System

Abstract

An object of a system according to an embodiment is to provide an environment in which a child can naturally learn a foreign language.SOLUTION: A system includes a voice recognition unit, a translation unit, and an output unit. The voice recognition unit recognizes a Japanese conversation. The translation unit translates the Japanese conversation recognized by the voice recognition unit into a foreign language. The output unit outputs the foreign language translated by the translation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to provide an environment in which children can naturally learn a foreign language.

[0005] The system according to the embodiment aims to provide an environment in which children can naturally learn a foreign language. [Means for solving the problem]

[0006] The system according to the embodiment includes a speech recognition unit, a translation unit, and an output unit. The speech recognition unit recognizes Japanese speech. The translation unit translates the Japanese speech recognized by the speech recognition unit into a foreign language. The output unit outputs the foreign language translated by the translation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an environment in which children can naturally learn a foreign language. [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 multilingual acquisition system according to the embodiment of the present invention is a system that allows children to naturally acquire multiple languages ​​by equipping stuffed toys and the like with a simultaneous interpretation function using generative AI. This allows children to naturally acquire multiple languages.

[0029] A multilingual learning system according to an embodiment includes a speech recognition unit, a translation unit, and an output unit. The speech recognition unit recognizes Japanese conversation. For example, the speech recognition unit recognizes everyday conversation. The speech recognition unit can also recognize business conversation. The speech recognition unit can also recognize academic conversation. For example, the speech recognition unit recognizes speech using an acoustic model. The speech recognition unit can also recognize speech using a language model. The speech recognition unit can also recognize speech using an algorithm used. The translation unit translates the Japanese conversation recognized by the speech recognition unit into a foreign language. For example, the translation unit performs translation using a translation engine used. The translation unit can also use technology to improve translation accuracy. The translation unit can also increase the number of supported languages. For example, the translation unit translates into English. The translation unit can also translate into French. The translation unit can also translate into Spanish. The output unit outputs the foreign language translated by the translation unit. For example, the output unit performs speech output. The output unit can also perform text output. The output unit can also perform output using a display device. For example, the output unit may output audio using a speaker, display text using a display, or print text using a printer. This allows the multilingual acquisition system according to the embodiment to allow children to naturally acquire multiple languages.

[0030] The speech recognition unit can evaluate a child's pronunciation and intonation in real time and provide appropriate feedback. For example, the speech recognition unit allows the generation AI to analyze a child's pronunciation in real time and provide feedback to help them improve their pronunciation. For example, when a child pronounces "apple," the generation AI evaluates their pronunciation and offers advice to improve their pronunciation. The speech recognition unit also allows the generation AI to analyze a child's intonation and provide feedback to improve their pronunciation. For example, when a child says "How are you?", the generation AI points out areas for improvement in their intonation and teaches them the correct intonation. The speech recognition unit also allows the generation AI to evaluate a child's pronunciation and intonation and provide an individualized learning plan based on the results. For example, it generates a plan that focuses on practicing words and phrases that the child has difficulty pronouncing. This allows the child's pronunciation and intonation to improve.

[0031] The translation unit understands the context of the conversation and can translate taking into account the appropriate cultural background and nuances. For example, the generation AI analyzes the context of the conversation and translates taking into account the appropriate cultural background. For example, it translates the Japanese greeting "otsukaresama" into English as "Good job." The generation AI also understands the nuances of the conversation and selects the appropriate expression. For example, if a child says "arigatou" (thank you), the generation AI translates it as "Thank you," but depending on the context, it can also select expressions such as "Thanks" or "I appreciate it." The translation unit also considers the background information of the conversation and translates appropriately. For example, if a child says "itadakimasu" (thank you for the meal), the generation AI translates it as "Let's eat," understanding that this is a greeting said before eating. This makes it possible to translate taking into account the cultural background and nuances.

[0032] The translation unit can learn multiple languages ​​simultaneously, allowing children to smoothly switch between different languages. For example, the translation unit allows the generation AI to learn multiple languages ​​simultaneously, allowing children to smoothly switch between different languages. For example, if a child says "Good morning" after saying "Ohayo," the generation AI responds immediately. The translation unit also allows the generation AI to process multiple languages ​​simultaneously, translating without delay when a child switches languages. For example, when a child switches from Japanese to English or from English to French, the generation AI translates immediately. The translation unit also allows the generation AI to learn multiple languages ​​simultaneously, allowing children to smoothly switch between different languages. For example, if a child says "Thank you" and then "Merci," the generation AI responds immediately. This allows for smooth switching between multiple languages.

[0033] The speech recognition unit recognizes not only speech, but also gestures and facial expressions, and can perform translations based on these. For example, the generation AI recognizes a child's gestures and performs translations based on them. For example, if a child waves their hand and says "bye-bye," the generation AI translates it as "Goodbye." The speech recognition unit also analyzes the child's facial expressions and performs translations based on these. For example, if a child says "thank you" with a smile, the generation AI translates it as "Thank you" and pronounces it in a tone that reflects the emotion. The speech recognition unit also recognizes not only speech, but also gestures and facial expressions, and performs translations based on these. For example, if a child says "Amazing!" with a surprised expression, the generation AI translates it as "Amazing!" and pronounces it in a tone that reflects the emotion. This makes it possible to perform translations based on gestures and facial expressions.

[0034] The system can track a child's learning progress and provide an individually customized learning plan. For example, the system uses a generating AI to track a child's learning progress in real time and provide an individually customized learning plan. For example, it generates a plan to focus on practicing words and phrases that the child has difficulty pronouncing. The system also uses a generating AI to analyze a child's learning history and provide an individually customized learning plan. For example, it creates a plan to strengthen areas in which the child is weak based on past learning data. The system also uses a generating AI to track a child's learning progress and provide an individually customized learning plan. For example, it provides foreign language content related to topics that the child is interested in. This makes it possible to provide an individually customized learning plan.

[0035] The system can automatically provide relevant foreign language content based on a child's interests. For example, the system uses a generation AI to analyze a child's interests and automatically provide relevant foreign language content based on the analysis. For example, if a child is interested in animals, foreign language videos and articles about animals are provided. The system also uses a generation AI to track a child's interests and provide relevant foreign language content based on the analysis. For example, if a child is interested in sports, foreign language content related to sports is provided. The system also uses a generation AI to analyze a child's interests and automatically provide relevant foreign language content based on the analysis. For example, if a child is interested in music, foreign language content related to music is provided. This makes it possible to provide foreign language content based on a child's interests.

[0036] The system can provide visual, auditory, and tactile learning materials according to a child's learning style. For example, the system uses a generation AI to analyze a child's learning style and provide visual learning materials. For example, if a child prefers visual learning, it provides learning materials using illustrations and videos. The system also uses a generation AI to analyze a child's learning style and provide auditory learning materials. For example, if a child prefers auditory learning, it provides learning materials using audio or music. The system also uses a generation AI to analyze a child's learning style and provide tactile learning materials. For example, if a child prefers tactile learning, it provides learning materials that can be learned by touch. This makes it possible to provide learning materials according to a child's learning style.

[0037] The system can link with other devices in the home to create an integrated learning environment. For example, the generative AI in the system links with a smart speaker to create an environment where children can learn a foreign language through audio. For example, foreign language quizzes are presented through the smart speaker. The system can also link with a tablet to create an environment where children can learn a foreign language visually. For example, a foreign language picture book is displayed using the tablet. The system can also link with other devices in the home to create an integrated learning environment. For example, a smart speaker and a tablet can be linked to provide an environment where a foreign language can be learned both audio and visually. This provides an integrated learning environment.

[0038] The system can evaluate a child's language skills and provide language learning advice based on their future career path. For example, the system uses a generative AI to evaluate a child's language skills and provide language learning advice based on their future career path. For example, if a child is interested in international business, the system recommends learning business English. The system also uses a generative AI to evaluate a child's language skills and provide language learning advice based on their future career path. For example, if a child is interested in tourism, the system recommends learning tourism English or other languages ​​used in tourist destinations. The system also uses a generative AI to evaluate a child's language skills and provide language learning advice based on their future career path. For example, if a child is interested in the medical field, the system recommends learning medical English or other languages ​​used in medical settings. This makes it possible to provide language learning advice based on their future career path.

[0039] The system can cultivate well-rounded international human resources by having participants learn international culture and business etiquette at the same time. For example, the system can cultivate well-rounded international human resources by having the generative AI learn international culture and business etiquette. For example, it can provide content for learning intercultural understanding and international business etiquette. The system can also cultivate well-rounded international human resources by having the generative AI learn international culture and business etiquette. For example, it can provide simulations for improving intercultural communication skills. The system can also cultivate well-rounded international human resources by having the generative AI learn international culture and business etiquette. For example, it can provide teaching materials for learning international business etiquette and etiquette. This can cultivate well-rounded international human resources.

[0040] The system provides a platform for online interaction with children from different countries and regions, thereby cultivating practical language skills. For example, the system provides a platform for the generative AI to interact with children from different countries and regions online, thereby cultivating practical language skills. For example, they may interact through video chat or messaging functions. The system also provides a platform for the generative AI to interact with children from different countries and regions online, thereby cultivating practical language skills. For example, they may interact through collaborative projects or games. The system also provides a platform for the generative AI to interact with children from different countries and regions online, thereby cultivating practical language skills. For example, they may host cultural exchange events or workshops, thereby cultivating practical language skills.

[0041] The system suggests project-based learning that makes use of a child's language skills, allowing them to gain practical experience. For example, the system uses a generative AI to suggest project-based learning that makes use of a child's language skills, allowing them to gain practical experience. For example, it provides projects that involve making presentations or writing reports in a foreign language. The system also suggests project-based learning that makes use of a child's language skills, allowing them to gain practical experience. For example, it provides projects that involve conducting interviews or surveys in a foreign language. The system also suggests project-based learning that makes use of a child's language skills, allowing them to gain practical experience. For example, it provides projects that involve discussions or debates in a foreign language. This allows them to gain practical experience.

[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 system can track a child's learning progress and provide an individually customized learning plan. For example, the generating AI can track a child's learning progress in real time and generate a plan to focus on practicing words and phrases that the child has difficulty pronouncing. The generating AI can also analyze the child's learning history and create a plan to strengthen areas the child is weak in based on past learning data. Furthermore, the generating AI can increase the child's motivation to learn by providing foreign language content related to topics that interest the child. This allows the system to provide an individually customized learning plan.

[0044] The system can be linked with other devices in the home to create an integrated learning environment. For example, the generative AI can be linked with a smart speaker to create an environment where children can learn foreign languages ​​through audio. Foreign language quizzes can be given through the smart speaker. The generative AI can also be linked with a tablet to create an environment where children can learn foreign languages ​​visually. Foreign language picture books can also be displayed using the tablet. Furthermore, it is possible to link a smart speaker with a tablet to provide an environment where foreign languages ​​can be learned both audio and visually. This allows for the provision of an integrated learning environment.

[0045] The system can assess a child's language skills and provide language learning advice tailored to their future career path. For example, if the generative AI assesses a child's language skills and the child is interested in international business, it can recommend learning business English. If the child is interested in tourism, it can recommend learning tourism English or languages ​​used in other tourist destinations. Furthermore, if the child is interested in the medical field, it can recommend learning medical English or other languages ​​used in medical settings. This allows the system to provide language learning advice tailored to the child's future career path.

[0046] The system provides a platform for children from different countries and regions to interact online and develop practical language skills. For example, the generative AI allows children from different countries and regions to interact through video chat and messaging. They can also interact through joint projects and games. It is also possible to hold cultural exchange events and workshops to develop practical language skills. This helps develop practical language skills.

[0047] The system can suggest project-based learning that utilizes children's language skills and allows them to gain practical experience. For example, the generative AI can suggest projects in which they make presentations or write reports in a foreign language. It can also suggest projects in which they conduct interviews or surveys in a foreign language. It can also suggest projects in which they hold discussions or debates in a foreign language. This allows children to gain practical experience.

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

[0049] Step 1: The speech recognition unit recognizes Japanese conversation. For example, the speech recognition unit can recognize everyday conversation, business conversation, and academic conversation. The speech recognition unit recognizes speech using acoustic models, language models, and other algorithms. Step 2: The translation unit translates the Japanese conversation recognized by the speech recognition unit into a foreign language. For example, the translation unit uses a translation engine to translate and can use technology to improve the accuracy of the translation. The translation unit can translate into multiple languages, including English, French, and Spanish. Step 3: The output unit outputs the foreign language translated by the translation unit. For example, the output unit can output audio, text, or output using a display device. Specifically, audio can be output using a speaker, text can be displayed using a display, and text can be printed using a printer.

[0050] (Example 2) The multilingual acquisition system according to the embodiment of the present invention is a system that allows children to naturally acquire multiple languages ​​by equipping stuffed toys and the like with a simultaneous interpretation function using generative AI. This allows children to naturally acquire multiple languages.

[0051] A multilingual learning system according to an embodiment includes a speech recognition unit, a translation unit, and an output unit. The speech recognition unit recognizes Japanese conversation. For example, the speech recognition unit recognizes everyday conversation. The speech recognition unit can also recognize business conversation. The speech recognition unit can also recognize academic conversation. For example, the speech recognition unit recognizes speech using an acoustic model. The speech recognition unit can also recognize speech using a language model. The speech recognition unit can also recognize speech using an algorithm used. The translation unit translates the Japanese conversation recognized by the speech recognition unit into a foreign language. For example, the translation unit performs translation using a translation engine used. The translation unit can also use technology to improve translation accuracy. The translation unit can also increase the number of supported languages. For example, the translation unit translates into English. The translation unit can also translate into French. The translation unit can also translate into Spanish. The output unit outputs the foreign language translated by the translation unit. For example, the output unit performs speech output. The output unit can also perform text output. The output unit can also perform output using a display device. For example, the output unit may output audio using a speaker, display text using a display, or print text using a printer. This allows the multilingual acquisition system according to the embodiment to allow children to naturally acquire multiple languages.

[0052] The speech recognition unit can evaluate a child's pronunciation and intonation in real time and provide appropriate feedback. For example, the speech recognition unit allows the generation AI to analyze a child's pronunciation in real time and provide feedback to help them improve their pronunciation. For example, when a child pronounces "apple," the generation AI evaluates their pronunciation and offers advice to improve their pronunciation. The speech recognition unit also allows the generation AI to analyze a child's intonation and provide feedback to improve their pronunciation. For example, when a child says "How are you?", the generation AI points out areas for improvement in their intonation and teaches them the correct intonation. The speech recognition unit also allows the generation AI to evaluate a child's pronunciation and intonation and provide an individualized learning plan based on the results. For example, it generates a plan that focuses on practicing words and phrases that the child has difficulty pronouncing. This allows the child's pronunciation and intonation to improve.

[0053] The translation unit understands the context of the conversation and can translate taking into account the appropriate cultural background and nuances. For example, the generation AI analyzes the context of the conversation and translates taking into account the appropriate cultural background. For example, it translates the Japanese greeting "otsukaresama" into English as "Good job." The generation AI also understands the nuances of the conversation and selects the appropriate expression. For example, if a child says "arigatou" (thank you), the generation AI translates it as "Thank you," but depending on the context, it can also select expressions such as "Thanks" or "I appreciate it." The translation unit also considers the background information of the conversation and translates appropriately. For example, if a child says "itadakimasu" (thank you for the meal), the generation AI translates it as "Let's eat," understanding that this is a greeting said before eating. This makes it possible to translate taking into account the cultural background and nuances.

[0054] The translation unit uses an emotion estimation function to translate based on the child's emotional state, enabling conversations that are sensitive to the child's emotions. For example, the generation AI analyzes the child's emotional state and translates in a way that is sensitive to the child's emotions. For example, if a child says "goodbye" sadly, the generation AI translates it as "goodbye," but pronounces it in a gentle tone. Furthermore, the translation unit uses the emotion estimation function to translate when a child is excited, so the generation AI translates it to match the child's emotions. For example, if a child excitedly says "wow!", the generation AI translates it as "Amazing!" and pronounces it in a tone that reflects the child's emotions. Furthermore, the translation unit allows the generation AI to understand the child's emotions and provide appropriate feedback. For example, if a child anxiously says "I can't do it," the generation AI translates it as an encouraging "You can do it!" This enables translations that are sensitive to the child's emotions.

[0055] The translation unit can learn multiple languages ​​simultaneously, allowing children to smoothly switch between different languages. For example, the translation unit allows the generation AI to learn multiple languages ​​simultaneously, allowing children to smoothly switch between different languages. For example, if a child says "Good morning" after saying "Ohayo," the generation AI responds immediately. The translation unit also allows the generation AI to process multiple languages ​​simultaneously, translating without delay when a child switches languages. For example, when a child switches from Japanese to English or from English to French, the generation AI translates immediately. The translation unit also allows the generation AI to learn multiple languages ​​simultaneously, allowing children to smoothly switch between different languages. For example, if a child says "Thank you" and then "Merci," the generation AI responds immediately. This allows for smooth switching between multiple languages.

[0056] The speech recognition unit recognizes not only speech, but also gestures and facial expressions, and can perform translations based on these. For example, the generation AI recognizes a child's gestures and performs translations based on them. For example, if a child waves their hand and says "bye-bye," the generation AI translates it as "Goodbye." The speech recognition unit also analyzes the child's facial expressions and performs translations based on these. For example, if a child says "thank you" with a smile, the generation AI translates it as "Thank you" and pronounces it in a tone that reflects the emotion. The speech recognition unit also recognizes not only speech, but also gestures and facial expressions, and performs translations based on these. For example, if a child says "Amazing!" with a surprised expression, the generation AI translates it as "Amazing!" and pronounces it in a tone that reflects the emotion. This makes it possible to perform translations based on gestures and facial expressions.

[0057] The translation unit can use the emotion estimation function to provide a linguistic expression appropriate for a child's specific emotion when that emotion is expressed. For example, when a child sadly says "goodbye," the generation AI translates it as "goodbye" and pronounces it in a gentle tone. The translation unit also analyzes the child's emotion and provides a linguistic expression appropriate for that emotion. For example, when a child excitedly says "wow!", the generation AI translates it as "Amazing!" and pronounces it in a tone that reflects the emotion. The translation unit also uses the emotion estimation function to provide a linguistic expression appropriate for a child's specific emotion when that emotion is expressed. For example, when a child anxiously says "I can't do it," the generation AI translates it as "You can do it!", which is an encouraging phrase. This makes it possible to provide a linguistic expression appropriate for the emotion.

[0058] The system can track a child's learning progress and provide an individually customized learning plan. For example, the system uses a generating AI to track a child's learning progress in real time and provide an individually customized learning plan. For example, it generates a plan to focus on practicing words and phrases that the child has difficulty pronouncing. The system also uses a generating AI to analyze a child's learning history and provide an individually customized learning plan. For example, it creates a plan to strengthen areas in which the child is weak based on past learning data. The system also uses a generating AI to track a child's learning progress and provide an individually customized learning plan. For example, it provides foreign language content related to topics that the child is interested in. This makes it possible to provide an individually customized learning plan.

[0059] The system can automatically provide relevant foreign language content based on a child's interests. For example, the system uses a generation AI to analyze a child's interests and automatically provide relevant foreign language content based on the analysis. For example, if a child is interested in animals, foreign language videos and articles about animals are provided. The system also uses a generation AI to track a child's interests and provide relevant foreign language content based on the analysis. For example, if a child is interested in sports, foreign language content related to sports is provided. The system also uses a generation AI to analyze a child's interests and automatically provide relevant foreign language content based on the analysis. For example, if a child is interested in music, foreign language content related to music is provided. This makes it possible to provide foreign language content based on a child's interests.

[0060] The system can provide visual, auditory, and tactile learning materials according to a child's learning style. For example, the system uses a generation AI to analyze a child's learning style and provide visual learning materials. For example, if a child prefers visual learning, it provides learning materials using illustrations and videos. The system also uses a generation AI to analyze a child's learning style and provide auditory learning materials. For example, if a child prefers auditory learning, it provides learning materials using audio or music. The system also uses a generation AI to analyze a child's learning style and provide tactile learning materials. For example, if a child prefers tactile learning, it provides learning materials that can be learned by touch. This makes it possible to provide learning materials according to a child's learning style.

[0061] The system can link with other devices in the home to create an integrated learning environment. For example, the generative AI in the system links with a smart speaker to create an environment where children can learn a foreign language through audio. For example, foreign language quizzes are presented through the smart speaker. The system can also link with a tablet to create an environment where children can learn a foreign language visually. For example, a foreign language picture book is displayed using the tablet. The system can also link with other devices in the home to create an integrated learning environment. For example, a smart speaker and a tablet can be linked to provide an environment where a foreign language can be learned both audio and visually. This provides an integrated learning environment.

[0062] The system can use the emotion estimation function to identify the way a child most enjoys learning and build a lesson plan around that way. For example, the system can use the emotion estimation function to identify the way a child most enjoys learning and build a lesson plan around that way. For example, if a child enjoys games, it provides a lesson plan that incorporates games. The system also uses a generative AI to analyze a child's emotional state and identify the way a child most enjoys learning. For example, if a child enjoys music, it provides a lesson plan that incorporates music. The system can also use the emotion estimation function to identify the way a child most enjoys learning and build a lesson plan around that way. For example, if a child enjoys drawing, it provides a lesson plan that incorporates drawing. This makes it possible to provide a way for a child to learn that they enjoy.

[0063] The system can evaluate a child's language skills and provide language learning advice based on their future career path. For example, the system uses a generative AI to evaluate a child's language skills and provide language learning advice based on their future career path. For example, if a child is interested in international business, the system recommends learning business English. The system also uses a generative AI to evaluate a child's language skills and provide language learning advice based on their future career path. For example, if a child is interested in tourism, the system recommends learning tourism English or other languages ​​used in tourist destinations. The system also uses a generative AI to evaluate a child's language skills and provide language learning advice based on their future career path. For example, if a child is interested in the medical field, the system recommends learning medical English or other languages ​​used in medical settings. This makes it possible to provide language learning advice based on their future career path.

[0064] The system can cultivate well-rounded international human resources by having participants learn international culture and business etiquette at the same time. For example, the system can cultivate well-rounded international human resources by having the generative AI learn international culture and business etiquette. For example, it can provide content for learning intercultural understanding and international business etiquette. The system can also cultivate well-rounded international human resources by having the generative AI learn international culture and business etiquette. For example, it can provide simulations for improving intercultural communication skills. The system can also cultivate well-rounded international human resources by having the generative AI learn international culture and business etiquette. For example, it can provide teaching materials for learning international business etiquette and etiquette. This can cultivate well-rounded international human resources.

[0065] The system can use the emotion estimation function to analyze the emotions a child has about their future career and provide support to increase their motivation. For example, the system uses the emotion estimation function to analyze the emotions a child has about their future career and provide support to increase their motivation. For example, if a child is feeling anxious, it can provide an encouraging message. The system also uses the generative AI to analyze the child's emotions and provide support to elicit positive emotions about their future career. For example, it can introduce success stories and role models. The system also uses the emotion estimation function to analyze the emotions a child has about their future career and provide support to increase their motivation. For example, it can suggest career paths related to fields that the child is interested in. This can help increase the child's motivation for their career.

[0066] The system provides a platform for online interaction with children from different countries and regions, thereby cultivating practical language skills. For example, the system provides a platform for the generative AI to interact with children from different countries and regions online, thereby cultivating practical language skills. For example, they may interact through video chat or messaging functions. The system also provides a platform for the generative AI to interact with children from different countries and regions online, thereby cultivating practical language skills. For example, they may interact through collaborative projects or games. The system also provides a platform for the generative AI to interact with children from different countries and regions online, thereby cultivating practical language skills. For example, they may host cultural exchange events or workshops, thereby cultivating practical language skills.

[0067] The system suggests project-based learning that makes use of a child's language skills, allowing them to gain practical experience. For example, the system uses a generative AI to suggest project-based learning that makes use of a child's language skills, allowing them to gain practical experience. For example, it provides projects that involve making presentations or writing reports in a foreign language. The system also suggests project-based learning that makes use of a child's language skills, allowing them to gain practical experience. For example, it provides projects that involve conducting interviews or surveys in a foreign language. The system also suggests project-based learning that makes use of a child's language skills, allowing them to gain practical experience. For example, it provides projects that involve discussions or debates in a foreign language. This allows them to gain practical experience.

[0068] The system uses the emotion estimation function to recommend language learning related to the area in which the child is most interested, thereby increasing their motivation to learn. For example, the system uses the emotion estimation function to recommend language learning related to the area in which the child is most interested, thereby increasing their motivation to learn. For example, if the child is interested in science, foreign language content related to science is provided. The system also uses the generative AI to analyze the child's emotions and recommend language learning related to the area in which the child is most interested. For example, if the child is interested in art, foreign language content related to art is provided. The system also uses the emotion estimation function to recommend language learning related to the area in which the child is most interested, thereby increasing their motivation to learn. For example, if the child is interested in sports, foreign language content related to sports is provided. This can increase the child's motivation to learn.

[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 system can track a child's learning progress and provide an individually customized learning plan. For example, the generating AI can track a child's learning progress in real time and generate a plan to focus on practicing words and phrases that the child has difficulty pronouncing. The generating AI can also analyze the child's learning history and create a plan to strengthen areas the child is weak in based on past learning data. Furthermore, the generating AI can increase the child's motivation to learn by providing foreign language content related to topics that interest the child. This allows the system to provide an individually customized learning plan.

[0071] The system can be linked with other devices in the home to create an integrated learning environment. For example, the generative AI can be linked with a smart speaker to create an environment where children can learn foreign languages ​​through audio. Foreign language quizzes can be given through the smart speaker. The generative AI can also be linked with a tablet to create an environment where children can learn foreign languages ​​visually. Foreign language picture books can also be displayed using the tablet. Furthermore, it is possible to link a smart speaker with a tablet to provide an environment where foreign languages ​​can be learned both audio and visually. This allows for the provision of an integrated learning environment.

[0072] The system can assess a child's language skills and provide language learning advice tailored to their future career path. For example, if the generative AI assesses a child's language skills and the child is interested in international business, it can recommend learning business English. If the child is interested in tourism, it can recommend learning tourism English or languages ​​used in other tourist destinations. Furthermore, if the child is interested in the medical field, it can recommend learning medical English or other languages ​​used in medical settings. This allows the system to provide language learning advice tailored to the child's future career path.

[0073] The system provides a platform for children from different countries and regions to interact online and develop practical language skills. For example, the generative AI allows children from different countries and regions to interact through video chat and messaging. They can also interact through joint projects and games. It is also possible to hold cultural exchange events and workshops to develop practical language skills. This helps develop practical language skills.

[0074] The system can suggest project-based learning that utilizes children's language skills and allows them to gain practical experience. For example, the generative AI can suggest projects in which they make presentations or write reports in a foreign language. It can also suggest projects in which they conduct interviews or surveys in a foreign language. It can also suggest projects in which they hold discussions or debates in a foreign language. This allows children to gain practical experience.

[0075] Using emotion estimation, the system can identify the way a child most enjoys learning and build a lesson plan around that. For example, if a child enjoys playing games, it can provide a lesson plan that incorporates games. The generative AI can also analyze a child's emotional state and, if they enjoy music, it can provide a lesson plan that incorporates music. Furthermore, if they enjoy drawing, it can provide a lesson plan that incorporates drawing. This allows the system to provide children with ways to learn that they enjoy.

[0076] The system uses its emotion estimation function to analyze the feelings a child has about their future career and provide support to increase their motivation. For example, if a child is feeling anxious, it can provide an encouraging message. The generative AI can also analyze a child's emotions and introduce success stories and role models to help elicit positive feelings about their future career. It can also suggest career paths related to the child's field of interest. This can help increase a child's motivation for their career.

[0077] Using its emotion estimation function, the system can recommend language learning related to a child's most interesting subject, increasing their motivation to learn. For example, if a child is interested in science, it can provide foreign language content related to science. The generative AI can also analyze a child's emotions and, if they are interested in art, it can provide foreign language content related to art. Furthermore, if they are interested in sports, it can provide foreign language content related to sports. This can increase a child's motivation to learn.

[0078] Using its emotion estimation function, the system can provide language expressions appropriate to a child's emotions when that child expresses a specific emotion. For example, if a child sadly says "goodbye," the generation AI can translate it as "goodbye" and pronounce it in a gentle tone. If the generation AI analyzes a child's emotions and excitedly says "wow!", it can translate it as "amazing!" and pronounce it in a tone that reflects that emotion. Furthermore, if a child anxiously says "I can't do it," it can translate it as an encouraging phrase like "You can do it!" This allows the system to provide language expressions appropriate to the emotion.

[0079] Using its emotion estimation function, the system can translate based on the child's emotional state, enabling conversations that are sensitive to their emotions. For example, if a child sadly says "goodbye," the AI ​​generator will translate it as "goodbye" and pronounce it in a gentle tone. If the AI ​​analyzes a child's emotions and excitedly says "amazing!", it can translate it as "Amazing!" and pronounce it in a tone that reflects that emotion. Furthermore, if a child anxiously says "I can't do it," it can translate it as an encouraging "You can do it!". This makes it possible to translate in a way that is sensitive to emotions.

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

[0081] Step 1: The speech recognition unit recognizes Japanese conversation. For example, the speech recognition unit can recognize everyday conversation, business conversation, and academic conversation. The speech recognition unit recognizes speech using acoustic models, language models, and other algorithms. Step 2: The translation unit translates the Japanese conversation recognized by the speech recognition unit into a foreign language. For example, the translation unit uses a translation engine to translate and can use technology to improve the accuracy of the translation. The translation unit can translate into multiple languages, including English, French, and Spanish. Step 3: The output unit outputs the foreign language translated by the translation unit. For example, the output unit can output audio, text, or output using a display device. Specifically, audio can be output using a speaker, text can be displayed using a display, and text can be printed using a printer.

[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 the 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 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.

[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. 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.

[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 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.

[0110] 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.

[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 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.

[0126] 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 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.

[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 speech recognition unit that recognizes Japanese conversations; a translation unit that translates the Japanese conversation recognized by the speech recognition unit into a foreign language; an output unit that outputs the foreign language translated by the translation unit; A system characterized by:

2. The voice recognition unit Evaluate your child's pronunciation and intonation in real time and provide appropriate feedback 2. The system of claim 1.

3. The translation unit Understand the context of the conversation and translate with appropriate cultural context and nuance 2. The system of claim 1.

4. The translation unit Translating based on the child's emotional state, enabling conversations that are sensitive to their emotions 2. The system of claim 1.

5. The translation unit Learning multiple languages ​​simultaneously and helping children switch smoothly between different languages 2. The system of claim 1.

6. The voice recognition unit It recognizes not only voice but also gestures and facial expressions and translates based on them.

2. The system of claim 1.

7. The translation unit When a child expresses a particular emotion, provide appropriate verbal expressions for that emotion.

2. The system of claim 1.

8. The system comprises: Track your child's progress and provide personalized learning plans 2. The system of claim 1.

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