition and supports a healthy lifestyle.

[0041] The smart speaker system may further include a learning progress management unit. The learning progress management unit can monitor a child's learning progress and provide an appropriate learning plan. For example, the learning progress management unit records a child's learning content and progress and periodically evaluates them. The learning progress management unit can also suggest an appropriate learning plan based on the child's learning progress. For example, it can suggest strengthening specific subjects or skills. The learning progress management unit can also customize learning content according to the child's learning progress. For example, it can provide a learning plan that focuses on weak areas. This makes it possible to comprehensively manage a child's learning progress and support effective learning.

[0042] The smart speaker system can further be equipped with functions to promote parent-child communication. For example, it can provide quizzes and games that parents and children can enjoy together. It can also provide puzzles and missions that parents and children can solve together. Furthermore, to promote parent-child communication, it can provide educational content that parents and children can learn together. For example, it can provide a guide for parents and children to conduct science experiments together. It can also provide storytelling that parents and children can enjoy together. For example, it can generate stories that parents and children can tell alternately. This can promote parent-child communication and deepen the bond between them.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The age setting unit sets the child's age. For example, a parent can enter the child's age through a smartphone app. Alternatively, the child can set their own age using voice input. Furthermore, the age setting unit can set multiple age categories and adjust the conversation level based on them. For example, age categories such as 0-3 years old, 4-6 years old, and 7-10 years old can be set and the corresponding conversation level provided. Step 2: The conversation generation unit generates conversations based on the age set by the age setting unit. For example, it uses generation AI (e.g., text generation AI or multimodal generation AI) to generate conversation content appropriate for the child's age. It can also adjust the difficulty of vocabulary and grammar according to the child's age. For example, it can speak to a 3-year-old child using simple words and short sentences, and to a 10-year-old child using more complex words and longer sentences. Step 3: The image projection unit projects an image related to the conversation content generated by the conversation generation unit. For example, the image selected by the generation AI can be projected onto a television or mobile phone. It can also be used on projectors and VR headsets. For example, it can connect to a projector via Wi-Fi or Bluetooth and project the image.

[0045] (Example 2) A smart speaker system according to an embodiment of the present invention is a system that sets a child's age, generates conversations appropriate for that age, and projects related images onto a television or mobile phone, thereby providing educational conversations and images appropriate for the child's age.

[0046] A smart speaker system according to an embodiment includes an age setting unit, a conversation generation unit, and an image projection unit. The age setting unit sets the child's age. For example, the age setting unit allows a parent to input the child's age through a smartphone app. The age setting unit also allows the child to set the child's age using voice input. The age setting unit can set multiple age categories and adjust the conversation level based on the age category. For example, the age setting unit can set age categories such as 0-3 years old, 4-6 years old, and 7-10 years old, and provide a conversation level corresponding to the age category. The conversation generation unit generates conversation based on the age set by the age setting unit. For example, the conversation generation unit generates conversation content appropriate for the child's age using a generation AI (e.g., a text generation AI or a multimodal generation AI). The conversation generation unit can also adjust the difficulty of vocabulary and grammar according to the child's age. For example, a 3-year-old child is spoken to using simple words and short sentences, while a 10-year-old child is spoken to using more complex words and longer sentences. The image projection unit projects an image related to the conversation content generated by the conversation generation unit. For example, the image projection unit projects the image selected by the generation AI onto a television or mobile phone. The image projection unit can also be compatible with projectors and VR headsets. For example, the image projection unit connects to a projector via Wi-Fi or Bluetooth and projects the image. This allows the smart speaker system according to the embodiment to provide educational conversations and images appropriate for the child's age. For example, if a child asks, "Tell me about dinosaurs," the generation AI selects an image related to dinosaurs and projects it onto a television or mobile phone. This also supports visual learning for children.

[0047] The age setting unit can automatically adjust the conversation level according to the child's learning progress. For example, the age setting unit periodically evaluates the child's learning progress and builds a system that automatically adjusts the conversation level based on the results. For example, it may work in conjunction with a learning app to obtain progress data. The age setting unit also develops an algorithm that gradually increases the difficulty of conversation content according to the child's learning progress. For example, it may start with simple questions and gradually move on to more complex questions. The age setting unit also builds a system that automatically adjusts conversation topics and vocabulary based on the child's learning progress. For example, it may incorporate technical terms and complex concepts as the child's learning progress progresses. This supports effective learning by providing a conversation level that matches the child's learning progress.

[0048] The conversation generation unit can personalize the conversation content by incorporating the child's name or favorite character. The conversation generation unit, for example, frequently incorporates the child's name into the conversation to increase familiarity. For example, it generates conversations such as, "Hey, what did you do today?" The conversation generation unit also incorporates the child's favorite character into the conversation to provide an experience as if the character is talking to them. For example, it generates conversations such as, "Mickey is asking you a question." The conversation generation unit also builds a system that customizes the conversation content based on the child's interests and concerns. For example, for a child who likes animals, it incorporates many animal-related topics. In this way, by incorporating the child's name and favorite character, it provides a familiar conversation experience.

[0049] The image projection unit can project images onto a projector or VR headset. For example, the image projection unit extends the image output function of a smart speaker to enable it to project images onto a projector. For example, it connects to a projector via Wi-Fi or Bluetooth and projects images. The image projection unit also builds a system that converts the smart speaker's image output into a VR format to support VR headsets. For example, it generates 360-degree images or 3D images and sends them to the VR headset. The image projection unit also enables the smart speaker's image output to be projected onto multiple devices simultaneously. For example, it can simultaneously send images to a TV, mobile phone, projector, and VR headset. This allows it to support projectors and VR headsets, providing a more diverse video experience.

[0050] The video projection unit can use the emotion estimation function to automatically select and project video that is likely to interest children. The video projection unit, for example, uses the emotion estimation function to build a system that automatically selects video that is likely to interest children. For example, it records video of when a child smiles and provides similar video. The video projection unit also analyzes children's emotional reactions in real time and develops an algorithm that dynamically selects video that is likely to interest children. For example, it provides video that shows a child's excitement preferentially. The video projection unit also builds a system that automatically selects and projects video that is likely to interest children based on the emotion estimation data. For example, it continuously provides video that shows a child's interest. This improves learning effectiveness by providing video that matches children's interests.

[0051] The conversation generation unit can use the emotion estimation function to change the tone and content of the conversation depending on the child's emotional state. For example, using the emotion estimation function, the conversation generation unit will speak to the child in a bright tone when the child is happy. For example, it will generate a conversation such as, "You look like you're having so much fun today!". The conversation generation unit also generates a comforting conversation in a gentle tone when the child is sad. For example, it will generate a conversation such as, "What's wrong? Did something happen?" The conversation generation unit also builds a system that adjusts the content of the conversation depending on the child's emotional state. For example, it will provide a topic that will calm the child when the child is excited. This provides a more familiar experience by providing conversation that suits the child's emotional state.

[0052] The age setting unit can customize the content of the conversation based on the child's interests and concerns. For example, the age setting unit periodically evaluates the child's interests and concerns and builds a system that customizes the content of the conversation based on the results. For example, it could incorporate many topics related to favorite animals or hobbies. The age setting unit also develops an algorithm that automatically selects conversation topics based on the child's interests and concerns. For example, it could provide new topics based on recent interests. The age setting unit also builds a system that dynamically changes the content of the conversation based on the child's interests and concerns. For example, it could switch to a new topic if their interests change. This improves learning effectiveness by providing conversations that are tailored to the child's interests and concerns.

[0053] The conversation generation unit can provide conversation content in multiple languages ​​to support bilingual education. For example, the conversation generation unit builds a system that provides conversation content in multiple languages, allowing children to receive bilingual education naturally. For example, conversations are held in both English and Japanese. The conversation generation unit also develops an algorithm that provides conversations at an appropriate language level depending on the child's age and learning progress. For example, starting with simple English and gradually increasing the difficulty level. The conversation generation unit also builds a system that incorporates cultural elements into the conversation content to support bilingual education. For example, topics related to the culture and customs of English-speaking countries are provided. In this way, bilingual education is supported by providing conversations in multiple languages.

[0054] The conversation generation unit can use the emotion estimation function to automatically select topics that are likely to interest children and incorporate them into the conversation. The conversation generation unit, for example, uses the emotion estimation function to build a system that automatically selects topics that are likely to interest children. For example, it records topics that make children smile and provides similar topics. The conversation generation unit also analyzes children's emotional reactions in real time and develops an algorithm that dynamically selects topics that are likely to interest children. For example, it provides topics that make children excited preferentially. The conversation generation unit also builds a system that automatically selects topics that are likely to interest children based on the emotion estimation data and incorporates them into the conversation. For example, it continuously provides topics that children have shown interest in. This improves learning effectiveness by providing topics that match children's interests.

[0055] The video projection unit can customize the content of the video according to the child's learning progress and interests. The video projection unit, for example, builds a system that customizes the content of the video based on the child's learning progress. For example, as the child's learning progress advances, the video projection unit provides videos with more advanced content. The video projection unit also develops an algorithm that automatically selects video topics based on the child's interests. For example, a child who is interested in animals is provided with videos related to animals. The video projection unit also builds a system that dynamically changes the difficulty and content of the video according to the child's learning progress and interests. For example, videos starting with easy content and gradually increasing in difficulty are provided. This improves the learning effect by providing videos that match the child's learning progress and interests.

[0056] The image projection unit makes the content of the image interactive, allowing children to react to the image and thereby enhance learning effectiveness. The image projection unit, for example, builds a system that makes the content of the image interactive and allows children to react to the image and thereby enhance learning effectiveness. For example, the image projection unit can ask questions to characters in the image. The image projection unit also provides interactive images and enhances learning effectiveness by allowing children to manipulate objects in the image. For example, the image projection unit can provide images in the form of puzzles or quizzes. The image projection unit also develops an algorithm that makes the content of the image interactive and enhances learning effectiveness by allowing children to react to the image. For example, the image projection unit can interact with characters in the image. In this way, the provision of interactive images enhances learning effectiveness for children.

[0057] The video projection unit uses the emotion estimation function to monitor the emotional state of the child while watching the video and can switch the video at the appropriate timing. For example, the video projection unit uses the emotion estimation function to build a system that monitors the emotional state of the child while watching the video in real time. For example, it switches the video when the child is excited. The video projection unit also develops an algorithm that dynamically changes the content of the video according to the child's emotional state. For example, it provides new video when the child loses interest. The video projection unit also builds a system that monitors the emotional state of the child while watching the video based on the emotion estimation data and switches the video at the appropriate timing. For example, it switches the video when the child is bored. In this way, switching the video according to the child's emotional state improves learning effectiveness.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The smart speaker system may further include a voice recognition unit. The voice recognition unit may analyze a child's pronunciation and word usage to support pronunciation improvement and vocabulary expansion. For example, the voice recognition unit may analyze words pronounced by a child and provide feedback on correct pronunciation. The voice recognition unit may also record and periodically evaluate the child's pronunciation progress. For example, the voice recognition unit may evaluate the accuracy and fluency of pronunciation and suggest areas for improvement. The voice recognition unit may also provide appropriate pronunciation practice content based on the child's pronunciation. For example, the voice recognition unit may suggest pronunciation practice for specific sounds or words. This may support the child's pronunciation and vocabulary improvement.

[0060] The smart speaker system can further include a sensor unit. The sensor unit can detect a child's movements and provide exercise or gymnastics guidance. For example, the sensor unit can analyze a child's movements in real time and provide feedback on the correct exercise method. The sensor unit can also record the child's exercise progress and periodically evaluate it. For example, it can evaluate the accuracy and endurance of the exercise and suggest areas for improvement. The sensor unit can also provide an appropriate exercise program based on the child's movements. For example, it can suggest specific exercise or gymnastics instruction. This can support the improvement of a child's motor skills.

[0061] The smart speaker system may further include a music player. The music player can play appropriate music according to the child's emotional state. For example, when the child is relaxed, it can play calm music. When the child is excited, it can play lively music. Furthermore, the music player can adjust the genre and tempo of the music based on the child's emotional state. For example, when the child wants to calm down, it can play slow music. The music player can also automatically adjust the volume of the music according to the child's emotional state. For example, when the child is concentrating, it can lower the volume of the music. In this way, music that matches the child's emotional state can be provided to support relaxation and concentration.

[0062] The smart speaker system may further include a lighting control unit. The lighting control unit can adjust the lighting in the room according to the child's emotional state. For example, when the child is relaxed, warm lighting can be provided. When the child is concentrating, white lighting can be provided. The lighting control unit can also adjust the brightness and color of the lighting based on the child's emotional state. For example, when the child wants to sleep, dimmer lighting can be provided. The lighting control unit can also automatically change the lighting pattern according to the child's emotional state. For example, when the child is excited, a rhythmic lighting pattern can be provided. This makes it possible to support relaxation and concentration by providing lighting according to the child's emotional state.

[0063] The smart speaker system can further include a health management unit. The health management unit can monitor a child's health condition and provide appropriate advice. For example, the health management unit can measure a child's body temperature and heart rate and issue an alert if any abnormalities are detected. The health management unit can also manage a child's diet and sleep records to support a balanced lifestyle. For example, it can evaluate the nutritional balance of a child's diet and suggest areas for improvement. The health management unit can also provide appropriate advice on exercise and rest based on the child's health condition. For example, if the child is not getting enough exercise, it can provide advice to encourage exercise. This allows for comprehensive management of a child's health condition and supports a healthy lifestyle.

[0064] The smart speaker system may further include a learning progress management unit. The learning progress management unit can monitor a child's learning progress and provide an appropriate learning plan. For example, the learning progress management unit records a child's learning content and progress and periodically evaluates them. The learning progress management unit can also suggest an appropriate learning plan based on the child's learning progress. For example, it can suggest strengthening specific subjects or skills. The learning progress management unit can also customize learning content according to the child's learning progress. For example, it can provide a learning plan that focuses on weak areas. This makes it possible to comprehensively manage a child's learning progress and support effective learning.

[0065] The smart speaker system can further use emotion estimation to provide storytelling that is tailored to the child's emotional state. For example, when a child is relaxed, a calming story can be provided. When a child is excited, an adventurous story can be provided. Furthermore, the emotion estimation function can be used to adjust the content and tone of the story based on the child's emotional state. For example, when a child is sad, a comforting story can be provided. The emotion estimation function can also be used to dynamically change the progression of the story based on the child's emotional state. For example, when a child loses interest, a new development can be added. This allows for storytelling tailored to the child's emotional state, providing a more intimate experience.

[0066] The smart speaker system can also use its emotion estimation function to provide learning content that matches a child's emotional state. For example, when a child is relaxed, it can provide learning content that helps them concentrate. When a child is excited, it can provide learning content that helps them release their energy. The emotion estimation function can also be used to adjust the difficulty and topics of the learning content based on the child's emotional state. For example, when a child is tired, it can provide easy learning content. The emotion estimation function can also be used to dynamically change the progress of the learning content according to the child's emotional state. For example, when a child loses interest, it can add a new topic. This makes it possible to provide learning content that matches a child's emotional state, thereby supporting effective learning.

[0067] The smart speaker system can also use its emotion estimation function to provide relaxation content that matches a child's emotional state. For example, when a child is feeling stressed, it can provide relaxation music or a meditation guide. When a child is relaxing, it can provide calming natural sounds. The emotion estimation function can also be used to adjust the type and length of relaxation content based on the child's emotional state. For example, when a child wants to relax for a short period of time, it can provide a short meditation guide. The emotion estimation function can also be used to dynamically change the progress of relaxation content according to the child's emotional state. For example, when a child loses interest, it can add a new relaxation method. In this way, relaxation content that matches a child's emotional state can be provided to support relaxation and stress relief.

[0068] The smart speaker system can further be equipped with functions to promote parent-child communication. For example, it can provide quizzes and games that parents and children can enjoy together. It can also provide puzzles and missions that parents and children can solve together. Furthermore, to promote parent-child communication, it can provide educational content that parents and children can learn together. For example, it can provide a guide for parents and children to conduct science experiments together. It can also provide storytelling that parents and children can enjoy together. For example, it can generate stories that parents and children can tell alternately. This can promote parent-child communication and deepen the bond between them.

[0069] The processing flow of the second embodiment will be briefly explained below.

[0070] Step 1: The age setting unit sets the child's age. For example, a parent can enter the child's age through a smartphone app. Alternatively, the child can set their own age using voice input. Furthermore, the age setting unit can set multiple age categories and adjust the conversation level based on them. For example, age categories such as 0-3 years old, 4-6 years old, and 7-10 years old can be set and the corresponding conversation level provided. Step 2: The conversation generation unit generates conversations based on the age set by the age setting unit. For example, it uses generation AI (e.g., text generation AI or multimodal generation AI) to generate conversation content appropriate for the child's age. It can also adjust the difficulty of vocabulary and grammar according to the child's age. For example, it can speak to a 3-year-old child using simple words and short sentences, and to a 10-year-old child using more complex words and longer sentences. Step 3: The image projection unit projects an image related to the conversation content generated by the conversation generation unit. For example, the image selected by the generation AI can be projected onto a television or mobile phone. It can also be used on projectors and VR headsets. For example, it can connect to a projector via Wi-Fi or Bluetooth and project the image.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0075] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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).

[0080] 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.

[0081] 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.

[0082] 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.

[0083] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0084] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0099] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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."

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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]

[0138] 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. an age setting section for setting the age of the child; a conversation generation unit that generates a conversation based on the age set by the age setting unit; a video projection unit that projects a video related to the conversation content generated by the conversation generation unit. A system characterized by:

2. The age setting unit Automatically adjusts conversation level according to the child's learning progress 2. The system of claim 1.

3. The conversation generation unit Personalize the conversation with your child's name or favorite character 2. The system of claim 1.

4. The image projection unit Compatible with projectors and VR headsets 2. The system of claim 1.

5. The image projection unit Automatically selects and projects images that are likely to interest children 2. The system of claim 1.

6. The conversation generation unit Vary the tone and content of your conversations depending on your child's emotional state 2. The system of claim 1.

7. The age setting unit Customize conversations based on your child's interests 2. The system of claim 1.

8. The conversation generation unit Providing conversation content in multiple languages ​​to support bilingual education 2. The system of claim 1.

Images