System

Abstract

An object of the system according to the embodiment is to immediately respond to a child's question and to develop curiosity and power of idea.SOLUTION: A system according to an embodiment includes a recognition unit, a response generation unit, and a voice output unit. The recognition unit performs speech recognition on the child's question. The response generation unit generates a response to the question recognized by the recognition unit. The voice output unit outputs the response generated by the response generation unit by voice.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to respond immediately to children's questions and curiosity, and therefore not being able to fully develop children's curiosity and creativity.

[0005] The system according to the embodiment aims to provide immediate responses to children's questions and foster their curiosity and creativity. [Means for solving the problem]

[0006] The system according to the embodiment includes a recognition unit, a response generation unit, and a voice output unit. The recognition unit recognizes a child's question by voice. The response generation unit generates a response to the question recognized by the recognition unit. The voice output unit outputs the response generated by the response generation unit by voice. [Effects of the Invention]

[0007] The system according to the embodiment can respond immediately to children's questions and foster their curiosity and creativity. [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 question solving system according to the embodiment of the present invention is a system that recognizes children's questions through speech recognition, generates responses using a generation AI, and outputs the responses in speech. This allows the question solving system to automatically provide answers to children's questions and satisfy their curiosity.

[0029] A question-solving system according to an embodiment includes a speech recognition unit, a response generation unit, and a speech output unit. The speech recognition unit recognizes a child's question by speech. For example, the speech recognition unit collects the child's voice using a microphone and converts it into text data using speech recognition technology. The speech recognition unit can also learn characteristics of the child's voice to improve recognition accuracy. For example, the speech recognition unit can learn the child's voice pitch and speaking habits to improve recognition accuracy. The speech recognition unit can also analyze background sounds surrounding the child's speech and automatically remove environmental sounds and noise. For example, the speech recognition unit incorporates an algorithm to remove household noise and external noise. The response generation unit generates a response to the question recognized by the speech recognition unit. For example, the response generation unit uses a generation AI to generate an appropriate response to the child's question. The generation AI plays the role of an educator and provides easy-to-understand and interesting answers to children's questions. For example, in response to the question "Why is the sky blue?", the generation AI generates a response such as "The sky is blue because sunlight is scattered by small particles in the atmosphere." The audio output unit outputs the response generated by the response generation unit as audio. For example, the audio output unit uses speech synthesis technology to convert the generated response into audio data with natural pronunciation and intonation, and provides the audio response to the child. In this way, the question resolution system according to the embodiment can automatically provide an answer to a child's question and satisfy the child's curiosity. For example, if a child asks "Why is the sky blue?", the system recognizes the voice and outputs the audio response "The sky is blue because sunlight is scattered by small particles in the atmosphere."

[0030] The voice recognition unit uses generative AI to create a customized recognition model for each individual child to learn the characteristics of a child's voice, thereby improving recognition accuracy. For example, the voice recognition unit uses generative AI to create a customized voice recognition model for each individual child to learn the characteristics of a child's voice. For example, it learns the child's voice pitch and speaking habits to improve recognition accuracy. To learn the characteristics of a child's voice, the voice recognition unit also focuses on learning specific phrases and words uttered by children. For example, it reflects the words and question patterns that children often use in the model. To learn the characteristics of a child's voice, the voice recognition unit also creates a voice recognition model that takes into account the environmental and background sounds in which the child speaks. For example, a filtering function can be added to remove household noise and television sounds. This improves the accuracy of voice recognition by learning the characteristics of a child's voice.

[0031] The voice recognition unit can add a filtering function that analyzes background sounds around a child's speech and automatically removes environmental sounds and noise. For example, the voice recognition unit can add a filtering function that analyzes background sounds around a child's speech and automatically removes environmental sounds and noise. For example, an algorithm can be introduced to remove household noise and external noise. The voice recognition unit can also add a filtering function that analyzes background sounds around a child's speech and removes noise in specific frequency bands. For example, the sound of an air conditioner or a car running can be removed. The voice recognition unit can also add a filtering function that analyzes background sounds around a child's speech and removes noise in real time. For example, the filtering function can detect noise the moment a child speaks and remove it immediately. This improves the accuracy of voice recognition by removing background sounds and noise.

[0032] The speech recognition unit uses speech recognition technology to simultaneously recognize not only a child's speech but also their gestures and facial expressions, enabling more multifaceted question resolution. For example, the speech recognition unit uses speech recognition technology to build a system that simultaneously recognizes a child's speech, gestures, and facial expressions. For example, it recognizes a child's gesture of raising their hand or a smile, which helps resolve questions. The speech recognition unit also combines speech recognition technology with image recognition technology to simultaneously analyze a child's speech and gestures. For example, it recognizes the direction a child is pointing and the direction of their face to identify the content of their question. The speech recognition unit also uses speech recognition technology to simultaneously analyze a child's speech and facial expressions, developing a system that understands their emotions and intentions. For example, if a child has a troubled expression, it determines that they are asking for help. This allows for more multifaceted question resolution by recognizing not only a child's speech, but also their gestures and facial expressions.

[0033] The speech recognition unit can use generative AI to develop a multilingual recognition model so that it can accommodate children who speak different languages. For example, the speech recognition unit uses generative AI to develop a multilingual speech recognition model so that it can accommodate children who speak different languages. For example, it can accommodate multiple languages ​​such as English, Japanese, and Spanish. The speech recognition unit also develops a multilingual speech recognition model and builds a system that automatically detects the language spoken by a child. For example, it switches the recognition model depending on the language spoken by the child. The speech recognition unit also trains the speech recognition model in multiple languages ​​so that it can accommodate children who speak different languages. For example, it learns the pronunciation and accent of each language and improves recognition accuracy. This makes it possible to accommodate children who speak different languages.

[0034] The response generation unit can train the generation AI with the educator's expertise to generate customized responses based on the child's age and level of understanding. For example, the response generation unit trains the generation AI with the educator's expertise to generate customized responses based on the child's age and level of understanding. For example, it can provide explanations in simple terms for young children and detailed explanations for elementary school students. The response generation unit also trains the generation AI with the educator's expertise to generate responses based on the child's interests and concerns. For example, it can provide scientific explanations for children who are interested in science. The response generation unit also trains the generation AI with the educator's expertise to generate responses based on the child's learning style. For example, it can provide explanations using diagrams and illustrations for children who prefer visual explanations. This allows the generation of customized responses based on the child's age and level of understanding, deepening the child's understanding.

[0035] The response generation unit can add a history management function that allows the generation AI to refer to a child's past question history and provide consistent responses. The response generation unit, for example, adds a history management function that allows the generation AI to refer to a child's past question history and provide consistent responses. For example, it records the content of questions the child has previously asked and provides related responses. The response generation unit also analyzes a child's past question history and provides responses that match the child's interests and concerns. For example, it provides detailed explanations for topics that children repeatedly ask about. The response generation unit also builds a system that allows the generation AI to refer to a child's past question history and provide consistent responses. For example, it provides related information based on the content the child has previously asked. This makes it possible to provide consistent responses by referring to past question history.

[0036] The response generation unit can use generative AI to not only provide answers to children's questions but also suggest related learning content and activities. For example, the response generation unit uses generative AI to build a system that not only provides answers to children's questions but also suggests related learning content and activities. For example, if a child becomes interested in science, the response generation unit suggests a science experiment activity. The response generation unit also uses generative AI to generate answers to children's questions and suggests learning content related to the answer. For example, if a child asks, "Why is the sky blue?", the response generation unit suggests videos and articles about sky colors. The response generation unit also uses generative AI to develop a system that not only provides answers to children's questions but also suggests related activities. For example, if a child asks, "Why do plants grow?", the response generation unit suggests an activity to observe plant growth. This not only provides answers to children's questions but also suggests related learning content and activities, thereby increasing children's motivation to learn.

[0037] The response generation unit uses a generation AI to generate responses from multiple perspectives to children's questions, promoting multifaceted thinking in children. The response generation unit builds a system in which the generation AI generates responses from multiple perspectives to children's questions. For example, to the question, "Why is the sky blue?", it provides responses from both a scientific and a cultural perspective. The response generation unit also generates responses from different specialized perspectives to children's questions. For example, to the question, "Why do plants grow?", it provides responses from both a biological and an agricultural perspective. The response generation unit also develops a system in which the generation AI generates responses to children's questions from historical and future perspectives. For example, to the question, "Why do humans go into space?", it provides responses about the history of past space exploration and future space programs. This allows responses to children's questions from multiple perspectives, promoting multifaceted thinking in children.

[0038] The voice output unit can add a customization function using voice synthesis technology that allows a child to select a voice quality and speaking style that is familiar to them. The voice output unit, for example, uses voice synthesis technology to add a customization function that allows a child to select a voice quality and speaking style that is familiar to them. For example, a response is provided in the voice of a character that the child likes. The voice output unit also uses voice synthesis technology to build a system that allows a child to select a voice quality and speaking style that is familiar to them. For example, a response is provided in the voice of a voice actor selected by the child. The voice output unit also uses voice synthesis technology to add a customization function that allows a child to select a voice quality and speaking style that is familiar to them. For example, a response is provided in the tone and speed of the voice selected by the child. This allows a child to select a voice quality and speaking style that is familiar to them, thereby maintaining their interest.

[0039] The voice output unit can be enhanced with a function that allows the generation AI to analyze a child's reactions in real time and adjust the tone and speed of the response. For example, if a child is excited, the generation AI can respond in a calm tone. The voice output unit can also be enhanced with a function that allows the generation AI to analyze a child's reactions in real time and adjust the tone and speed of the response. For example, if a child is confused, the generation AI can respond at a slower pace. The voice output unit can also be enhanced with a function that allows the generation AI to analyze a child's reactions in real time and adjust the tone and speed of the response. For example, if a child is interested, the generation AI can respond in a brighter tone. This allows the generation AI to adjust the tone and speed of the response according to the child's reaction, thereby deepening the child's understanding.

[0040] The audio output unit can provide not only audio output but also visual aids at the same time, deepening children's understanding. For example, a system can be constructed in which the audio output unit simultaneously provides not only audio output but also visual aids (e.g., animations and illustrations). For example, if a child asks, "Why is the sky blue?", an animation showing the changing color of the sky is provided. The audio output unit can also combine audio output and visual aids to develop a system that deepens children's understanding. For example, if a child asks, "Why do plants grow?", an illustration showing the plant growth process is provided. The audio output unit can also simultaneously provide audio output and visual aids to build a system that deepens children's understanding. For example, if a child asks, "Why do humans go into space?", an animation showing the history of space exploration is provided. In this way, children's understanding can be deepened by providing visual aids at the same time.

[0041] The audio output unit can add an interactive function where the generation AI analyzes a child's response and adjusts the content of the response in real time. The audio output unit can add an interactive function where the generation AI analyzes a child's response and adjusts the content of the response in real time. For example, if a child shows interest, a detailed explanation can be provided. The audio output unit can also build a system where the generation AI analyzes a child's response in real time and adjusts the content of the response. For example, if a child is in trouble, a simple explanation can be provided. The audio output unit can also add an interactive function where the generation AI analyzes a child's response and adjusts the content of the response in real time. For example, if a child shows interest, additional related information can be provided. This allows the content of the response to be adjusted in real time according to the child's response, thereby deepening the child's understanding.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The question-solving system can also provide relevant learning content to children's questions. For example, if a child asks, "Why is the sky blue?", in addition to providing an answer, it can suggest scientific videos and articles about the color of the sky. If a child asks, "Why do plants grow?", it can suggest animations and experiment kits that show the plant growth process. Furthermore, if a child asks, "Why do humans go into space?", it could suggest a documentary about the history of space exploration and future plans. This not only provides answers to children's questions but also provides relevant learning content, thereby increasing children's motivation to learn.

[0044] The question-solving system can also provide answers to children's questions from multiple perspectives. For example, to the question, "Why is the sky blue?", answers can be provided from both a scientific and a cultural perspective. To the question, "Why do plants grow?", answers can be provided from both a biological and an agricultural perspective. Furthermore, to the question, "Why do humans go into space?", answers can be provided about the history of past space exploration and future space programs. By providing answers to children's questions from multiple perspectives, this system can promote multifaceted thinking in children.

[0045] The question-solving system can also provide customized responses to children's questions based on the child's age and level of understanding. For example, it can provide simple explanations for young children and detailed explanations for elementary school students. It can also generate responses based on a child's interests. For example, it can provide scientific explanations for children who are interested in science, and historical background for children who are interested in history. It can also provide responses based on a child's learning style. For example, it can provide explanations using diagrams and illustrations for children who prefer visual explanations. This allows children to deepen their understanding by providing customized responses based on their age and level of understanding.

[0046] The question-solving system can also refer to a child's past question history to provide consistent responses to children's questions. For example, it can record the questions a child has previously asked and provide relevant responses. It can also analyze a child's past question history to provide responses that are tailored to the child's interests and concerns. For example, it can provide detailed explanations for topics that children repeatedly ask about. It is also possible to build a system that refers to a child's past question history to provide consistent responses. For example, it can provide related information based on the questions a child has previously asked. This makes it possible to provide consistent responses by referring to the past question history.

[0047] The question-solving system can also suggest related activities in response to children's questions. For example, if a child is interested in science, it can suggest a science experiment activity. If a child asks, "Why is the sky blue?", it can suggest an experiment related to the color of the sky. If a child asks, "Why do plants grow?", it can suggest an activity to observe plant growth. This not only answers children's questions but also suggests related activities, which can increase children's motivation to learn.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The recognition unit recognizes the child's question through speech recognition. For example, the recognition unit collects the child's voice through a microphone and converts it into text data using speech recognition technology. The recognition unit can also learn the characteristics of the child's voice to improve recognition accuracy. Furthermore, the recognition unit can analyze the background sounds of the child's speech and automatically remove environmental sounds and noise. Step 2: The response generator generates a response to the question recognized by the recognizer. For example, the response generator uses a generation AI to generate an appropriate response to a child's question. The generation AI plays the role of an educator, providing easy-to-understand and interesting answers to children's questions. Step 3: The audio output unit outputs the response generated by the response generation unit by voice. For example, the audio output unit uses voice synthesis technology to convert the generated response into audio data with natural pronunciation and intonation, and provides the response by voice to the child.

[0050] (Example 2) The question solving system according to the embodiment of the present invention is a system that recognizes children's questions through speech recognition, generates responses using a generation AI, and outputs the responses in speech. This allows the question solving system to automatically provide answers to children's questions and satisfy their curiosity.

[0051] A question-solving system according to an embodiment includes a speech recognition unit, a response generation unit, and a speech output unit. The speech recognition unit recognizes a child's question by speech. For example, the speech recognition unit collects the child's voice using a microphone and converts it into text data using speech recognition technology. The speech recognition unit can also learn characteristics of the child's voice to improve recognition accuracy. For example, the speech recognition unit can learn the child's voice pitch and speaking habits to improve recognition accuracy. The speech recognition unit can also analyze background sounds surrounding the child's speech and automatically remove environmental sounds and noise. For example, the speech recognition unit incorporates an algorithm to remove household noise and external noise. The response generation unit generates a response to the question recognized by the speech recognition unit. For example, the response generation unit uses a generation AI to generate an appropriate response to the child's question. The generation AI plays the role of an educator and provides easy-to-understand and interesting answers to children's questions. For example, in response to the question "Why is the sky blue?", the generation AI generates a response such as "The sky is blue because sunlight is scattered by small particles in the atmosphere." The audio output unit outputs the response generated by the response generation unit as audio. For example, the audio output unit uses speech synthesis technology to convert the generated response into audio data with natural pronunciation and intonation, and provides the audio response to the child. In this way, the question resolution system according to the embodiment can automatically provide an answer to a child's question and satisfy the child's curiosity. For example, if a child asks "Why is the sky blue?", the system recognizes the voice and outputs the audio response "The sky is blue because sunlight is scattered by small particles in the atmosphere."

[0052] The voice recognition unit uses generative AI to create a customized recognition model for each individual child to learn the characteristics of a child's voice, thereby improving recognition accuracy. For example, the voice recognition unit uses generative AI to create a customized voice recognition model for each individual child to learn the characteristics of a child's voice. For example, it learns the child's voice pitch and speaking habits to improve recognition accuracy. To learn the characteristics of a child's voice, the voice recognition unit also focuses on learning specific phrases and words uttered by children. For example, it reflects the words and question patterns that children often use in the model. To learn the characteristics of a child's voice, the voice recognition unit also creates a voice recognition model that takes into account the environmental and background sounds in which the child speaks. For example, a filtering function can be added to remove household noise and television sounds. This improves the accuracy of voice recognition by learning the characteristics of a child's voice.

[0053] The voice recognition unit can add a filtering function that analyzes background sounds around a child's speech and automatically removes environmental sounds and noise. For example, the voice recognition unit can add a filtering function that analyzes background sounds around a child's speech and automatically removes environmental sounds and noise. For example, an algorithm can be introduced to remove household noise and external noise. The voice recognition unit can also add a filtering function that analyzes background sounds around a child's speech and removes noise in specific frequency bands. For example, the sound of an air conditioner or a car running can be removed. The voice recognition unit can also add a filtering function that analyzes background sounds around a child's speech and removes noise in real time. For example, the filtering function can detect noise the moment a child speaks and remove it immediately. This improves the accuracy of voice recognition by removing background sounds and noise.

[0054] The voice recognition unit can use the emotion estimation function to estimate the emotion a child expresses when speaking and generate an appropriate response according to the emotion. The voice recognition unit, for example, uses the emotion estimation function to estimate the emotion a child expresses when speaking and generate an appropriate response according to the emotion. For example, if the child is excited, the voice recognition unit responds in a calm tone. The voice recognition unit also uses the emotion estimation function to analyze the emotion a child expresses when speaking in real time and generate a response according to the emotion. For example, if the child is sad, the voice recognition unit provides words of encouragement. The voice recognition unit also uses the emotion estimation function to build a system that estimates the emotion a child expresses when speaking and generates a response according to the emotion. For example, if the child has a question, the voice recognition unit provides a detailed explanation. In this way, by generating an appropriate response according to the child's emotion, the child's satisfaction is improved.

[0055] The speech recognition unit uses speech recognition technology to simultaneously recognize not only a child's speech but also their gestures and facial expressions, enabling more multifaceted question resolution. For example, the speech recognition unit uses speech recognition technology to build a system that simultaneously recognizes a child's speech, gestures, and facial expressions. For example, it recognizes a child's gesture of raising their hand or a smile, which helps resolve questions. The speech recognition unit also combines speech recognition technology with image recognition technology to simultaneously analyze a child's speech and gestures. For example, it recognizes the direction a child is pointing and the direction of their face to identify the content of their question. The speech recognition unit also uses speech recognition technology to simultaneously analyze a child's speech and facial expressions, developing a system that understands their emotions and intentions. For example, if a child has a troubled expression, it determines that they are asking for help. This allows for more multifaceted question resolution by recognizing not only a child's speech, but also their gestures and facial expressions.

[0056] The speech recognition unit can use generative AI to develop a multilingual recognition model so that it can accommodate children who speak different languages. For example, the speech recognition unit uses generative AI to develop a multilingual speech recognition model so that it can accommodate children who speak different languages. For example, it can accommodate multiple languages ​​such as English, Japanese, and Spanish. The speech recognition unit also develops a multilingual speech recognition model and builds a system that automatically detects the language spoken by a child. For example, it switches the recognition model depending on the language spoken by the child. The speech recognition unit also trains the speech recognition model in multiple languages ​​so that it can accommodate children who speak different languages. For example, it learns the pronunciation and accent of each language and improves recognition accuracy. This makes it possible to accommodate children who speak different languages.

[0057] The voice recognition unit can use the emotion estimation function to monitor the emotion a child expresses when speaking in real time and provide feedback according to the emotion. The voice recognition unit, for example, uses the emotion estimation function to build a system that monitors the emotion a child expresses when speaking in real time and provides feedback according to the emotion. For example, if the child is excited, the system responds in a calm tone. The voice recognition unit also uses the emotion estimation function to analyze the emotion a child expresses when speaking in real time and provides feedback according to the emotion. For example, if the child is sad, the system provides words of encouragement. The voice recognition unit also uses the emotion estimation function to develop a system that monitors the emotion a child expresses when speaking and provides feedback according to the emotion. For example, if the child has a question, the system provides a detailed explanation. In this way, by providing feedback according to the child's emotion, the child's satisfaction is improved.

[0058] The response generation unit can train the generation AI with the educator's expertise to generate customized responses based on the child's age and level of understanding. For example, the response generation unit trains the generation AI with the educator's expertise to generate customized responses based on the child's age and level of understanding. For example, it can provide explanations in simple terms for young children and detailed explanations for elementary school students. The response generation unit also trains the generation AI with the educator's expertise to generate responses based on the child's interests and concerns. For example, it can provide scientific explanations for children who are interested in science. The response generation unit also trains the generation AI with the educator's expertise to generate responses based on the child's learning style. For example, it can provide explanations using diagrams and illustrations for children who prefer visual explanations. This allows the generation of customized responses based on the child's age and level of understanding, deepening the child's understanding.

[0059] The response generation unit can add a history management function that allows the generation AI to refer to a child's past question history and provide consistent responses. The response generation unit, for example, adds a history management function that allows the generation AI to refer to a child's past question history and provide consistent responses. For example, it records the content of questions the child has previously asked and provides related responses. The response generation unit also analyzes a child's past question history and provides responses that match the child's interests and concerns. For example, it provides detailed explanations for topics that children repeatedly ask about. The response generation unit also builds a system that allows the generation AI to refer to a child's past question history and provide consistent responses. For example, it provides related information based on the content the child has previously asked. This makes it possible to provide consistent responses by referring to past question history.

[0060] The response generation unit can use generative AI to not only provide answers to children's questions but also suggest related learning content and activities. For example, the response generation unit uses generative AI to build a system that not only provides answers to children's questions but also suggests related learning content and activities. For example, if a child becomes interested in science, the response generation unit suggests a science experiment activity. The response generation unit also uses generative AI to generate answers to children's questions and suggests learning content related to the answer. For example, if a child asks, "Why is the sky blue?", the response generation unit suggests videos and articles about sky colors. The response generation unit also uses generative AI to develop a system that not only provides answers to children's questions but also suggests related activities. For example, if a child asks, "Why do plants grow?", the response generation unit suggests an activity to observe plant growth. This not only provides answers to children's questions but also suggests related learning content and activities, thereby increasing children's motivation to learn.

[0061] The response generation unit uses a generation AI to generate responses from multiple perspectives to children's questions, promoting multifaceted thinking in children. The response generation unit builds a system in which the generation AI generates responses from multiple perspectives to children's questions. For example, to the question, "Why is the sky blue?", it provides responses from both a scientific and a cultural perspective. The response generation unit also generates responses from different specialized perspectives to children's questions. For example, to the question, "Why do plants grow?", it provides responses from both a biological and an agricultural perspective. The response generation unit also develops a system in which the generation AI generates responses to children's questions from historical and future perspectives. For example, to the question, "Why do humans go into space?", it provides responses about the history of past space exploration and future space programs. This allows responses to children's questions from multiple perspectives, promoting multifaceted thinking in children.

[0062] The response generation unit can use the emotion estimation function to generate responses that increase a child's motivation to learn based on their emotions. The response generation unit, for example, uses the emotion estimation function to build a system that generates responses that increase a child's motivation to learn based on their emotions. For example, if a child is excited, it provides an interesting response. The response generation unit also uses the emotion estimation function to analyze a child's emotions in real time and generate responses that increase a child's motivation to learn based on their emotions. For example, if a child is sad, it provides words of encouragement. The response generation unit also uses the emotion estimation function to develop a system that generates responses that increase a child's motivation to learn based on their emotions. For example, if a child has a question, it provides a detailed explanation. In this way, by generating responses that increase a child's motivation to learn based on their emotions, it is possible to improve a child's motivation to learn.

[0063] The voice output unit can add a customization function using voice synthesis technology that allows a child to select a voice quality and speaking style that is familiar to them. The voice output unit, for example, uses voice synthesis technology to add a customization function that allows a child to select a voice quality and speaking style that is familiar to them. For example, a response is provided in the voice of a character that the child likes. The voice output unit also uses voice synthesis technology to build a system that allows a child to select a voice quality and speaking style that is familiar to them. For example, a response is provided in the voice of a voice actor selected by the child. The voice output unit also uses voice synthesis technology to add a customization function that allows a child to select a voice quality and speaking style that is familiar to them. For example, a response is provided in the tone and speed of the voice selected by the child. This allows a child to select a voice quality and speaking style that is familiar to them, thereby maintaining their interest.

[0064] The voice output unit can be enhanced with a function that allows the generation AI to analyze a child's reactions in real time and adjust the tone and speed of the response. For example, if a child is excited, the generation AI can respond in a calm tone. The voice output unit can also be enhanced with a function that allows the generation AI to analyze a child's reactions in real time and adjust the tone and speed of the response. For example, if a child is confused, the generation AI can respond at a slower pace. The voice output unit can also be enhanced with a function that allows the generation AI to analyze a child's reactions in real time and adjust the tone and speed of the response. For example, if a child is interested, the generation AI can respond in a brighter tone. This allows the generation AI to adjust the tone and speed of the response according to the child's reaction, thereby deepening the child's understanding.

[0065] The voice output unit can use the emotion estimation function to generate a response with a tone and intonation that corresponds to the child's emotion. For example, the voice output unit uses the emotion estimation function to build a system that generates a response with a tone and intonation that corresponds to the child's emotion. For example, if the child is excited, the voice output unit responds in an interesting tone. The voice output unit also uses the emotion estimation function to analyze the child's emotion in real time and generate a response with a tone and intonation that corresponds to the emotion. For example, if the child is sad, the voice output unit responds in a gentle tone. The voice output unit also uses the emotion estimation function to develop a system that generates a response with a tone and intonation that corresponds to the child's emotion. For example, if the child has a question, the voice output unit responds in a tone that provides a detailed explanation. In this way, generating a response with a tone and intonation that corresponds to the child's emotion can deepen the child's understanding.

[0066] The audio output unit can provide not only audio output but also visual aids at the same time, deepening children's understanding. For example, a system can be constructed in which the audio output unit simultaneously provides not only audio output but also visual aids (e.g., animations and illustrations). For example, if a child asks, "Why is the sky blue?", an animation showing the changing color of the sky is provided. The audio output unit can also combine audio output and visual aids to develop a system that deepens children's understanding. For example, if a child asks, "Why do plants grow?", an illustration showing the plant growth process is provided. The audio output unit can also simultaneously provide audio output and visual aids to build a system that deepens children's understanding. For example, if a child asks, "Why do humans go into space?", an animation showing the history of space exploration is provided. In this way, children's understanding can be deepened by providing visual aids at the same time.

[0067] The audio output unit can add an interactive function where the generation AI analyzes a child's response and adjusts the content of the response in real time. The audio output unit can add an interactive function where the generation AI analyzes a child's response and adjusts the content of the response in real time. For example, if a child shows interest, a detailed explanation can be provided. The audio output unit can also build a system where the generation AI analyzes a child's response in real time and adjusts the content of the response. For example, if a child is in trouble, a simple explanation can be provided. The audio output unit can also add an interactive function where the generation AI analyzes a child's response and adjusts the content of the response in real time. For example, if a child shows interest, additional related information can be provided. This allows the content of the response to be adjusted in real time according to the child's response, thereby deepening the child's understanding.

[0068] The audio output unit uses the emotion estimation function to provide audio feedback according to the child's emotions, thereby maintaining the child's interest. The audio output unit, for example, uses the emotion estimation function to build a system that provides audio feedback according to the child's emotions. For example, if the child is excited, it provides interesting feedback. The audio output unit also uses the emotion estimation function to analyze the child's emotions in real time and provide audio feedback according to the emotions. For example, if the child is sad, it provides words of encouragement. The audio output unit also uses the emotion estimation function to develop a system that provides audio feedback according to the child's emotions. For example, if the child has a question, it provides a detailed explanation. In this way, by providing audio feedback according to the child's emotions, it is possible to maintain the child's interest.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The question-solving system can also provide relevant learning content to children's questions. For example, if a child asks, "Why is the sky blue?", in addition to providing an answer, it can suggest scientific videos and articles about the color of the sky. If a child asks, "Why do plants grow?", it can suggest animations and experiment kits that show the plant growth process. Furthermore, if a child asks, "Why do humans go into space?", it could suggest a documentary about the history of space exploration and future plans. This not only provides answers to children's questions but also provides relevant learning content, thereby increasing children's motivation to learn.

[0071] The question-solving system can also provide answers to children's questions from multiple perspectives. For example, to the question, "Why is the sky blue?", answers can be provided from both a scientific and a cultural perspective. To the question, "Why do plants grow?", answers can be provided from both a biological and an agricultural perspective. Furthermore, to the question, "Why do humans go into space?", answers can be provided about the history of past space exploration and future space programs. By providing answers to children's questions from multiple perspectives, this system can promote multifaceted thinking in children.

[0072] The question-solving system can also provide customized responses to children's questions based on the child's age and level of understanding. For example, it can provide simple explanations for young children and detailed explanations for elementary school students. It can also generate responses based on a child's interests. For example, it can provide scientific explanations for children who are interested in science, and historical background for children who are interested in history. It can also provide responses based on a child's learning style. For example, it can provide explanations using diagrams and illustrations for children who prefer visual explanations. This allows children to deepen their understanding by providing customized responses based on their age and level of understanding.

[0073] The question-solving system can also refer to a child's past question history to provide consistent responses to children's questions. For example, it can record the questions a child has previously asked and provide relevant responses. It can also analyze a child's past question history to provide responses that are tailored to the child's interests and concerns. For example, it can provide detailed explanations for topics that children repeatedly ask about. It is also possible to build a system that refers to a child's past question history to provide consistent responses. For example, it can provide related information based on the questions a child has previously asked. This makes it possible to provide consistent responses by referring to the past question history.

[0074] The question-solving system can also suggest related activities in response to children's questions. For example, if a child is interested in science, it can suggest a science experiment activity. If a child asks, "Why is the sky blue?", it can suggest an experiment related to the color of the sky. If a child asks, "Why do plants grow?", it can suggest an activity to observe plant growth. This not only answers children's questions but also suggests related activities, which can increase children's motivation to learn.

[0075] The question-solving system can also estimate the emotion a child is feeling when speaking and generate an appropriate response based on that emotion. For example, if a child is excited, the system can respond in a calm tone. If a child is sad, the system can offer words of encouragement. Furthermore, if a child has a question, the system can provide a detailed explanation. This increases the child's satisfaction by generating an appropriate response based on the child's emotion.

[0076] The question-solving system can also monitor the emotions of children when they speak in real time and provide feedback according to their emotions. For example, if a child is excited, the system can respond in a calm tone. If a child is sad, the system can provide words of encouragement. Furthermore, if a child has a question, the system can provide a detailed explanation. This provides feedback according to the child's emotions, thereby improving the child's satisfaction.

[0077] The question-solving system can also generate responses that increase a child's motivation to learn according to their emotions. For example, if a child is excited, it can provide an interesting response. If a child is sad, it can provide words of encouragement. Furthermore, if a child has a question, it can provide a detailed explanation. In this way, by generating responses that increase a child's motivation to learn according to their emotions, it is possible to improve a child's motivation to learn.

[0078] The question-solving system can also generate responses in a tone and intonation that corresponds to the child's emotions. For example, if the child is excited, the system can respond in an intriguing tone. If the child is sad, the system can respond in a gentle tone. Furthermore, if the child has a question, the system can respond in a tone that provides a detailed explanation. This allows the system to generate responses in a tone and intonation that corresponds to the child's emotions, thereby deepening the child's understanding.

[0079] The question-solving system can also provide voice feedback according to the child's emotions. For example, if the child is excited, it can provide interesting feedback. If the child is sad, it can provide words of encouragement. Furthermore, if the child has a question, it can provide a detailed explanation. In this way, by providing voice feedback according to the child's emotions, it is possible to keep the child interested.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The recognition unit recognizes the child's question through speech recognition. For example, the recognition unit collects the child's voice through a microphone and converts it into text data using speech recognition technology. The recognition unit can also learn the characteristics of the child's voice to improve recognition accuracy. Furthermore, the recognition unit can analyze the background sounds of the child's speech and automatically remove environmental sounds and noise. Step 2: The response generator generates a response to the question recognized by the recognizer. For example, the response generator uses a generation AI to generate an appropriate response to a child's question. The generation AI plays the role of an educator, providing easy-to-understand and interesting answers to children's questions. Step 3: The audio output unit outputs the response generated by the response generation unit by voice. For example, the audio output unit uses voice synthesis technology to convert the generated response into audio data with natural pronunciation and intonation, and provides the response by voice to the child.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0126] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0130] The data processing system 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 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 voice recognition unit that recognizes children's questions, a response generation unit that generates a response to the question recognized by the speech recognition unit; an audio output unit that outputs the response generated by the response generation unit by voice; A system characterized by:

2. The voice recognition unit Add a filter function that analyzes the background sounds of the child's speech and automatically removes environmental sounds and noise.

2. The system of claim 1.

3. The voice recognition unit Using voice recognition technology, the system can simultaneously recognize not only the child's speech but also their gestures and facial expressions, providing a more comprehensive solution to their questions.

2. The system of claim 1.

4. The response generation unit The generative AI learns the educator's expertise and generates customized responses based on the child's age and level of understanding.

2. The system of claim 1.

5. The audio output unit Add a customization function that uses voice synthesis technology to allow the child to select a voice quality and speaking style that is familiar to them.

2. The system of claim 1.

6. The voice recognition unit Estimate the emotion of the child when he / she speaks and generate an appropriate response according to the emotion.

2. The system of claim 1.

7. The response generation unit Generate responses according to the child's emotions to keep the child engaged 2. The system of claim 1.

8. The audio output unit Providing audio feedback according to the child's emotions to keep the child engaged 2. The system of claim 1.

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