System

Abstract

An object of a system according to an embodiment is to enable an adult to easily understand words spoken by a child and to smoothly communicate with the child.SOLUTION: A system includes a word detection unit, a conversion unit, and an answer generation unit. The word detector detects a word spoken by the child. The conversion unit converts the speech of the child detected by the speech detection unit so that the adult can understand the speech. The answer generation unit generates an answer to the question asked by the child and provides the answer in words that can be understood by the child.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] With conventional technology, it was difficult for adults to understand what children were saying, making communication difficult.

[0005] The system according to the embodiment aims to make it easier for adults to understand what children say and to facilitate smooth communication with children. [Means for solving the problem]

[0006] The system according to the embodiment includes a word detection unit, a conversion unit, and an answer generation unit. The word detection unit detects words spoken by a child. The conversion unit converts the words detected by the word detection unit into words that adults can understand. The answer generation unit generates answers to the child's questions and provides them in words that the child can understand. [Effects of the Invention]

[0007] The system according to the embodiment makes it easier for adults to understand what children say, facilitating smooth communication with children. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The communication support system according to an embodiment of the present invention detects the words spoken by a child, converts them into words that adults can understand, and provides answers to questions the child has in words the child can understand. This communication support system can eliminate frustration when parents cannot understand what the child is saying or when the child repeatedly asks questions such as "why?" and "how," thereby facilitating smooth communication between parents and children.

[0029] The communication support system according to the embodiment includes a word detection unit, a conversion unit, and an answer generation unit. The word detection unit detects the words a child is speaking. For example, it detects the child's words using voice recognition technology. The word detection unit can also learn the characteristics of a child's pronunciation to reduce misrecognition due to differences in pronunciation. For example, if a child pronounces "ringo" (apple) as "ingo," it can learn the characteristics and accurately recognize the pronunciation. The conversion unit converts the child's words detected by the word detection unit into words that an adult can understand. For example, if a child says "I'm hungry," the generation AI can convey to the adult that the words mean "I'm hungry." The conversion unit can also be expanded to support different languages ​​and dialects, allowing it to be used as a multilingual communication tool. For example, it supports multiple languages, including Japanese, English, and Spanish. The answer generation unit generates answers to the child's questions and provides them in language the child can understand. For example, if a child asks, "Why is the sky blue?", the generation AI analyzes the question and provides an answer such as, "The sky is blue because sunlight hits tiny particles in the air, scattering the blue color." The answer generation unit can also refer to past question history and provide related information. For example, if a child who previously asked, "Why is the sky blue?" now asks, "Why is the ocean blue?", the answer generation unit provides related information. This allows the communication support system according to the embodiment to eliminate frustration when parents cannot understand what their children are saying or when they are repeatedly asked, "Why?" and "How come?", thereby facilitating smooth communication between parents and children.

[0030] The word detection unit can learn the characteristics of children's pronunciation and reduce misrecognition due to differences in pronunciation. The word detection unit, for example, learns the characteristics of children's pronunciation and recognizes specific sound patterns. For example, if a child pronounces "ringo" (apple) as "ingo," it learns those characteristics and accurately recognizes them. It also takes differences in children's pronunciation into account to reduce misrecognition. For example, if a child pronounces "kakana" (fish) as "takana," it learns the difference and accurately recognizes them. It also registers children's pronunciation characteristics in a database and recognizes individual pronunciation patterns. For example, if a particular child pronounces "kuruma" (car) as "uruma," it learns those characteristics and accurately recognizes them. This reduces misrecognition due to differences in children's pronunciation, enabling accurate word detection.

[0031] The speech detection unit can filter background sounds and environmental sounds to minimize the influence of noise. The speech detection unit accurately detects children's speech using, for example, technology for filtering background sounds and environmental sounds. For example, television sounds and car sounds are removed to recognize children's voices. In addition, noise canceling technology is used to minimize the influence of environmental sounds. For example, wind sounds and rain sounds are removed to recognize children's voices. In addition, voice analysis technology is used to filter background sounds to accurately detect children's speech. For example, other people's voices and music are removed to recognize children's voices. In this way, children's speech can be accurately detected by minimizing the influence of background sounds and environmental sounds.

[0032] The conversion unit can be expanded to support different languages ​​and dialects, allowing it to be used as a multilingual communication tool. For example, the conversion unit can expand the child's speech detection and conversion functions to support multiple languages, making it compatible with children who speak different languages. For example, it can support multiple languages ​​such as Japanese, English, and Spanish. It can also be expanded to support dialects, recognizing regional differences in language. For example, it can accurately recognize dialects such as Kansai dialect and Tohoku dialect. It can also serve as a multilingual communication tool to support communication between parents and children who speak different languages. For example, it can enable smooth communication between parents who speak a foreign language and children who speak Japanese. This makes multilingual communication possible by supporting different languages ​​and dialects.

[0033] The conversion unit can be applied as a tool to support communication between teachers and students in educational settings. The conversion unit is applied, for example, as a tool to support communication between teachers and students in educational settings. For example, it is used to enable teachers to accurately understand what students are saying and provide appropriate instruction. It can also learn students' pronunciation and linguistic characteristics and be used as a support tool to enable teachers to accurately understand what students are saying. For example, it can reduce misrecognition due to differences in pronunciation. It can also utilize the detection and conversion functions of children's words to facilitate communication in educational settings. For example, it can be used in special needs education to enable teachers to accurately understand what students are saying. This facilitates communication between teachers and students in educational settings.

[0034] The answer generation unit can refer to past question history and provide related information. The answer generation unit, for example, refers to a child's past question history and provides related information. For example, if a child who previously asked "Why is the sky blue?" now asks "Why is the ocean blue?", related information is provided. Furthermore, an answer that takes the child's level of understanding into consideration is provided based on the question history. For example, if a child who previously asked "Why does it rain?" now asks "Why does it snow?", related information is provided. Furthermore, by referring to the past question history, an answer based on the child's interests and concerns is provided. For example, if a child who previously asked "Why can birds fly?" now asks "Why can airplanes fly?", related information is provided. In this way, by referring to the past question history, related information can be provided and the child's understanding can be deepened.

[0035] The answer generation unit can provide answers in a form that is easy for children to understand by using visual aids. The answer generation unit provides answers to questions from children using visual aids, for example. For example, the question "Why is the sky blue?" is explained using an animation of the sky changing color. Also, by using visual aids, answers are provided in a form that is easy for children to understand. For example, the question "Why does it rain?" is explained using an animation of rain falling from clouds. Also, answers to children's questions are provided using visual aids to promote children's understanding. For example, the question "Why do plants grow?" is explained using an illustration showing the process of plant growth. In this way, by using visual aids, answers can be provided in a form that is easy for children to understand.

[0036] The answer generation unit can be linked with other devices in the home, enabling use on multiple devices. For example, the answer generation unit links the answer function to children's questions with a smart speaker in the home to provide answers by voice. For example, when a child asks a question to a smart speaker, the generation AI provides an answer. The answer function to children's questions can also be linked with a tablet in the home to provide a visual answer. For example, when a child asks a question to a tablet, the generation AI provides an answer using illustrations or animations. The answer function to children's questions can also be linked with a television in the home to provide a visual answer on a large screen. For example, when a child asks a question to the television, the generation AI provides an answer using video. This allows use on multiple devices by linking with other devices in the home.

[0037] The answer generation unit can be integrated into educational apps or online learning platforms and used as a learning support tool. For example, the answer generation unit can be integrated into an educational app with a function for answering children's questions and used as a learning support tool. For example, when a child asks a question within the app, the generation AI provides an answer. Also, the answer generation unit can be integrated into an online learning platform with a function for answering children's questions and used as a learning support tool. For example, when a child asks a question within the platform, the generation AI provides an answer. Also, by integrating the answer generation unit into educational apps or online learning platforms, learning can be supported by providing answers to children's questions. For example, when a child has a question while studying, the generation AI provides an answer immediately. As a result, by integrating the answer generation unit into educational apps or online learning platforms, it can be used as a learning support tool.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The communication support system may further include an interest estimation unit that estimates a child's interests and provides related information based on those interests. For example, if a child asks about "dinosaurs," the interest estimation unit may estimate, "Maybe he wants to know more about the types of dinosaurs and their ecology," and provide related information. If a child asks about "space," the interest estimation unit may estimate, "Maybe he wants to know more about the stars and planets in space," and provide related information. If a child asks about "animals," the interest estimation unit may estimate, "Maybe he wants to know more about the ecology and habits of animals," and provide related information. In this way, providing information based on a child's interests and concerns can increase a child's motivation to learn.

[0040] The communication support system may further include a pronunciation learning unit that learns the characteristics of a child's pronunciation and reduces misrecognition due to differences in pronunciation. For example, if a child pronounces "ringo" (apple) as "ingo," the system will learn the characteristics and accurately recognize the word. Also, if a child pronounces "kakana" (fish) as "takana," the system will learn the difference and accurately recognize the word. Furthermore, if a particular child pronounces "kuruma" (car) as "uruma," the system will learn the characteristics and accurately recognize the word. This reduces misrecognition due to differences in pronunciation, enabling accurate word detection.

[0041] The communication support system may further include a noise filtering unit that filters background and environmental sounds to minimize the influence of noise. For example, a child's voice may be recognized by removing the sounds of a television or a car. Alternatively, a child's voice may be recognized by removing the sounds of wind or rain. Furthermore, a child's voice may be recognized by removing other people's voices and music. This minimizes the influence of background and environmental sounds, allowing the system to accurately detect a child's words.

[0042] The communication support system can be expanded to support different languages ​​and dialects, making it usable as a multilingual communication tool. For example, it can support multiple languages, including Japanese, English, and Spanish. It can also accurately recognize dialects such as the Kansai dialect and the Tohoku dialect. It can also enable smooth communication between parents who speak a foreign language and their children who speak Japanese. This makes multilingual communication possible by supporting different languages ​​and dialects.

[0043] The communication support system can also be used as a tool to support communication between teachers and students in educational settings. For example, it can be used by teachers to accurately understand what students say and provide appropriate instruction. It can also learn students' pronunciation and linguistic characteristics and be used as a support tool to help teachers accurately understand what students say. It can also be used in special needs education to help teachers accurately understand what students say. This can smooth communication between teachers and students in educational settings.

[0044] The communication support system can also refer to past question history to provide related information. For example, if a child who previously asked "Why is the sky blue?" now asks "Why is the ocean blue?", related information will be provided. Similarly, if a child who previously asked "Why does it rain?" now asks "Why does it snow?", related information will be provided. Furthermore, if a child who previously asked "Why can birds fly?" now asks "Why can airplanes fly?", related information will be provided. In this way, by referring to past question history, related information can be provided and children's understanding can be deepened.

[0045] The communication support system can also use visual aids to provide answers in a way that is easy for children to understand. For example, in response to the question "Why is the sky blue?", an animation of the sky changing color is used to explain. In response to the question "Why does it rain?", an animation of rain falling from clouds is used to explain. In response to the question "Why do plants grow?", an illustration showing the process of plant growth is used to explain. In this way, by using visual aids, answers can be provided in a way that is easy for children to understand.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The word detection unit detects the words the child is speaking. For example, it can use voice recognition technology to detect the child's words. The word detection unit can also learn the characteristics of a child's pronunciation to reduce misrecognition due to differences in pronunciation. For example, if a child pronounces "ringo" (apple) as "ingo," it can learn this characteristic and accurately recognize the words. Step 2: The conversion unit converts the child's words detected by the word detection unit into something that adults can understand. For example, if a child says "I'm hungry," the generation AI will tell the adult that the words mean "I'm hungry." The conversion unit can also be expanded to handle different languages ​​and dialects, making it possible to use it as a multilingual communication tool. For example, it can support multiple languages, including Japanese, English, and Spanish. Step 3: The answer generation unit generates an answer to the child's question and provides it in language the child can understand. For example, if a child asks, "Why is the sky blue?", the generation AI will analyze the question and provide an answer such as, "The sky is blue because sunlight hits tiny particles in the air, scattering the blue color." The answer generation unit can also refer to past question history and provide related information. For example, if a child who previously asked, "Why is the sky blue?" now asks, "Why is the ocean blue?", it will provide related information.

[0048] (Example 2) The communication support system according to an embodiment of the present invention detects the words spoken by a child, converts them into words that adults can understand, and provides answers to questions the child has in words the child can understand. This communication support system can eliminate frustration when parents cannot understand what the child is saying or when the child repeatedly asks questions such as "why?" and "how," thereby facilitating smooth communication between parents and children.

[0049] The communication support system according to the embodiment includes a word detection unit, a conversion unit, and an answer generation unit. The word detection unit detects the words a child is speaking. For example, it detects the child's words using voice recognition technology. The word detection unit can also learn the characteristics of a child's pronunciation to reduce misrecognition due to differences in pronunciation. For example, if a child pronounces "ringo" (apple) as "ingo," it can learn the characteristics and accurately recognize the pronunciation. The conversion unit converts the child's words detected by the word detection unit into words that an adult can understand. For example, if a child says "I'm hungry," the generation AI can convey to the adult that the words mean "I'm hungry." The conversion unit can also be expanded to support different languages ​​and dialects, allowing it to be used as a multilingual communication tool. For example, it supports multiple languages, including Japanese, English, and Spanish. The answer generation unit generates answers to the child's questions and provides them in language the child can understand. For example, if a child asks, "Why is the sky blue?", the generation AI analyzes the question and provides an answer such as, "The sky is blue because sunlight hits tiny particles in the air, scattering the blue color." The answer generation unit can also refer to past question history and provide related information. For example, if a child who previously asked, "Why is the sky blue?" now asks, "Why is the ocean blue?", the answer generation unit provides related information. This allows the communication support system according to the embodiment to eliminate frustration when parents cannot understand what their children are saying or when they are repeatedly asked, "Why?" and "How come?", thereby facilitating smooth communication between parents and children.

[0050] The word detection unit can estimate a child's emotions and convert them into appropriate words based on those emotions. For example, if a child says "I'm hungry," the generation AI estimates that emotion and converts it to "I'm very hungry" if the emotion is strong. For example, if a child says this while crying, the generation AI takes the intensity of their emotion into consideration when converting. Also, if a child says "I don't want to anymore," the generation AI estimates that emotion and converts it to "I really don't want to" if the emotion is strong. For example, if a child is angry, the generation AI converts it to reflect the intensity of their emotion. Also, if a child says "I'm scared," the generation AI estimates that emotion and converts it to "I'm very scared" if the emotion is strong. For example, if a child says this while trembling, the generation AI converts it to appropriate words based on the child's emotions, enabling smoother communication.

[0051] The word detection unit can learn the characteristics of children's pronunciation and reduce misrecognition due to differences in pronunciation. The word detection unit, for example, learns the characteristics of children's pronunciation and recognizes specific sound patterns. For example, if a child pronounces "ringo" (apple) as "ingo," it learns those characteristics and accurately recognizes them. It also takes differences in children's pronunciation into account to reduce misrecognition. For example, if a child pronounces "kakana" (fish) as "takana," it learns the difference and accurately recognizes them. It also registers children's pronunciation characteristics in a database and recognizes individual pronunciation patterns. For example, if a particular child pronounces "kuruma" (car) as "uruma," it learns those characteristics and accurately recognizes them. This reduces misrecognition due to differences in children's pronunciation, enabling accurate word detection.

[0052] The speech detection unit can filter background sounds and environmental sounds to minimize the influence of noise. The speech detection unit accurately detects children's speech using, for example, technology for filtering background sounds and environmental sounds. For example, television sounds and car sounds are removed to recognize children's voices. In addition, noise canceling technology is used to minimize the influence of environmental sounds. For example, wind sounds and rain sounds are removed to recognize children's voices. In addition, voice analysis technology is used to filter background sounds to accurately detect children's speech. For example, other people's voices and music are removed to recognize children's voices. In this way, children's speech can be accurately detected by minimizing the influence of background sounds and environmental sounds.

[0053] The conversion unit can be expanded to support different languages ​​and dialects, allowing it to be used as a multilingual communication tool. For example, the conversion unit can expand the child's speech detection and conversion functions to support multiple languages, making it compatible with children who speak different languages. For example, it can support multiple languages ​​such as Japanese, English, and Spanish. It can also be expanded to support dialects, recognizing regional differences in language. For example, it can accurately recognize dialects such as Kansai dialect and Tohoku dialect. It can also serve as a multilingual communication tool to support communication between parents and children who speak different languages. For example, it can enable smooth communication between parents who speak a foreign language and children who speak Japanese. This makes multilingual communication possible by supporting different languages ​​and dialects.

[0054] The conversion unit can be applied as a tool to support communication between teachers and students in educational settings. The conversion unit is applied, for example, as a tool to support communication between teachers and students in educational settings. For example, it is used to enable teachers to accurately understand what students are saying and provide appropriate instruction. It can also learn students' pronunciation and linguistic characteristics and be used as a support tool to enable teachers to accurately understand what students are saying. For example, it can reduce misrecognition due to differences in pronunciation. It can also utilize the detection and conversion functions of children's words to facilitate communication in educational settings. For example, it can be used in special needs education to enable teachers to accurately understand what students are saying. This facilitates communication between teachers and students in educational settings.

[0055] The conversion unit adds an emotion estimation function, making it possible to convey the emotion a child is feeling when speaking to a parent in real time. The conversion unit adds a function, for example, to estimate the emotion a child is feeling when speaking in real time and convey that emotion to a parent. For example, if a child sounds sad when speaking, the emotion is notified to the parent. The emotion estimation function is also used to analyze the emotion a child is feeling when speaking, making it easier for parents to understand their child's emotion. For example, if a child is angry, that emotion is conveyed to the parent. The emotion a child is feeling when speaking is also estimated in real time, and used as a support tool for parents to understand their child's emotion. For example, if a child sounds anxious when speaking, that emotion is notified to the parent. In this way, the emotion a child is feeling when speaking can be conveyed to the parent in real time, making it easier for parents to understand their child's emotion.

[0056] The answer generation unit can estimate a child's emotions and provide an answer in gentle language that corresponds to those emotions. For example, if a child asks "Why?" in an anxious manner, the generation AI can estimate the child's emotions and respond in reassuring words. For example, it can respond in gentle language such as "It's okay, here's why." If a child asks "Why?" in an angry manner, the generation AI can estimate the child's emotions and respond in calming words. For example, it can respond in gentle language such as "Calm down, here's why." If a child asks "Why?" in a sad manner, the generation AI can estimate the child's emotions and respond in comforting words. For example, it can respond in gentle language such as "It's sad, but here's why." This allows children to ask questions with peace of mind by providing answers in gentle language that corresponds to the child's emotions.

[0057] The answer generation unit can refer to past question history and provide related information. The answer generation unit, for example, refers to a child's past question history and provides related information. For example, if a child who previously asked "Why is the sky blue?" now asks "Why is the ocean blue?", related information is provided. Furthermore, an answer that takes the child's level of understanding into consideration is provided based on the question history. For example, if a child who previously asked "Why does it rain?" now asks "Why does it snow?", related information is provided. Furthermore, by referring to the past question history, an answer based on the child's interests and concerns is provided. For example, if a child who previously asked "Why can birds fly?" now asks "Why can airplanes fly?", related information is provided. In this way, by referring to the past question history, related information can be provided and the child's understanding can be deepened.

[0058] The answer generation unit can provide answers in a form that is easy for children to understand by using visual aids. The answer generation unit provides answers to questions from children using visual aids, for example. For example, the question "Why is the sky blue?" is explained using an animation of the sky changing color. Also, by using visual aids, answers are provided in a form that is easy for children to understand. For example, the question "Why does it rain?" is explained using an animation of rain falling from clouds. Also, answers to children's questions are provided using visual aids to promote children's understanding. For example, the question "Why do plants grow?" is explained using an illustration showing the process of plant growth. In this way, by using visual aids, answers can be provided in a form that is easy for children to understand.

[0059] The answer generation unit can be linked with other devices in the home, enabling use on multiple devices. For example, the answer generation unit links the answer function to children's questions with a smart speaker in the home to provide answers by voice. For example, when a child asks a question to a smart speaker, the generation AI provides an answer. The answer function to children's questions can also be linked with a tablet in the home to provide a visual answer. For example, when a child asks a question to a tablet, the generation AI provides an answer using illustrations or animations. The answer function to children's questions can also be linked with a television in the home to provide a visual answer on a large screen. For example, when a child asks a question to the television, the generation AI provides an answer using video. This allows use on multiple devices by linking with other devices in the home.

[0060] The answer generation unit can be integrated into educational apps or online learning platforms and used as a learning support tool. For example, the answer generation unit can be integrated into an educational app with a function for answering children's questions and used as a learning support tool. For example, when a child asks a question within the app, the generation AI provides an answer. Also, the answer generation unit can be integrated into an online learning platform with a function for answering children's questions and used as a learning support tool. For example, when a child asks a question within the platform, the generation AI provides an answer. Also, by integrating the answer generation unit into educational apps or online learning platforms, learning can be supported by providing answers to children's questions. For example, when a child has a question while studying, the generation AI provides an answer immediately. As a result, by integrating the answer generation unit into educational apps or online learning platforms, it can be used as a learning support tool.

[0061] The answer generation unit has an added emotion estimation function, and can analyze the emotion a child expresses when asking a question and provide an answer that corresponds to that emotion. The answer generation unit, for example, analyzes the emotion a child expresses when asking a question anxiously and provides an answer that corresponds to that emotion. For example, if the child asks "Why?" anxiously, the answer generation unit answers with reassuring words. The answer generation unit also analyzes the emotion a child expresses when asking a question excitedly and provides an answer that corresponds to that emotion. For example, if the child asks "Why?" excitedly, the answer generation unit answers with words that maintain the child's excitement. The answer generation unit also analyzes the emotion a child expresses when asking a question sadly and provides an answer that corresponds to that emotion. For example, if the child asks "Why?" sadly, the answer generation unit answers with words that soothe the child's emotion. In this way, by analyzing the emotion a child expresses when asking a question and providing an answer that corresponds to that emotion, it is possible to deepen a child's understanding.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The communication support system may further include a feedback unit that estimates the user's emotions and provides appropriate feedback based on the estimated emotions. For example, if a child feels anxious about a question, the feedback unit provides reassuring words such as "It's okay, take your time to think about it." If the child is excited, the feedback unit provides words to maintain the child's excitement, such as "That's great! Tell me more." If the child is angry, the feedback unit provides calming words such as "Calm down, let me hear your story." By providing appropriate feedback according to the child's emotions, smoother communication is possible.

[0064] The communication support system may further include an interest estimation unit that estimates a child's interests and provides related information based on those interests. For example, if a child asks about "dinosaurs," the interest estimation unit may estimate, "Maybe he wants to know more about the types of dinosaurs and their ecology," and provide related information. If a child asks about "space," the interest estimation unit may estimate, "Maybe he wants to know more about the stars and planets in space," and provide related information. If a child asks about "animals," the interest estimation unit may estimate, "Maybe he wants to know more about the ecology and habits of animals," and provide related information. In this way, providing information based on a child's interests and concerns can increase a child's motivation to learn.

[0065] The communication support system may further include a pronunciation learning unit that learns the characteristics of a child's pronunciation and reduces misrecognition due to differences in pronunciation. For example, if a child pronounces "ringo" (apple) as "ingo," the system will learn the characteristics and accurately recognize the word. Also, if a child pronounces "kakana" (fish) as "takana," the system will learn the difference and accurately recognize the word. Furthermore, if a particular child pronounces "kuruma" (car) as "uruma," the system will learn the characteristics and accurately recognize the word. This reduces misrecognition due to differences in pronunciation, enabling accurate word detection.

[0066] The communication support system may further include a noise filtering unit that filters background and environmental sounds to minimize the influence of noise. For example, a child's voice may be recognized by removing the sounds of a television or a car. Alternatively, a child's voice may be recognized by removing the sounds of wind or rain. Furthermore, a child's voice may be recognized by removing other people's voices and music. This minimizes the influence of background and environmental sounds, allowing the system to accurately detect a child's words.

[0067] The communication support system can be expanded to support different languages ​​and dialects, making it usable as a multilingual communication tool. For example, it can support multiple languages, including Japanese, English, and Spanish. It can also accurately recognize dialects such as the Kansai dialect and the Tohoku dialect. It can also enable smooth communication between parents who speak a foreign language and their children who speak Japanese. This makes multilingual communication possible by supporting different languages ​​and dialects.

[0068] The communication support system can also be used as a tool to support communication between teachers and students in educational settings. For example, it can be used by teachers to accurately understand what students say and provide appropriate instruction. It can also learn students' pronunciation and linguistic characteristics and be used as a support tool to help teachers accurately understand what students say. It can also be used in special needs education to help teachers accurately understand what students say. This can smooth communication between teachers and students in educational settings.

[0069] The communication support system can also convey to parents in real time the emotions a child is feeling when they speak. For example, if a child sounds sad when they speak, the system will notify the parent of that emotion. If a child sounds angry, the system will also notify the parent of that emotion. Furthermore, if a child sounds anxious when they speak, the system will also notify the parent of that emotion. In this way, by conveying the emotions a child is feeling when they speak in real time, it becomes easier for parents to understand their children's emotions.

[0070] The communication support system can also estimate a child's emotions and provide answers in gentle language that reflects those emotions. For example, if a child asks "Why?" in an anxious manner, the system will respond in reassuring terms. If a child asks "Why?" in an angry manner, the system will respond in calming terms. If a child asks "Why?" in a sad manner, the system will respond in comforting terms. By providing answers in gentle language that reflects a child's emotions, children can feel at ease when asking questions.

[0071] The communication support system can also refer to past question history to provide related information. For example, if a child who previously asked "Why is the sky blue?" now asks "Why is the ocean blue?", related information will be provided. Similarly, if a child who previously asked "Why does it rain?" now asks "Why does it snow?", related information will be provided. Furthermore, if a child who previously asked "Why can birds fly?" now asks "Why can airplanes fly?", related information will be provided. In this way, by referring to past question history, related information can be provided and children's understanding can be deepened.

[0072] The communication support system can also use visual aids to provide answers in a way that is easy for children to understand. For example, in response to the question "Why is the sky blue?", an animation of the sky changing color is used to explain. In response to the question "Why does it rain?", an animation of rain falling from clouds is used to explain. In response to the question "Why do plants grow?", an illustration showing the process of plant growth is used to explain. In this way, by using visual aids, answers can be provided in a way that is easy for children to understand.

[0073] The processing flow of the second embodiment will be briefly explained below.

[0074] Step 1: The word detection unit detects the words the child is speaking. For example, it can use voice recognition technology to detect the child's words. The word detection unit can also learn the characteristics of a child's pronunciation to reduce misrecognition due to differences in pronunciation. For example, if a child pronounces "ringo" (apple) as "ingo," it can learn this characteristic and accurately recognize the words. Step 2: The conversion unit converts the child's words detected by the word detection unit into something that adults can understand. For example, if a child says "I'm hungry," the generation AI will tell the adult that the words mean "I'm hungry." The conversion unit can also be expanded to handle different languages ​​and dialects, making it possible to use it as a multilingual communication tool. For example, it can support multiple languages, including Japanese, English, and Spanish. Step 3: The answer generation unit generates an answer to the child's question and provides it in language the child can understand. For example, if a child asks, "Why is the sky blue?", the generation AI will analyze the question and provide an answer such as, "The sky is blue because sunlight hits tiny particles in the air, scattering the blue color." The answer generation unit can also refer to past question history and provide related information. For example, if a child who previously asked, "Why is the sky blue?" now asks, "Why is the ocean blue?", it will provide related information.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0079] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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).

[0084] 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.

[0085] 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.

[0086] 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.

[0087] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0088] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0089] 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.

[0090] 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.

[0091] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0092] 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.

[0093] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0103] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0104] 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.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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."

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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]

[0142] 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 word detection unit that detects words spoken by a child; a conversion unit that converts the child's words detected by the word detection unit into words that can be understood by adults; an answer generation unit that generates answers to the child's questions and provides them in words that the child can understand; A system characterized by:

2. The word detection unit Infer the child's feelings and convert them into appropriate words based on those feelings 2. The system of claim 1.

3. The word detection unit Learn the characteristics of the child's pronunciation and reduce misrecognition due to differences in pronunciation 2. The system of claim 1.

4. The word detection unit Filters background and environmental sounds to minimize the impact of noise 2. The system of claim 1.

5. The conversion unit Expand it to support different languages ​​and dialects, and use it as a multilingual communication tool.

2. The system of claim 1.

Images