System

Abstract

An object of the system according to the embodiment is to integrate educational contents and provide the educational contents in multiple languages.SOLUTION: A system includes an information aggregation unit, a generation unit, and a translation unit. The information aggregation unit centrally aggregates information on lesson contents and wisdom from teachers of elementary schools to professors of universities in Japan and overseas. The generation unit generates learning content based on the information aggregated by the information aggregation unit. The translation unit translates the learning content generated by the generation unit into multiple languages.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the problem that educational content is dispersed, making it difficult to efficiently consolidate and utilize it.

[0005] The system according to the embodiment aims to centrally collect educational content and provide it in multiple languages. [Means for solving the problem]

[0006] The system according to the embodiment includes an information aggregation unit, a generation unit, and a translation unit. The information aggregation unit centrally aggregates information on lesson content and wisdom from elementary school teachers to university professors in Japan and overseas. The generation unit generates learning content based on the information aggregated by the information aggregation unit. The translation unit translates the learning content generated by the generation unit into multiple languages. [Effects of the Invention]

[0007] The system according to the embodiment can centrally collect educational content and provide it in multiple languages. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The learning support system according to an embodiment of the present invention centrally collects information on lesson content and wisdom from elementary school teachers to university professors in Japan and overseas, and provides it through a generative AI. This allows the learning support system to provide new learning experiences to children and adults around the world, helping to create a more creative and equal world.

[0029] A learning support system according to an embodiment includes an information aggregation unit, a generation unit, and a translation unit. The information aggregation unit centrally aggregates information on lesson content and knowledge from elementary school teachers to university professors in Japan and overseas. For example, it aggregates lesson videos, lecture notes, experimental data, research papers, and the like. The information aggregation unit also stores this information in a database and converts it into a format that is easy for the generation AI to analyze. The generation unit generates learning content based on the information aggregated by the information aggregation unit. For example, when the generation AI receives a prompt requesting a lecture on a specific topic, it generates optimal lecture content on that topic. The generation AI can also generate learning content tailored to user needs. For example, the generation AI generates learning content based on prompts containing instructions on what the user wants to learn. The translation unit translates the learning content generated by the generation unit into multiple languages. For example, it translates lesson content written in Japanese into English, French, and other languages. The translation unit can also use the generation AI to provide more natural translations by taking into account the cultural background and nuances of the translated content. This enables the learning support system to provide new learning experiences for children and adults around the world, helping to create a more creative and equal world.

[0030] The information aggregator can automatically classify content and convert it into the optimal format based on the educator's field of expertise and teaching style. For example, the information aggregator registers the educator's field of expertise and teaching style in a database and automatically classifies lesson content based on that information. For example, science lesson content and humanities lesson content are stored separately. The information aggregator also builds a system that converts content into the optimal format based on the educator's teaching style (e.g., lecture format, discussion format). For example, lecture-style content is converted into slide format. The information aggregator also automatically tags lesson content according to the educator's field of expertise to make it easier to search. For example, physics lesson content is tagged with "physics." This makes it possible to provide the best learning experience for learners by optimizing content based on the educator's field of expertise and teaching style.

[0031] The information aggregator can analyze educators' past lesson evaluations and feedback, and prioritize the aggregation of highly rated lesson content. For example, the information aggregator can analyze educators' past lesson evaluation data and build a system that automatically extracts highly rated lesson content. For example, it can prioritize the aggregation of lesson videos that have received high ratings from students. The information aggregator can also perform text analysis of lesson feedback and prioritize the aggregation of lesson content that has received a lot of positive feedback. For example, it can analyze feedback that includes keywords such as "easy to understand" and "interesting." The information aggregator can also develop a system that automatically ranks highly rated lesson content and stores the top content in a database. For example, it can display content in order of highest evaluation score. This makes it possible to provide a high-quality learning experience by prioritizing the aggregation of highly rated lesson content.

[0032] In addition to lesson content, the information aggregation unit also aggregates the content of educator interviews and panel discussions, allowing for more multifaceted learning. For example, the information aggregation unit collects video and audio data of educator interviews and stores them in a database together with lesson content. For example, it aggregates interviews that include educators' educational philosophies and lesson innovations. The information aggregation unit also builds a system that converts the content of panel discussions into text and integrates it with lesson content. For example, it adds the content of discussions between educators to lesson notes. The information aggregation unit also analyzes the content of educator interviews and panel discussions and links them to related lesson content. For example, it links discussion content on a specific theme with related lesson videos. In this way, by aggregating the content of educator interviews and panel discussions, it is possible to provide more multifaceted learning.

[0033] The information aggregation unit can conduct comparative analyses of different educational systems and curricula and propose optimal teaching methods. For example, the information aggregation unit collects educational systems and curricula from different countries and regions and builds a system for comparative analyses. For example, it compares the mathematics education curricula of Japan and the United States. The information aggregation unit also develops a system that proposes optimal teaching methods based on the results of the comparative analyses. For example, it proposes incorporating the advantages of a particular educational system into other systems. The information aggregation unit also visualizes data from different educational systems and curricula, allowing educators to easily compare them. For example, it displays the differences in curricula using graphs and charts. This makes it possible to conduct comparative analyses of different educational systems and curricula and propose optimal teaching methods.

[0034] The generation unit can analyze a user's learning history and interests to generate individually optimized learning content. For example, the generation unit analyzes a user's learning history and builds a system that generates individually optimized learning content based on the content learned in the past and interests. For example, it provides new content related to themes the user has previously studied. The generation unit also analyzes a user's interests and generates learning content based on those interests. For example, it provides lectures and experimental data related to topics that interest the user. The generation unit also integrates learning history and interest data to develop a system that generates individually optimized learning plans. For example, it suggests the next content to learn based on the user's learning progress. This makes it possible to improve learning efficiency by providing individually optimized learning content based on the user's learning history and interests.

[0035] The generation unit can collect user feedback on learning content in real time and improve the content based on that feedback. For example, the generation unit collects user feedback on learning content in real time and builds a system in which the generation AI improves the content based on that data. For example, it analyzes user comments and ratings. The generation unit also automatically updates the learning content based on user feedback. For example, it revises the lecture content to reflect user opinions. The generation unit also analyzes the feedback data collected in real time and develops a system in which the generation AI identifies areas for improvement in the content. For example, it makes improvements to resolve user dissatisfaction. This makes it possible to improve the learning experience by improving the learning content based on user feedback.

[0036] The generation unit can incorporate game elements into learning content to enhance its entertainment value. For example, the generation unit uses a generation AI to build a system that incorporates game elements into learning content. For example, it introduces quiz-style questions or a point system. The generation unit also adds game elements to the learning content to enhance its entertainment value. For example, it introduces a system that allows users to level up according to their learning progress. The generation unit also generates learning content that incorporates game elements, allowing users to learn while having fun. For example, it provides a simulation game based on the learning content. In this way, incorporating game elements into learning content can enhance its entertainment value and attract learners' interest.

[0037] The generation unit generates content that corresponds to different learning styles, thereby improving the user's learning efficiency. The generation unit, for example, uses generative AI to build a system that generates learning content that corresponds to visual, auditory, and tactile senses. For example, it provides video lectures, audio lectures, and interactive experimental data. The generation unit also generates content that corresponds to different learning styles, thereby improving the user's learning efficiency. For example, it provides content that makes extensive use of diagrams and graphs to visual learners. The generation unit also develops a system that automatically generates content according to learning styles, allowing users to learn in the optimal way. For example, it provides lectures in podcast format to auditory learners. In this way, it is possible to improve the user's learning efficiency by providing content that corresponds to different learning styles.

[0038] The translation unit takes into account the cultural background and nuances of the translated content, allowing it to provide more natural translations. For example, the translation unit uses generative AI to build a system that takes into account the cultural background and nuances of the translated content. For example, it appropriately translates expressions and phrases that are unique to a particular culture. Furthermore, to preserve the nuances of the translated content, the generative AI understands the context and provides an appropriate translation. For example, it translates jokes and metaphors naturally. Furthermore, to provide translations that take cultural background into account, the generative AI references a database of each country's culture and customs. For example, it translates using culturally appropriate examples and cases. This allows it to provide more natural translations by taking cultural background and nuances into account.

[0039] The translation department can integrate the translated content into a platform where users of different languages ​​can study together. For example, the translation department builds a system that integrates translated content into a platform where users of different languages ​​can study together. For example, it allows English and Japanese users to study the same content. The translation department also develops a system that shares translated content in real time so that users of different languages ​​can study together. For example, it supports online discussions and collaborative work. The translation department also provides a platform where users of different languages ​​can work together on projects based on the translated content. For example, it supports collaborative research and group work. In this way, it is possible to promote international learning exchange by providing a platform where users of different languages ​​can study together.

[0040] The translation department may also provide the translated content in audio or video format to promote visual and auditory learning. For example, the translation department may build a system that provides translated content in audio format to promote visual and auditory learning. For example, the translation department may provide translated lectures as audio files. The translation department may also provide translated content in video format to enable users to learn visually. For example, the translation department may provide translated lecture videos with subtitles. The translation department may also develop a system that generates translated content in audio or video format in real time to enable users to access it instantly. For example, the translation department may provide a live translation function. This may promote visual and auditory learning by providing translated content in audio or video format.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The information aggregator can automatically classify content and convert it into the optimal format based on the educator's field of expertise and teaching style. For example, the educator's field of expertise and teaching style are registered in a database, and lesson content is automatically classified based on that information. For example, science lesson content and humanities lesson content are stored separately. The information aggregator also builds a system that converts content into the optimal format based on the educator's teaching style (e.g., lecture format, discussion format). For example, lecture-style content is converted into slide format. The information aggregator also automatically tags lesson content according to the educator's field of expertise, making it easier to search. For example, physics lesson content is tagged with "physics." This makes it possible to provide the best learning experience for learners by optimizing content based on the educator's field of expertise and teaching style.

[0043] The information aggregation unit can analyze educators' past lesson evaluations and feedback, and prioritize the aggregation of highly rated lesson content. For example, a system can be built that analyzes educators' past lesson evaluation data and automatically extracts highly rated lesson content. For example, lesson videos that have received high ratings from students can be prioritized and aggregated. The information aggregation unit can also perform text analysis of lesson feedback and prioritize the aggregation of lesson content that has received a lot of positive feedback. For example, it can analyze feedback that includes keywords such as "easy to understand" and "interesting." The information aggregation unit can also develop a system that automatically ranks highly rated lesson content and stores the top content in a database. For example, content can be displayed in descending order of evaluation score. This makes it possible to provide a high-quality learning experience by prioritizing the aggregation of highly rated lesson content.

[0044] In addition to lesson content, the information aggregation unit can also aggregate the content of educator interviews and panel discussions, allowing for more multifaceted learning. For example, it can collect video and audio data of educator interviews and store them in a database along with lesson content. For example, it can aggregate interviews that include educators' educational philosophies and innovative lesson ideas. The information aggregation unit can also convert the content of panel discussions into text and build a system that integrates it with lesson content. For example, it can add the content of discussions between educators to lesson notes. The information aggregation unit can also analyze the content of educator interviews and panel discussions and link them to related lesson content. For example, it can link discussion content on a specific theme with related lesson videos. In this way, by aggregating the content of educator interviews and panel discussions, it can provide more multifaceted learning.

[0045] The information aggregation unit can conduct comparative analyses of different educational systems and curricula and propose optimal teaching methods. For example, it can collect information on educational systems and curricula from different countries and regions and build a system for conducting comparative analyses. For example, it can compare the mathematics education curricula of Japan and the United States. The information aggregation unit can also develop a system that proposes optimal teaching methods based on the results of the comparative analyses. For example, it can propose incorporating the advantages of a particular educational system into other systems. The information aggregation unit can also visualize data on different educational systems and curricula, making it easier for educators to compare them. For example, it can display the differences in curricula using graphs and charts. This makes it possible to conduct comparative analyses of different educational systems and curricula and propose optimal teaching methods.

[0046] The generation unit can analyze a user's learning history and interests to generate individually optimized learning content. For example, a system is constructed that analyzes a user's learning history and generates individually optimized learning content based on the content learned in the past and their interests. For example, new content related to themes the user has previously studied is provided. The generation unit also analyzes the user's interests and generates learning content based on those interests. For example, lectures and experimental data related to topics that interest the user are provided. The generation unit also develops a system that integrates learning history and interest data to generate individually optimized learning plans. For example, it suggests the next content to learn based on the user's learning progress. This makes it possible to improve learning efficiency by providing individually optimized learning content based on the user's learning history and interests.

[0047] The generation unit can collect user feedback on learning content in real time and improve the content based on that feedback. For example, a system can be built in which user feedback on learning content is collected in real time and the generation AI improves the content based on that data. For example, user comments and ratings can be analyzed. The generation unit also automatically updates the learning content based on user feedback. For example, the generation unit can revise the lecture content to reflect user opinions. The generation unit also analyzes the feedback data collected in real time and develops a system in which the generation AI identifies areas for improvement in the content. For example, improvements can be made to resolve user dissatisfaction. This makes it possible to improve the learning experience by improving the learning content based on user feedback.

[0048] The generation unit can incorporate game elements into learning content to increase its entertainment value. For example, a generation AI can be used to build a system that incorporates game elements into learning content. For example, quiz-style questions or a point system can be introduced. The generation unit can also add game elements to the learning content to increase its entertainment value. For example, a system can be introduced that allows users to level up according to their learning progress. The generation unit can also generate learning content that incorporates game elements, allowing users to learn while having fun. For example, it can provide a simulation game based on the learning content. In this way, incorporating game elements into learning content can increase its entertainment value and attract learners' interest.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The information aggregation unit centrally collects information on lesson content and knowledge from people in Japan and overseas, from elementary school teachers to university professors. For example, it collects lesson videos, lecture notes, experimental data, research papers, etc. The information aggregation unit also stores this information in a database and converts it into a format that is easy for the generative AI to analyze. Step 2: The generation unit generates learning content based on the information aggregated by the information aggregation unit. For example, when the generation AI receives a prompt requesting a lecture on a specific topic, it generates the optimal lecture content on that topic. The generation AI can also generate learning content tailored to the user's needs. For example, it generates learning content based on a prompt that includes instructions on what the user wants to learn. Step 3: The translation unit translates the learning content generated by the generation unit into multiple languages. For example, lesson content created in Japanese can be translated into English, French, etc. The translation unit can also use generative AI to take into account the cultural background and nuances of the translated content to provide a more natural translation.

[0051] (Example 2) The learning support system according to an embodiment of the present invention centrally collects information on lesson content and wisdom from elementary school teachers to university professors in Japan and overseas, and provides it through a generative AI. This allows the learning support system to provide new learning experiences to children and adults around the world, helping to create a more creative and equal world.

[0052] A learning support system according to an embodiment includes an information aggregation unit, a generation unit, and a translation unit. The information aggregation unit centrally aggregates information on lesson content and knowledge from elementary school teachers to university professors in Japan and overseas. For example, it aggregates lesson videos, lecture notes, experimental data, research papers, and the like. The information aggregation unit also stores this information in a database and converts it into a format that is easy for the generation AI to analyze. The generation unit generates learning content based on the information aggregated by the information aggregation unit. For example, when the generation AI receives a prompt requesting a lecture on a specific topic, it generates optimal lecture content on that topic. The generation AI can also generate learning content tailored to user needs. For example, the generation AI generates learning content based on prompts containing instructions on what the user wants to learn. The translation unit translates the learning content generated by the generation unit into multiple languages. For example, it translates lesson content written in Japanese into English, French, and other languages. The translation unit can also use the generation AI to provide more natural translations by taking into account the cultural background and nuances of the translated content. This enables the learning support system to provide new learning experiences for children and adults around the world, helping to create a more creative and equal world.

[0053] The information aggregating unit can perform sentiment analysis on the lesson content and prioritize aggregating content that elicits positive emotions. For example, the information aggregating unit performs sentiment analysis on the lesson content and identifies elements that elicit positive emotions. For example, it prioritizes aggregating episodes that interest students or parts that include humor. The information aggregating unit also automatically classifies content that elicits positive emotions based on the results of the sentiment analysis and stores it in a database. For example, it aggregates stories and success stories that motivate students. The information aggregating unit also calculates a sentiment score for the lesson content and builds a system that prioritizes displaying content with a high positive sentiment score. For example, it recommends video lectures and lecture notes with a high sentiment score. This makes it possible to prioritize aggregating content that elicits positive emotions and increase learners' motivation.

[0054] The information aggregator can automatically classify content and convert it into the optimal format based on the educator's field of expertise and teaching style. For example, the information aggregator registers the educator's field of expertise and teaching style in a database and automatically classifies lesson content based on that information. For example, science lesson content and humanities lesson content are stored separately. The information aggregator also builds a system that converts content into the optimal format based on the educator's teaching style (e.g., lecture format, discussion format). For example, lecture-style content is converted into slide format. The information aggregator also automatically tags lesson content according to the educator's field of expertise to make it easier to search. For example, physics lesson content is tagged with "physics." This makes it possible to provide the best learning experience for learners by optimizing content based on the educator's field of expertise and teaching style.

[0055] The information aggregator can analyze educators' past lesson evaluations and feedback, and prioritize the aggregation of highly rated lesson content. For example, the information aggregator can analyze educators' past lesson evaluation data and build a system that automatically extracts highly rated lesson content. For example, it can prioritize the aggregation of lesson videos that have received high ratings from students. The information aggregator can also perform text analysis of lesson feedback and prioritize the aggregation of lesson content that has received a lot of positive feedback. For example, it can analyze feedback that includes keywords such as "easy to understand" and "interesting." The information aggregator can also develop a system that automatically ranks highly rated lesson content and stores the top content in a database. For example, it can display content in order of highest evaluation score. This makes it possible to provide a high-quality learning experience by prioritizing the aggregation of highly rated lesson content.

[0056] In addition to lesson content, the information aggregation unit also aggregates the content of educator interviews and panel discussions, allowing for more multifaceted learning. For example, the information aggregation unit collects video and audio data of educator interviews and stores them in a database together with lesson content. For example, it aggregates interviews that include educators' educational philosophies and lesson innovations. The information aggregation unit also builds a system that converts the content of panel discussions into text and integrates it with lesson content. For example, it adds the content of discussions between educators to lesson notes. The information aggregation unit also analyzes the content of educator interviews and panel discussions and links them to related lesson content. For example, it links discussion content on a specific theme with related lesson videos. In this way, by aggregating the content of educator interviews and panel discussions, it is possible to provide more multifaceted learning.

[0057] The information aggregation unit can conduct comparative analyses of different educational systems and curricula and propose optimal teaching methods. For example, the information aggregation unit collects educational systems and curricula from different countries and regions and builds a system for comparative analyses. For example, it compares the mathematics education curricula of Japan and the United States. The information aggregation unit also develops a system that proposes optimal teaching methods based on the results of the comparative analyses. For example, it proposes incorporating the advantages of a particular educational system into other systems. The information aggregation unit also visualizes data from different educational systems and curricula, allowing educators to easily compare them. For example, it displays the differences in curricula using graphs and charts. This makes it possible to conduct comparative analyses of different educational systems and curricula and propose optimal teaching methods.

[0058] The information aggregation unit can use the emotion estimation function to analyze the emotions felt by educators during classes and aggregate content that is likely to resonate emotionally. For example, the information aggregation unit analyzes the emotions felt by educators during classes in real time, and builds a system that aggregates content that is likely to resonate emotionally based on that data. For example, it analyzes the educator's facial expressions and voice. The information aggregation unit also prioritizes and aggregates lesson content that educators felt positive emotions about based on the emotion estimation data. For example, it aggregates videos of lessons that educators enjoyed. The information aggregation unit also analyzes the educator's emotion data and develops a system that automatically classifies content that is likely to resonate emotionally. For example, it prioritizes and saves lesson notes and experiment data with high emotion scores. In this way, it is possible to aggregate content that is likely to resonate emotionally by analyzing the emotions felt by educators during classes.

[0059] The generation unit can analyze a user's learning history and interests to generate individually optimized learning content. For example, the generation unit analyzes a user's learning history and builds a system that generates individually optimized learning content based on the content learned in the past and interests. For example, it provides new content related to themes the user has previously studied. The generation unit also analyzes a user's interests and generates learning content based on those interests. For example, it provides lectures and experimental data related to topics that interest the user. The generation unit also integrates learning history and interest data to develop a system that generates individually optimized learning plans. For example, it suggests the next content to learn based on the user's learning progress. This makes it possible to improve learning efficiency by providing individually optimized learning content based on the user's learning history and interests.

[0060] The generation unit can collect user feedback on learning content in real time and improve the content based on that feedback. For example, the generation unit collects user feedback on learning content in real time and builds a system in which the generation AI improves the content based on that data. For example, it analyzes user comments and ratings. The generation unit also automatically updates the learning content based on user feedback. For example, it revises the lecture content to reflect user opinions. The generation unit also analyzes the feedback data collected in real time and develops a system in which the generation AI identifies areas for improvement in the content. For example, it makes improvements to resolve user dissatisfaction. This makes it possible to improve the learning experience by improving the learning content based on user feedback.

[0061] The generation unit can provide a positive learning experience by taking the user's emotions into consideration using the emotion estimation function. For example, the generation unit uses the emotion estimation function to analyze the user's emotions in real time and build a system that provides a positive learning experience based on that data. For example, it generates content that allows the user to learn while having fun. The generation unit also uses the generation AI to adjust the learning content based on the user's emotion data. For example, it provides content that helps the user relax when they are feeling stressed. The generation unit also analyzes the emotion estimation data and develops a system that preferentially generates learning content that evokes positive emotions in the user. For example, it recommends content with a high emotion score. This makes it possible to provide a positive learning experience by taking the user's emotions into consideration.

[0062] The generation unit can incorporate game elements into learning content to enhance its entertainment value. For example, the generation unit uses a generation AI to build a system that incorporates game elements into learning content. For example, it introduces quiz-style questions or a point system. The generation unit also adds game elements to the learning content to enhance its entertainment value. For example, it introduces a system that allows users to level up according to their learning progress. The generation unit also generates learning content that incorporates game elements, allowing users to learn while having fun. For example, it provides a simulation game based on the learning content. In this way, incorporating game elements into learning content can enhance its entertainment value and attract learners' interest.

[0063] The generation unit generates content that corresponds to different learning styles, thereby improving the user's learning efficiency. The generation unit, for example, uses generative AI to build a system that generates learning content that corresponds to visual, auditory, and tactile senses. For example, it provides video lectures, audio lectures, and interactive experimental data. The generation unit also generates content that corresponds to different learning styles, thereby improving the user's learning efficiency. For example, it provides content that makes extensive use of diagrams and graphs to visual learners. The generation unit also develops a system that automatically generates content according to learning styles, allowing users to learn in the optimal way. For example, it provides lectures in podcast format to auditory learners. In this way, it is possible to improve the user's learning efficiency by providing content that corresponds to different learning styles.

[0064] The generation unit can use the emotion estimation function to analyze the stress and fatigue felt by the user while studying and suggest appropriate breaks and refreshment methods. The generation unit, for example, uses the emotion estimation function to build a system that analyzes the stress and fatigue felt by the user while studying in real time. For example, it analyzes the user's facial expressions and voice. The generation unit also suggests appropriate breaks and refreshment methods based on the user's stress and fatigue data. For example, it recommends short breaks and relaxation exercises. The generation unit also analyzes the emotion estimation data to develop a system that enables the user to maintain an optimal learning pace. For example, it suggests taking a break when stress increases. In this way, learning efficiency can be maintained by analyzing the user's stress and fatigue and suggesting appropriate breaks and refreshment methods.

[0065] The translation unit takes into account the cultural background and nuances of the translated content, allowing it to provide more natural translations. For example, the translation unit uses generative AI to build a system that takes into account the cultural background and nuances of the translated content. For example, it appropriately translates expressions and phrases that are unique to a particular culture. Furthermore, to preserve the nuances of the translated content, the generative AI understands the context and provides an appropriate translation. For example, it translates jokes and metaphors naturally. Furthermore, to provide translations that take cultural background into account, the generative AI references a database of each country's culture and customs. For example, it translates using culturally appropriate examples and cases. This allows it to provide more natural translations by taking cultural background and nuances into account.

[0066] The translation unit analyzes the user's emotional response to the translated content and can provide a translation that is easy to empathize with emotionally. For example, the translation unit analyzes the user's emotional response to the translated content in real time and builds a system that provides an emotionally empathetic translation based on that data. For example, it analyzes the user's facial expressions and voice. The translation unit also prioritizes providing translations that the user has positive emotions about based on emotion estimation data. For example, it recommends translations with high emotional scores. The translation unit also analyzes the emotional response data of the translated content and develops a system that automatically adjusts the translation to be easy to empathize with emotionally. For example, it modifies the translation according to changes in the user's emotions. In this way, by analyzing the user's emotional response, it is possible to provide an emotionally empathetic translation.

[0067] The translation department can integrate the translated content into a platform where users of different languages ​​can study together. For example, the translation department builds a system that integrates translated content into a platform where users of different languages ​​can study together. For example, it allows English and Japanese users to study the same content. The translation department also develops a system that shares translated content in real time so that users of different languages ​​can study together. For example, it supports online discussions and collaborative work. The translation department also provides a platform where users of different languages ​​can work together on projects based on the translated content. For example, it supports collaborative research and group work. In this way, it is possible to promote international learning exchange by providing a platform where users of different languages ​​can study together.

[0068] The translation department may also provide the translated content in audio or video format to promote visual and auditory learning. For example, the translation department may build a system that provides translated content in audio format to promote visual and auditory learning. For example, the translation department may provide translated lectures as audio files. The translation department may also provide translated content in video format to enable users to learn visually. For example, the translation department may provide translated lecture videos with subtitles. The translation department may also develop a system that generates translated content in audio or video format in real time to enable users to access it instantly. For example, the translation department may provide a live translation function. This may promote visual and auditory learning by providing translated content in audio or video format.

[0069] The translation department can use the emotion estimation function to monitor the user's emotional reactions to translated content in real time and continuously search for the optimal translation method. The translation department, for example, uses the emotion estimation function to build a system that monitors the user's emotional reactions to translated content in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The translation department also develops a system that continuously searches for the optimal translation method based on the user's emotional reaction data. For example, it prioritizes the adoption of translation methods that result in a high number of positive emotional reactions. The translation department also builds a system that collects emotion estimation data in real time and uses it to improve translation methods. For example, it dynamically adjusts translations according to changes in the user's emotions. In this way, the optimal translation method can be continuously searched for by monitoring the user's emotional reactions in real time.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The information aggregator can automatically classify content and convert it into the optimal format based on the educator's field of expertise and teaching style. For example, the educator's field of expertise and teaching style are registered in a database, and lesson content is automatically classified based on that information. For example, science lesson content and humanities lesson content are stored separately. The information aggregator also builds a system that converts content into the optimal format based on the educator's teaching style (e.g., lecture format, discussion format). For example, lecture-style content is converted into slide format. The information aggregator also automatically tags lesson content according to the educator's field of expertise, making it easier to search. For example, physics lesson content is tagged with "physics." This makes it possible to provide the best learning experience for learners by optimizing content based on the educator's field of expertise and teaching style.

[0072] The information aggregator can perform sentiment analysis on the lesson content and prioritize aggregating content that elicits positive emotions. For example, sentiment analysis is performed on the lesson content to identify elements that elicit positive emotions. For example, parts that include humor or episodes that interest students are prioritized for aggregation. The information aggregator also automatically classifies content that elicits positive emotions based on the results of the sentiment analysis and stores it in a database. For example, it aggregates stories and success stories that motivate students. The information aggregator also calculates a sentiment score for the lesson content and builds a system that prioritizes displaying content with a high positive sentiment score. For example, it recommends video lectures and lecture notes with a high sentiment score. This makes it possible to prioritize aggregating content that elicits positive emotions and increase learner motivation.

[0073] The information aggregation unit can analyze educators' past lesson evaluations and feedback, and prioritize the aggregation of highly rated lesson content. For example, a system can be built that analyzes educators' past lesson evaluation data and automatically extracts highly rated lesson content. For example, lesson videos that have received high ratings from students can be prioritized and aggregated. The information aggregation unit can also perform text analysis of lesson feedback and prioritize the aggregation of lesson content that has received a lot of positive feedback. For example, it can analyze feedback that includes keywords such as "easy to understand" and "interesting." The information aggregation unit can also develop a system that automatically ranks highly rated lesson content and stores the top content in a database. For example, content can be displayed in descending order of evaluation score. This makes it possible to provide a high-quality learning experience by prioritizing the aggregation of highly rated lesson content.

[0074] In addition to lesson content, the information aggregation unit can also aggregate the content of educator interviews and panel discussions, allowing for more multifaceted learning. For example, it can collect video and audio data of educator interviews and store them in a database along with lesson content. For example, it can aggregate interviews that include educators' educational philosophies and innovative lesson ideas. The information aggregation unit can also convert the content of panel discussions into text and build a system that integrates it with lesson content. For example, it can add the content of discussions between educators to lesson notes. The information aggregation unit can also analyze the content of educator interviews and panel discussions and link them to related lesson content. For example, it can link discussion content on a specific theme with related lesson videos. In this way, by aggregating the content of educator interviews and panel discussions, it can provide more multifaceted learning.

[0075] The information aggregation unit can conduct comparative analyses of different educational systems and curricula and propose optimal teaching methods. For example, it can collect information on educational systems and curricula from different countries and regions and build a system for conducting comparative analyses. For example, it can compare the mathematics education curricula of Japan and the United States. The information aggregation unit can also develop a system that proposes optimal teaching methods based on the results of the comparative analyses. For example, it can propose incorporating the advantages of a particular educational system into other systems. The information aggregation unit can also visualize data on different educational systems and curricula, making it easier for educators to compare them. For example, it can display the differences in curricula using graphs and charts. This makes it possible to conduct comparative analyses of different educational systems and curricula and propose optimal teaching methods.

[0076] The information aggregation unit can use the emotion estimation function to analyze the emotions felt by educators during classes and aggregate content that is likely to resonate emotionally. For example, a system can be built that analyzes the emotions felt by educators during classes in real time and aggregates content that is likely to resonate emotionally based on that data. For example, it can analyze the educator's facial expressions and voice. The information aggregation unit also prioritizes and aggregates lesson content that educators felt positive emotions about based on the emotion estimation data. For example, it can aggregate videos of lessons that educators enjoyed. The information aggregation unit can also analyze educators' emotion data and develop a system that automatically classifies content that is likely to resonate emotionally. For example, it can prioritize and save lesson notes and experiment data with high emotion scores. In this way, content that is likely to resonate emotionally can be aggregated by analyzing the emotions felt by educators during classes.

[0077] The generation unit can analyze a user's learning history and interests to generate individually optimized learning content. For example, a system is constructed that analyzes a user's learning history and generates individually optimized learning content based on the content learned in the past and their interests. For example, new content related to themes the user has previously studied is provided. The generation unit also analyzes the user's interests and generates learning content based on those interests. For example, lectures and experimental data related to topics that interest the user are provided. The generation unit also develops a system that integrates learning history and interest data to generate individually optimized learning plans. For example, it suggests the next content to learn based on the user's learning progress. This makes it possible to improve learning efficiency by providing individually optimized learning content based on the user's learning history and interests.

[0078] The generation unit can collect user feedback on learning content in real time and improve the content based on that feedback. For example, a system can be built in which user feedback on learning content is collected in real time and the generation AI improves the content based on that data. For example, user comments and ratings can be analyzed. The generation unit also automatically updates the learning content based on user feedback. For example, the generation unit can revise the lecture content to reflect user opinions. The generation unit also analyzes the feedback data collected in real time and develops a system in which the generation AI identifies areas for improvement in the content. For example, improvements can be made to resolve user dissatisfaction. This makes it possible to improve the learning experience by improving the learning content based on user feedback.

[0079] The generation unit can use the emotion estimation function to take the user's emotions into consideration and provide a positive learning experience. For example, the emotion estimation function can be used to analyze the user's emotions in real time, and a system can be built to provide a positive learning experience based on that data. For example, content that allows the user to enjoy learning can be generated. The generation unit also uses the generation AI to adjust the learning content based on the user's emotion data. For example, content that helps the user relax when they are feeling stressed can be provided. The generation unit can also analyze the emotion estimation data and develop a system that preferentially generates learning content that evokes positive emotions in the user. For example, content with a high emotion score can be recommended. This makes it possible to provide a positive learning experience by taking the user's emotions into consideration.

[0080] The generation unit can incorporate game elements into learning content to increase its entertainment value. For example, a generation AI can be used to build a system that incorporates game elements into learning content. For example, quiz-style questions or a point system can be introduced. The generation unit can also add game elements to the learning content to increase its entertainment value. For example, a system can be introduced that allows users to level up according to their learning progress. The generation unit can also generate learning content that incorporates game elements, allowing users to learn while having fun. For example, it can provide a simulation game based on the learning content. In this way, incorporating game elements into learning content can increase its entertainment value and attract learners' interest.

[0081] The generation unit can use the emotion estimation function to analyze the stress and fatigue a user feels while studying and suggest appropriate breaks and refreshment methods. For example, a system is constructed that uses the emotion estimation function to analyze the stress and fatigue a user feels while studying in real time. For example, the user's facial expressions and voice are analyzed. The generation unit also suggests appropriate breaks and refreshment methods based on the user's stress and fatigue data. For example, it recommends short breaks and relaxation exercises. The generation unit also analyzes the emotion estimation data and develops a system that enables the user to maintain an optimal learning pace. For example, it suggests taking a break when stress increases. In this way, learning efficiency can be maintained by analyzing the user's stress and fatigue and suggesting appropriate breaks and refreshment methods.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The information aggregation unit centrally collects information on lesson content and knowledge from people in Japan and overseas, from elementary school teachers to university professors. For example, it collects lesson videos, lecture notes, experimental data, research papers, etc. The information aggregation unit also stores this information in a database and converts it into a format that is easy for the generative AI to analyze. Step 2: The generation unit generates learning content based on the information aggregated by the information aggregation unit. For example, when the generation AI receives a prompt requesting a lecture on a specific topic, it generates the optimal lecture content on that topic. The generation AI can also generate learning content tailored to the user's needs. For example, it generates learning content based on a prompt that includes instructions on what the user wants to learn. Step 3: The translation unit translates the learning content generated by the generation unit into multiple languages. For example, lesson content created in Japanese can be translated into English, French, etc. The translation unit can also use generative AI to take into account the cultural background and nuances of the translated content to provide a more natural translation.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] 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.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, a 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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. The information aggregation department centrally collects information on lesson content and wisdom from elementary school teachers and university professors in Japan and overseas, and a generation unit that generates study content based on the information aggregated by the information aggregation unit; a translation unit that translates the learning content generated by the generation unit into multiple languages. A system characterized by:

2. The information aggregation unit Sentiment analysis is performed on the lesson content, and content that elicits positive emotions is prioritized and aggregated.

2. The system of claim 1.

3. The information aggregation unit In addition to the above-mentioned lesson content, we will also compile interviews with educators and panel discussions to provide a more multifaceted learning experience.

2. The system of claim 1.

4. The generation unit Analyzing the user's learning history and interests and generating individually optimized learning content 2. The system of claim 1.

5. The generation unit Incorporating game elements into the learning content to enhance its entertainment value 2. The system of claim 1.

6. The translation unit Considering the cultural context and nuances of the translated content to provide a more natural translation 2. The system of claim 1.

7. The translation unit Monitor users' emotional reactions to the translated content in real time and continuously find the best translation method.

2. The system of claim 1.

8. The information aggregation unit Analyzing the emotions felt by educators during lessons and aggregating content that resonates with them emotionally 2. The system of claim 1.

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