system

Abstract

The system according to the embodiment aims to generate and provide interactive learning content by utilizing data from educational institutions. [Solution] According to an embodiment, the system includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data from educational institutions. The analysis unit analyzes the data collected by the collection unit. The generation unit generates interactive learning content based on the data analyzed by the analysis unit. The provision unit provides the content generated by the generation unit.

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] Existing technologies have not yet effectively utilized educational institution data to generate interactive learning content, leaving room for improvement.

[0005] The system according to the embodiment aims to generate and provide interactive learning content by utilizing data from educational institutions. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data from educational institutions. The analysis unit analyzes the data collected by the collection unit. The generation unit generates interactive learning content based on the data analyzed by the analysis unit. The provision unit provides the content generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate and provide interactive learning content by utilizing data from educational institutions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A digital textbook system according to an embodiment of the present invention collects, analyzes, generates, and provides data from educational institutions. This digital textbook system uses a data integration platform to integrate data from textbook publishers, museums, universities, and other sources, and creates textbooks that allow children to learn while immersed in the content. For example, the digital textbook system uses the data integration platform to collect data from textbook publishers, museums, universities, and other sources. This data is integrated and collected from each educational institution. For example, this data may include data on museum exhibits and university research results. Next, the digital textbook system analyzes the collected data. An analysis unit analyzes the collected data and extracts information that will interest children. For example, this may include detailed information about museum exhibits and new knowledge based on university research results. Furthermore, the digital textbook system generates interactive learning content based on the analysis results. A generation unit generates content that children can enjoy based on the analyzed data. For example, this may include interactive quizzes, games, videos, and the like. Finally, the digital textbook system provides the generated content to children. A provision unit provides the generated content to children to enhance their learning. For example, this may include content provided as a digital textbook or content provided through an online platform. This allows the digital textbook system to provide content that interests children and improve the effectiveness of their learning.

[0029] A digital textbook system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data on educational institutions. Examples of the data on educational institutions include, but are not limited to, textbook content from textbook publishers, museum exhibition information, and university research data. For example, the collection unit collects content data on textbooks provided by textbook publishers. The collection unit can also collect data on museum exhibits. The collection unit can also collect data on university research results. For example, the collection unit acquires textbook content from a textbook publisher's database, exhibit information from a museum database, and research results from a university database. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data using data mining technology. The analysis unit can also analyze the data using statistical analysis technology. The analysis unit can also analyze the data using a machine learning algorithm. For example, the analysis unit uses data mining technology to extract information of interest to children from the collected data. The statistical analysis technology is used to analyze trends in the data and identify topics of interest to children. The data is classified using a machine learning algorithm and information that interests children is extracted. The generation unit generates interactive learning content based on the data analyzed by the analysis unit. The generation unit generates, for example, an interactive quiz. The generation unit can also generate interactive games. The generation unit can also generate educational videos. For example, the generation unit generates multiple-choice quizzes based on the analyzed data. The generation unit generates interactive games to enable children to learn while having fun. The generation unit generates educational videos to enable children to learn visually. The provision unit provides the content generated by the generation unit. The provision unit provides the content as, for example, a digital textbook. The provision unit can also provide the content through an online platform. The provision unit can also provide the content through a mobile app. For example, the provision unit provides the generated content as a digital textbook to enable children to access it.The generated content is provided through an online platform, allowing children to study via the internet. The generated content is provided through a mobile app, allowing children to study using a smartphone or tablet. As a result, the digital textbook system according to the embodiment can provide content that interests children and improve the effectiveness of their learning.

[0030] The collection unit can collect data from textbook companies, museums, and universities. For example, the collection unit collects content data of textbooks provided by textbook companies. For example, the collection unit acquires the content of textbooks from the textbook company's database. The collection unit can also collect data on museum exhibits. For example, the collection unit acquires information on exhibits from the museum's database. The collection unit can also collect data on university research results. For example, the collection unit acquires research results from the university's database. This allows data to be collected from a variety of educational institutions, making it possible to provide a wealth of content. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit may cause AI to execute a process of acquiring data from the databases of textbook companies, museums, and universities.

[0031] The analysis unit can analyze the collected data and extract information that interests children. The analysis unit can analyze the collected data using, for example, data mining technology. For example, the analysis unit can extract information that interests children from the collected data using data mining technology. The analysis unit can also analyze the data using statistical analysis technology. For example, the analysis unit can use statistical analysis technology to analyze data trends and identify topics that interest children. The analysis unit can also analyze the data using a machine learning algorithm. For example, the analysis unit can use a machine learning algorithm to classify the data and extract information that interests children. This extraction of information that interests children can improve the effectiveness of learning. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected data into AI and have the AI ​​extract information that interests children.

[0032] The generation unit can generate interactive quiz, game, and video content based on the analyzed data. The generation unit, for example, generates an interactive quiz. For example, the generation unit generates a multiple-choice quiz based on the analyzed data. The generation unit can also generate interactive games. For example, the generation unit generates an interactive game to enable children to learn while having fun. The generation unit can also generate educational videos. For example, the generation unit generates educational videos to enable children to learn visually. In this way, by generating interactive content, children can learn while having fun. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the analyzed data into AI and cause the AI ​​to generate interactive quizzes, games, and videos.

[0033] The providing unit can provide the generated content through a digital textbook or an online platform. The providing unit, for example, provides the content as a digital textbook. For example, the providing unit can provide the generated content as a digital textbook so that children can access it. The providing unit can also provide the content through an online platform. For example, the providing unit can provide the generated content through an online platform so that children can study via the Internet. The providing unit can also provide the content through a mobile app. For example, the providing unit can provide the generated content through a mobile app so that children can study using a smartphone or tablet. This makes it easier for children to access the generated content by providing it through a digital textbook or an online platform. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the generated content into AI and cause the AI ​​to execute a process of providing the content through a digital textbook or an online platform.

[0034] The collection unit can analyze the data provision frequency of the educational institution and set a collection schedule. The collection unit, for example, analyzes the data provision frequency of the educational institution on a weekly basis and sets an optimal collection schedule. For example, the collection unit analyzes the data provision frequency of the educational institution based on past provision history and sets a weekly collection schedule. The collection unit can also analyze the data provision frequency of the educational institution on a monthly basis and optimize the timing of data updates. For example, the collection unit analyzes the data provision frequency of the educational institution on a monthly basis and optimize the timing of data updates. The collection unit can also analyze the data provision frequency of the educational institution on a yearly basis and set a long-term collection schedule. For example, the collection unit analyzes the data provision frequency of the educational institution on a yearly basis and sets a long-term collection schedule. This enables efficient data collection by analyzing the data provision frequency. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause AI to analyze the data provision frequency of the educational institution and set an optimal collection schedule.

[0035] The collection unit can evaluate the reliability of data at the time of collection and prioritize collecting reliable data. The collection unit, for example, evaluates the source of the data and prioritizes collecting data from reliable educational institutions. For example, the collection unit evaluates the reliability of educational institutions based on past data provision history and prioritizes collecting data from reliable educational institutions. The collection unit can also evaluate the frequency of data updates and prioritize collecting the latest data. For example, the collection unit evaluates the frequency of data updates and prioritizes collecting the latest data. Furthermore, the collection unit can evaluate the content of the data and prioritize collecting data with high accuracy. For example, the collection unit evaluates the content of the data and prioritizes collecting data with high accuracy. This enables high-quality content to be provided by preferentially collecting reliable data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can have AI perform a data reliability evaluation and have AI prioritize collecting reliable data.

[0036] The collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the educational institutions during collection. The collection unit, for example, prioritizes collecting data from geographically nearby educational institutions. For example, the collection unit prioritizes collecting data from geographically nearby educational institutions based on the geographical location information of the educational institutions. The collection unit can also prioritize collecting data from geographically relevant areas. For example, the collection unit prioritizes collecting data from geographically relevant areas. Furthermore, the collection unit can also prioritize collecting data related to a geographically specific theme. For example, the collection unit prioritizes collecting data related to a geographically specific theme. In this way, highly relevant data can be collected by taking the geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the educational institutions into AI and cause the AI ​​to prioritize the collection of highly relevant data.

[0037] The collection unit can analyze the social media activity of the educational institution at the time of collection and collect related data. For example, the collection unit can analyze the social media activity of the educational institution and collect data related to the latest trends. For example, the collection unit can analyze the social media activity of the educational institution and collect data related to the latest trends. The collection unit can also analyze the social media activity of the educational institution and collect data related to popular content. For example, the collection unit can analyze the social media activity of the educational institution and collect data related to popular content. Furthermore, the collection unit can analyze the social media activity of the educational institution and collect data of high user interest. For example, the collection unit can analyze the social media activity of the educational institution and collect data of high user interest. In this way, data related to the latest trends can be collected by analyzing the social media activity. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can cause AI to analyze the social media activity of the educational institution and collect related data.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data of high importance. For example, the analysis unit performs a detailed analysis on data of high importance based on the influence and frequency of use of the data. The analysis unit can also perform a simplified analysis on data of low importance. For example, the analysis unit performs a simplified analysis on data of low importance based on the novelty and importance of the data. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on data of medium importance. For example, the analysis unit performs an analysis with an appropriate level of detail on data of medium importance based on the frequency of use and influence of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have AI perform an evaluation of the importance of the data and adjust the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a statistical analysis algorithm to scientific data. For example, the analysis unit applies a statistical analysis algorithm to scientific data to analyze trends and patterns in the data. The analysis unit can also apply a time series analysis algorithm to historical data. For example, the analysis unit applies a time series analysis algorithm to historical data to analyze data transitions and trends. The analysis unit can also apply a pattern recognition algorithm to artistic data. For example, the analysis unit applies a pattern recognition algorithm to artistic data to analyze data characteristics and patterns. This enables highly accurate analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can cause AI to apply an analysis algorithm depending on the data category.

[0040] The analysis unit can determine the analysis priority based on the time when the data was collected during analysis. The analysis unit, for example, prioritizes analyzing the most recent data. For example, the analysis unit evaluates the time when the data was collected and prioritizes analyzing the most recent data. The analysis unit can also postpone analyzing older data. For example, the analysis unit evaluates the time when the data was collected and prioritizes analyzing older data. The analysis unit can also prioritize analyzing data collected during a specific period. For example, the analysis unit prioritizes analyzing data collected during a specific period. This enables efficient analysis by determining the analysis priority based on the time when the data was collected. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have AI evaluate the time when the data was collected and have AI determine the analysis priority.

[0041] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit evaluates correlations between data and prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit evaluates correlations between data and prioritizes analysis of less relevant data. Furthermore, the analysis unit can also prioritize analysis of data related to a specific theme. For example, the analysis unit prioritizes analysis of data related to a specific theme. This enables efficient analysis by adjusting the order of analysis based on the relevance of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have AI perform a data relevance evaluation and adjust the order of analysis.

[0042] The generation unit can adjust the level of detail of the generation based on the importance of the content during generation. For example, the generation unit generates detailed content for highly important content. For example, the generation unit generates detailed content for highly important content based on the educational effect of the content and the user's interest. The generation unit can also generate simplified content for low importance. For example, the generation unit generates simplified content for low importance based on the novelty and importance of the content. Furthermore, the generation unit can generate content with an appropriate level of detail for content with medium importance. For example, the generation unit generates content with an appropriate level of detail for content with medium importance based on the frequency of use and influence of the content. This enables efficient content generation by adjusting the level of detail of the generation based on the importance of the content. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI. For example, the generation unit can have AI evaluate the importance of the content and adjust the level of detail of the generation.

[0043] The generation unit can apply different generation algorithms depending on the content category during generation. For example, the generation unit applies a statistical generation algorithm to science content. For example, the generation unit applies a statistical generation algorithm to science content to generate data trends and patterns. The generation unit can also apply a time series generation algorithm to historical content. For example, the generation unit applies a time series generation algorithm to historical content to generate data transitions and trends. The generation unit can also apply a pattern recognition generation algorithm to artistic content. For example, the generation unit applies a pattern recognition generation algorithm to artistic content to generate data features and patterns. This enables highly accurate content generation by applying an appropriate generation algorithm depending on the content category. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause AI to apply a generation algorithm depending on the content category.

[0044] The generation unit can determine generation priorities based on the time when content was collected during generation. The generation unit, for example, prioritizes generating content based on the latest data. For example, the generation unit evaluates the time when data was collected and prioritizes generating content based on the latest data. The generation unit can also postpone generating content based on older data. For example, the generation unit evaluates the time when data was collected and prioritizes generating content based on older data. The generation unit can also prioritize generating content based on data collected during a specific period. For example, the generation unit prioritizes generating content based on data collected during a specific period. This enables efficient content generation by determining generation priorities based on the time when content was collected. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can have AI evaluate the time when data was collected and have AI determine the generation priorities.

[0045] The generation unit can adjust the order of generation based on the relevance of the content during generation. The generation unit, for example, prioritizes generating content based on highly relevant data. For example, the generation unit evaluates correlations between data and prioritizes generating content based on highly relevant data. The generation unit can also postpone generating content based on less relevant data. For example, the generation unit evaluates correlations between data and prioritizes generating content based on less relevant data. The generation unit can also prioritize generating content based on data related to a specific theme. For example, the generation unit prioritizes generating content based on data related to a specific theme. This enables efficient content generation by adjusting the order of generation based on the relevance of the content. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can have AI perform data relevance evaluation and adjust the order of generation.

[0046] The providing unit can select the provision method by referring to the user's past learning history when providing the information. The providing unit, for example, analyzes the user's past learning history and selects the most effective provision method. For example, the providing unit selects the most effective provision method based on the user's past learning history. The providing unit can also select a preferred provision method from the user's past learning history. For example, the providing unit selects a preferred provision method based on the user's past learning history. Furthermore, the providing unit can also select a provision method with a high learning effect based on the user's past learning history. For example, the providing unit selects a provision method with a high learning effect based on the user's past learning history. In this way, the optimal provision method can be selected by referring to the user's past learning history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can cause AI to analyze the user's past learning history and select a provision method.

[0047] The providing unit can customize the means of provision based on the user's current learning situation at the time of provision. The providing unit, for example, analyzes the user's current learning situation and customizes the optimal means of provision. For example, the providing unit customizes the optimal means of provision based on the user's current learning situation. The providing unit can also customize an effective means of provision based on the user's current learning situation. For example, the providing unit customizes an effective means of provision based on the user's current learning situation. Furthermore, the providing unit can also customize a means of provision with high learning effectiveness based on the user's current learning situation. For example, the providing unit customizes a means of provision with high learning effectiveness based on the user's current learning situation. This enables more effective content provision by customizing the means of provision based on the user's current learning situation. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can cause AI to analyze the user's current learning situation and customize the means of provision.

[0048] The providing unit can select the optimal delivery method by taking into account the user's geographical location information at the time of delivery. The providing unit, for example, provides content related to a region based on the user's geographical location information. For example, the providing unit provides content related to a region based on the user's geographical location information. The providing unit can also provide content in an optimal language based on the user's geographical location information. For example, the providing unit provides content in an optimal language based on the user's geographical location information. The providing unit can also provide content related to the culture or history of a region based on the user's geographical location information. For example, the providing unit provides content related to the culture or history of a region based on the user's geographical location information. This makes it possible to select the optimal delivery method by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into AI and cause the AI ​​to select the optimal delivery method.

[0049] The providing unit can analyze the user's social media activity at the time of providing and suggest a means of provision. The providing unit, for example, analyzes the user's social media activity and suggests content that the user is likely to be interested in. For example, the providing unit analyzes the user's social media activity and suggests content that the user is likely to be interested in. The providing unit can also analyze the user's social media activity and suggest content shared by friends. For example, the providing unit analyzes the user's social media activity and suggests content shared by friends. The providing unit can also analyze the user's social media activity and suggest content related to trends. For example, the providing unit analyzes the user's social media activity and suggests content related to trends. In this way, content that the user is likely to be interested in can be suggested by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can cause AI to analyze the user's social media activity and suggest a means of provision.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The collection unit can also analyze the user's learning history and collect new data based on what the user has learned in the past. For example, the collection unit can analyze topics that the user has studied in the past and collect related new data. The collection unit can also identify topics that the user has struggled with in the past and collect supplementary data related to those topics. Furthermore, the collection unit can prioritize collecting topics that the user has shown interest in in the past. This allows for data collection based on the user's learning history, making it possible to provide a more personalized learning experience.

[0052] The generation unit can also monitor the user's learning progress in real time and generate content according to the progress. For example, if the user is struggling with a particular topic, the generation unit can generate supplemental content related to that topic. Also, if the user is making good progress in their learning, the generation unit can generate content for the next level. Furthermore, the generation unit can generate review content for topics that the user has completed learning. This makes it possible to maximize the learning effect by providing content according to the user's learning progress.

[0053] The providing unit can also detect the user's learning environment and select the optimal content providing method. For example, if the user is in a quiet environment, the providing unit can provide content including audio commentary. If the user is on the move, the providing unit can also provide visual content. Furthermore, if the user is learning in a group, the providing unit can also provide collaborative content. This makes it possible to improve learning effectiveness by selecting a content providing method that suits the user's learning environment.

[0054] The generation unit can also analyze the user's learning history and generate new content based on what the user has learned in the past. For example, the generation unit can analyze topics that the user has previously studied and generate new related content. The generation unit can also identify topics that the user has previously struggled with and generate supplemental content related to those topics. Furthermore, the generation unit can prioritize generating topics in which the user has previously shown interest. This allows for the provision of a more personalized learning experience by generating content based on the user's learning history.

[0055] The collection unit can also analyze the data provision frequency of the educational institution and dynamically adjust the collection schedule. For example, the collection unit can monitor the data provision frequency of the educational institution in real time and dynamically set an optimal collection schedule. The collection unit can also automatically adjust the collection schedule in response to fluctuations in the data provision frequency of the educational institution. Furthermore, the collection unit can set a future collection schedule based on a prediction of the data provision frequency of the educational institution. This enables efficient data collection by dynamically analyzing the data provision frequency.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The collection department collects data from educational institutions. Data from educational institutions includes textbook content from textbook companies, exhibit information from museums, and research data from universities. For example, the collection department obtains textbook content from textbook companies' databases, exhibit information from museum databases, and research results from university databases. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using data mining techniques, statistical analysis techniques, and machine learning algorithms, for example, to extract information that children are interested in, analyze data trends, and identify topics. Step 3: The generator generates interactive learning content based on the data analyzed by the analyzer. The generator generates interactive quizzes, games, educational videos, etc., allowing children to learn while having fun. Step 4: The provider provides the content generated by the generator. The provider provides the content through digital textbooks, online platforms, and mobile apps, making it accessible to children.

[0058] (Example 2) A digital textbook system according to an embodiment of the present invention collects, analyzes, generates, and provides data from educational institutions. This digital textbook system uses a data integration platform to integrate data from textbook publishers, museums, universities, and other sources, and creates textbooks that allow children to learn while immersed in the content. For example, the digital textbook system uses the data integration platform to collect data from textbook publishers, museums, universities, and other sources. This data is integrated and collected from each educational institution. For example, this data may include data on museum exhibits and university research results. Next, the digital textbook system analyzes the collected data. An analysis unit analyzes the collected data and extracts information that will interest children. For example, this may include detailed information about museum exhibits and new knowledge based on university research results. Furthermore, the digital textbook system generates interactive learning content based on the analysis results. A generation unit generates content that children can enjoy based on the analyzed data. For example, this may include interactive quizzes, games, videos, and the like. Finally, the digital textbook system provides the generated content to children. A provision unit provides the generated content to children to enhance their learning. For example, this may include content provided as a digital textbook or content provided through an online platform. This allows the digital textbook system to provide content that interests children and improve the effectiveness of their learning.

[0059] A digital textbook system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data on educational institutions. Examples of the data on educational institutions include, but are not limited to, textbook content from textbook publishers, museum exhibition information, and university research data. For example, the collection unit collects content data on textbooks provided by textbook publishers. The collection unit can also collect data on museum exhibits. The collection unit can also collect data on university research results. For example, the collection unit acquires textbook content from a textbook publisher's database, exhibit information from a museum database, and research results from a university database. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data using data mining technology. The analysis unit can also analyze the data using statistical analysis technology. The analysis unit can also analyze the data using a machine learning algorithm. For example, the analysis unit uses data mining technology to extract information of interest to children from the collected data. The statistical analysis technology is used to analyze trends in the data and identify topics of interest to children. The data is classified using a machine learning algorithm and information that interests children is extracted. The generation unit generates interactive learning content based on the data analyzed by the analysis unit. The generation unit generates, for example, an interactive quiz. The generation unit can also generate interactive games. The generation unit can also generate educational videos. For example, the generation unit generates multiple-choice quizzes based on the analyzed data. The generation unit generates interactive games to enable children to learn while having fun. The generation unit generates educational videos to enable children to learn visually. The provision unit provides the content generated by the generation unit. The provision unit provides the content as, for example, a digital textbook. The provision unit can also provide the content through an online platform. The provision unit can also provide the content through a mobile app. For example, the provision unit provides the generated content as a digital textbook to enable children to access it.The generated content is provided through an online platform, allowing children to study via the internet. The generated content is provided through a mobile app, allowing children to study using a smartphone or tablet. As a result, the digital textbook system according to the embodiment can provide content that interests children and improve the effectiveness of their learning.

[0060] The collection unit can collect data from textbook companies, museums, and universities. For example, the collection unit collects content data of textbooks provided by textbook companies. For example, the collection unit acquires the content of textbooks from the textbook company's database. The collection unit can also collect data on museum exhibits. For example, the collection unit acquires information on exhibits from the museum's database. The collection unit can also collect data on university research results. For example, the collection unit acquires research results from the university's database. This allows data to be collected from a variety of educational institutions, making it possible to provide a wealth of content. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit may cause AI to execute a process of acquiring data from the databases of textbook companies, museums, and universities.

[0061] The analysis unit can analyze the collected data and extract information that interests children. The analysis unit can analyze the collected data using, for example, data mining technology. For example, the analysis unit can extract information that interests children from the collected data using data mining technology. The analysis unit can also analyze the data using statistical analysis technology. For example, the analysis unit can use statistical analysis technology to analyze data trends and identify topics that interest children. The analysis unit can also analyze the data using a machine learning algorithm. For example, the analysis unit can use a machine learning algorithm to classify the data and extract information that interests children. This extraction of information that interests children can improve the effectiveness of learning. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected data into AI and have the AI ​​extract information that interests children.

[0062] The generation unit can generate interactive quiz, game, and video content based on the analyzed data. The generation unit, for example, generates an interactive quiz. For example, the generation unit generates a multiple-choice quiz based on the analyzed data. The generation unit can also generate interactive games. For example, the generation unit generates an interactive game to enable children to learn while having fun. The generation unit can also generate educational videos. For example, the generation unit generates educational videos to enable children to learn visually. In this way, by generating interactive content, children can learn while having fun. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the analyzed data into AI and cause the AI ​​to generate interactive quizzes, games, and videos.

[0063] The providing unit can provide the generated content through a digital textbook or an online platform. The providing unit, for example, provides the content as a digital textbook. For example, the providing unit can provide the generated content as a digital textbook so that children can access it. The providing unit can also provide the content through an online platform. For example, the providing unit can provide the generated content through an online platform so that children can study via the Internet. The providing unit can also provide the content through a mobile app. For example, the providing unit can provide the generated content through a mobile app so that children can study using a smartphone or tablet. This makes it easier for children to access the generated content by providing it through a digital textbook or an online platform. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the generated content into AI and cause the AI ​​to execute a process of providing the content through a digital textbook or an online platform.

[0064] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, when the user is excited, the collection unit collects data in real time and immediately reflects the data. For example, the collection unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, when the user is relaxed, the collection unit can collect data at regular intervals and provide stable updates. For example, the collection unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, when the user is stressed, the collection unit can reduce the frequency of data collection to reduce the user's burden. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows for more effective data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data into AI and have the AI ​​adjust the timing of data collection.

[0065] The collection unit can analyze the data provision frequency of the educational institution and set a collection schedule. The collection unit, for example, analyzes the data provision frequency of the educational institution on a weekly basis and sets an optimal collection schedule. For example, the collection unit analyzes the data provision frequency of the educational institution based on past provision history and sets a weekly collection schedule. The collection unit can also analyze the data provision frequency of the educational institution on a monthly basis and optimize the timing of data updates. For example, the collection unit analyzes the data provision frequency of the educational institution on a monthly basis and optimize the timing of data updates. The collection unit can also analyze the data provision frequency of the educational institution on a yearly basis and set a long-term collection schedule. For example, the collection unit analyzes the data provision frequency of the educational institution on a yearly basis and sets a long-term collection schedule. This enables efficient data collection by analyzing the data provision frequency. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause AI to analyze the data provision frequency of the educational institution and set an optimal collection schedule.

[0066] The collection unit can evaluate the reliability of data at the time of collection and prioritize collecting reliable data. The collection unit, for example, evaluates the source of the data and prioritizes collecting data from reliable educational institutions. For example, the collection unit evaluates the reliability of educational institutions based on past data provision history and prioritizes collecting data from reliable educational institutions. The collection unit can also evaluate the frequency of data updates and prioritize collecting the latest data. For example, the collection unit evaluates the frequency of data updates and prioritizes collecting the latest data. Furthermore, the collection unit can evaluate the content of the data and prioritize collecting data with high accuracy. For example, the collection unit evaluates the content of the data and prioritizes collecting data with high accuracy. This enables high-quality content to be provided by preferentially collecting reliable data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can have AI perform a data reliability evaluation and have AI prioritize collecting reliable data.

[0067] The collection unit can estimate the user's emotions and prioritize data to be collected based on the estimated user emotions. For example, if the user is excited, the collection unit prioritizes collecting data with a high entertainment value. For example, the collection unit captures the user's facial expressions with a camera, estimates the user's emotions using an emotion estimation algorithm, and prioritizes collecting data with a high entertainment value. Furthermore, if the user is relaxed, the collection unit can prioritize collecting data with a high educational value. For example, the collection unit records the user's voice, estimates the user's emotions using voice analysis technology, and prioritizes collecting data with a high educational value. Furthermore, if the user is stressed, the collection unit can prioritize collecting data with a relaxing effect. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor, estimates the user's emotions using an emotion estimation algorithm, and prioritizes collecting data with a relaxing effect. This enables more effective data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit may input user emotion data into AI and have the AI ​​determine the priority of the data.

[0068] The collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the educational institutions during collection. The collection unit, for example, prioritizes collecting data from geographically nearby educational institutions. For example, the collection unit prioritizes collecting data from geographically nearby educational institutions based on the geographical location information of the educational institutions. The collection unit can also prioritize collecting data from geographically relevant areas. For example, the collection unit prioritizes collecting data from geographically relevant areas. Furthermore, the collection unit can also prioritize collecting data related to a geographically specific theme. For example, the collection unit prioritizes collecting data related to a geographically specific theme. In this way, highly relevant data can be collected by taking the geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the educational institutions into AI and cause the AI ​​to prioritize the collection of highly relevant data.

[0069] The collection unit can analyze the social media activity of the educational institution at the time of collection and collect related data. For example, the collection unit can analyze the social media activity of the educational institution and collect data related to the latest trends. For example, the collection unit can analyze the social media activity of the educational institution and collect data related to the latest trends. The collection unit can also analyze the social media activity of the educational institution and collect data related to popular content. For example, the collection unit can analyze the social media activity of the educational institution and collect data related to popular content. Furthermore, the collection unit can analyze the social media activity of the educational institution and collect data of high user interest. For example, the collection unit can analyze the social media activity of the educational institution and collect data of high user interest. In this way, data related to the latest trends can be collected by analyzing the social media activity. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can cause AI to analyze the social media activity of the educational institution and collect related data.

[0070] The analysis unit can estimate the user's emotions and adjust the analysis expression method based on the estimated user emotions. For example, if the user is excited, the analysis unit uses a visually stimulating expression method. For example, the analysis unit captures the user's facial expression with a camera, estimates the user's emotions using an emotion estimation algorithm, and uses a visually stimulating expression method. The analysis unit can also use a calm expression method if the user is relaxed. For example, the analysis unit can record the user's voice, estimate the user's emotions using voice analysis technology, and use a calm expression method. Furthermore, if the user is feeling stressed, the analysis unit can use a simple, highly visible expression method. For example, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the user's emotions using an emotion estimation algorithm, and use a simple, highly visible expression method. This enables more effective analysis by adjusting the analysis expression method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into the AI ​​and have the AI ​​adjust the method of expression of the analysis.

[0071] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data of high importance. For example, the analysis unit performs a detailed analysis on data of high importance based on the influence and frequency of use of the data. The analysis unit can also perform a simplified analysis on data of low importance. For example, the analysis unit performs a simplified analysis on data of low importance based on the novelty and importance of the data. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on data of medium importance. For example, the analysis unit performs an analysis with an appropriate level of detail on data of medium importance based on the frequency of use and influence of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have AI perform an evaluation of the importance of the data and adjust the level of detail of the analysis.

[0072] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a statistical analysis algorithm to scientific data. For example, the analysis unit applies a statistical analysis algorithm to scientific data to analyze trends and patterns in the data. The analysis unit can also apply a time series analysis algorithm to historical data. For example, the analysis unit applies a time series analysis algorithm to historical data to analyze data transitions and trends. The analysis unit can also apply a pattern recognition algorithm to artistic data. For example, the analysis unit applies a pattern recognition algorithm to artistic data to analyze data characteristics and patterns. This enables highly accurate analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can cause AI to apply an analysis algorithm depending on the data category.

[0073] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit performs a short and concise analysis. For example, the analysis unit captures the user's facial expressions with a camera, estimates the user's emotions using an emotion estimation algorithm, and performs a short and concise analysis. The analysis unit can also perform a detailed analysis if the user is relaxed. For example, the analysis unit records the user's voice, estimates the user's emotions using voice analysis technology, and performs a detailed analysis. Furthermore, the analysis unit can perform a visually stimulating analysis if the user is excited. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor, estimates the user's emotions using an emotion estimation algorithm, and performs a visually stimulating analysis. This allows for more effective analysis by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, or without, an AI. For example, the analysis unit may input user emotion data into the AI ​​and have the AI ​​adjust the length of the analysis.

[0074] The analysis unit can determine the analysis priority based on the time when the data was collected during analysis. The analysis unit, for example, prioritizes analyzing the most recent data. For example, the analysis unit evaluates the time when the data was collected and prioritizes analyzing the most recent data. The analysis unit can also postpone analyzing older data. For example, the analysis unit evaluates the time when the data was collected and prioritizes analyzing older data. The analysis unit can also prioritize analyzing data collected during a specific period. For example, the analysis unit prioritizes analyzing data collected during a specific period. This enables efficient analysis by determining the analysis priority based on the time when the data was collected. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have AI evaluate the time when the data was collected and have AI determine the analysis priority.

[0075] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit evaluates correlations between data and prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit evaluates correlations between data and prioritizes analysis of less relevant data. Furthermore, the analysis unit can also prioritize analysis of data related to a specific theme. For example, the analysis unit prioritizes analysis of data related to a specific theme. This enables efficient analysis by adjusting the order of analysis based on the relevance of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have AI perform a data relevance evaluation and adjust the order of analysis.

[0076] The generation unit can estimate the user's emotions and adjust the expression method of the generated content based on the estimated user emotions. For example, if the user is excited, the generation unit uses a visually stimulating expression method. For example, the generation unit captures the user's facial expression with a camera, estimates the user's emotions using an emotion estimation algorithm, and uses a visually stimulating expression method. The generation unit can also use a calm expression method if the user is relaxed. For example, the generation unit can record the user's voice, estimate the user's emotions using voice analysis technology, and use a calm expression method. Furthermore, if the user is feeling stressed, the generation unit can use a simple, highly visible expression method. For example, the generation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the user's emotions using an emotion estimation algorithm, and use a simple, highly visible expression method. This enables more effective content generation by adjusting the expression method of the content according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit may input user emotion data into the AI ​​and cause the AI ​​to adjust the way the content is expressed.

[0077] The generation unit can adjust the level of detail of the generation based on the importance of the content during generation. For example, the generation unit generates detailed content for highly important content. For example, the generation unit generates detailed content for highly important content based on the educational effect of the content and the user's interest. The generation unit can also generate simplified content for low importance. For example, the generation unit generates simplified content for low importance based on the novelty and importance of the content. Furthermore, the generation unit can generate content with an appropriate level of detail for content with medium importance. For example, the generation unit generates content with an appropriate level of detail for content with medium importance based on the frequency of use and influence of the content. This enables efficient content generation by adjusting the level of detail of the generation based on the importance of the content. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI. For example, the generation unit can have AI evaluate the importance of the content and adjust the level of detail of the generation.

[0078] The generation unit can apply different generation algorithms depending on the content category during generation. For example, the generation unit applies a statistical generation algorithm to science content. For example, the generation unit applies a statistical generation algorithm to science content to generate data trends and patterns. The generation unit can also apply a time series generation algorithm to historical content. For example, the generation unit applies a time series generation algorithm to historical content to generate data transitions and trends. The generation unit can also apply a pattern recognition generation algorithm to artistic content. For example, the generation unit applies a pattern recognition generation algorithm to artistic content to generate data features and patterns. This enables highly accurate content generation by applying an appropriate generation algorithm depending on the content category. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause AI to apply a generation algorithm depending on the content category.

[0079] The generation unit can estimate the user's emotions and adjust the length of the generated content based on the estimated user emotions. For example, if the user is in a hurry, the generation unit generates short, to-the-point content. For example, the generation unit captures the user's facial expression with a camera, estimates the user's emotions using an emotion estimation algorithm, and generates short, to-the-point content. Furthermore, if the user is relaxed, the generation unit can generate longer content with detailed explanations. For example, the generation unit records the user's voice, estimates the user's emotions using voice analysis technology, and generates longer content with detailed explanations. Furthermore, if the user is excited, the generation unit can generate content with visually stimulating effects. For example, the generation unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor, estimates the user's emotions using an emotion estimation algorithm, and generates content with visually stimulating effects. This enables more effective content generation by adjusting the length of the content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit may input user emotion data into the AI ​​and cause the AI ​​to adjust the length of the content.

[0080] The generation unit can determine generation priorities based on the time when content was collected during generation. The generation unit, for example, prioritizes generating content based on the latest data. For example, the generation unit evaluates the time when data was collected and prioritizes generating content based on the latest data. The generation unit can also postpone generating content based on older data. For example, the generation unit evaluates the time when data was collected and prioritizes generating content based on older data. The generation unit can also prioritize generating content based on data collected during a specific period. For example, the generation unit prioritizes generating content based on data collected during a specific period. This enables efficient content generation by determining generation priorities based on the time when content was collected. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can have AI evaluate the time when data was collected and have AI determine the generation priorities.

[0081] The generation unit can adjust the order of generation based on the relevance of the content during generation. The generation unit, for example, prioritizes generating content based on highly relevant data. For example, the generation unit evaluates correlations between data and prioritizes generating content based on highly relevant data. The generation unit can also postpone generating content based on less relevant data. For example, the generation unit evaluates correlations between data and prioritizes generating content based on less relevant data. The generation unit can also prioritize generating content based on data related to a specific theme. For example, the generation unit prioritizes generating content based on data related to a specific theme. This enables efficient content generation by adjusting the order of generation based on the relevance of the content. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can have AI perform data relevance evaluation and adjust the order of generation.

[0082] The providing unit can estimate the user's emotions and adjust the content delivery method based on the estimated user emotions. For example, if the user is excited, the providing unit uses a visually stimulating delivery method. For example, the providing unit captures the user's facial expression with a camera, estimates the user's emotions using an emotion estimation algorithm, and uses a visually stimulating delivery method. Furthermore, if the user is relaxed, the providing unit can use a calming delivery method. For example, the providing unit records the user's voice, estimates the user's emotions using voice analysis technology, and uses a calming delivery method. Furthermore, if the user is stressed, the providing unit can use a simple, highly visible delivery method. For example, the providing unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor, estimates the user's emotions using an emotion estimation algorithm, and uses a simple, highly visible delivery method. This enables more effective content delivery by adjusting the content delivery method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input user emotion data into AI and cause the AI ​​to adjust the method of providing content.

[0083] The providing unit can select the provision method by referring to the user's past learning history when providing the information. The providing unit, for example, analyzes the user's past learning history and selects the most effective provision method. For example, the providing unit selects the most effective provision method based on the user's past learning history. The providing unit can also select a preferred provision method from the user's past learning history. For example, the providing unit selects a preferred provision method based on the user's past learning history. Furthermore, the providing unit can also select a provision method with a high learning effect based on the user's past learning history. For example, the providing unit selects a provision method with a high learning effect based on the user's past learning history. In this way, the optimal provision method can be selected by referring to the user's past learning history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can cause AI to analyze the user's past learning history and select a provision method.

[0084] The providing unit can customize the means of provision based on the user's current learning situation at the time of provision. The providing unit, for example, analyzes the user's current learning situation and customizes the optimal means of provision. For example, the providing unit customizes the optimal means of provision based on the user's current learning situation. The providing unit can also customize an effective means of provision based on the user's current learning situation. For example, the providing unit customizes an effective means of provision based on the user's current learning situation. Furthermore, the providing unit can also customize a means of provision with high learning effectiveness based on the user's current learning situation. For example, the providing unit customizes a means of provision with high learning effectiveness based on the user's current learning situation. This enables more effective content provision by customizing the means of provision based on the user's current learning situation. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can cause AI to analyze the user's current learning situation and customize the means of provision.

[0085] The providing unit can estimate the user's emotions and determine the priority of content provision based on the estimated user emotions. For example, if the user is excited, the providing unit can prioritize providing content with high entertainment value. For example, the providing unit can capture the user's facial expression with a camera, estimate the user's emotions using an emotion estimation algorithm, and prioritize providing content with high entertainment value. Furthermore, if the user is relaxed, the providing unit can prioritize providing content with high educational value. For example, the providing unit can record the user's voice, estimate the user's emotions using voice analysis technology, and prioritize providing content with high educational value. Furthermore, if the user is stressed, the providing unit can prioritize providing content with a relaxing effect. For example, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the user's emotions using an emotion estimation algorithm, and prioritize providing content with a relaxing effect. This enables more effective content provision by determining the priority of content provision based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, or without, an AI. For example, the providing unit may input user emotion data into the AI ​​and have the AI ​​determine the priority of content provision.

[0086] The providing unit can select the optimal delivery method by taking into account the user's geographical location information at the time of delivery. The providing unit, for example, provides content related to a region based on the user's geographical location information. For example, the providing unit provides content related to a region based on the user's geographical location information. The providing unit can also provide content in an optimal language based on the user's geographical location information. For example, the providing unit provides content in an optimal language based on the user's geographical location information. The providing unit can also provide content related to the culture or history of a region based on the user's geographical location information. For example, the providing unit provides content related to the culture or history of a region based on the user's geographical location information. This makes it possible to select the optimal delivery method by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into AI and cause the AI ​​to select the optimal delivery method.

[0087] The providing unit can analyze the user's social media activity at the time of providing and suggest a means of provision. The providing unit, for example, analyzes the user's social media activity and suggests content that the user is likely to be interested in. For example, the providing unit analyzes the user's social media activity and suggests content that the user is likely to be interested in. The providing unit can also analyze the user's social media activity and suggest content shared by friends. For example, the providing unit analyzes the user's social media activity and suggests content shared by friends. The providing unit can also analyze the user's social media activity and suggest content related to trends. For example, the providing unit analyzes the user's social media activity and suggests content related to trends. In this way, content that the user is likely to be interested in can be suggested by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can cause AI to analyze the user's social media activity and suggest a means of provision. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data on educational institutions using the camera 42 and communication I / F 44 of the smart device 14, and integrates the collected data using the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and extracts information that interests children. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates interactive learning content based on the analyzed data. The provision unit, realized, for example, by the control unit 46A of the smart device 14, provides the generated content to children via digital textbooks or online platforms. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data on educational institutions using the camera 42 and communication I / F 44 of the smart glasses 214 and integrates the collected data by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and extracts information that interests children. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates interactive learning content based on the analyzed data. The provision unit, realized, for example, by the control unit 46A of the smart glasses 214, provides the generated content to children via digital textbooks or online platforms. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects data on educational institutions using the camera 42 and communication I / F 44 of the headset-type terminal 314, and integrates the collected data using the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and extracts information that interests children. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates interactive learning content based on the analyzed data. The provision unit, realized, for example, by the control unit 46A of the headset-type terminal 314, provides the generated content to children via digital textbooks or online platforms. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data on educational institutions using the camera 42 and communication I / F 44 of the robot 414, and integrates the collected data by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and extracts information that interests children. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates interactive learning content based on the analyzed data. The provision unit, realized, for example, by the control unit 46A of the robot 414, provides the generated content to children via digital textbooks or online platforms.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The collection unit can also analyze the user's learning history and collect new data based on what the user has learned in the past. For example, the collection unit can analyze topics that the user has studied in the past and collect related new data. The collection unit can also identify topics that the user has struggled with in the past and collect supplementary data related to those topics. Furthermore, the collection unit can prioritize collecting topics that the user has shown interest in in the past. This allows for data collection based on the user's learning history, making it possible to provide a more personalized learning experience.

[0090] The analysis unit can also estimate the user's learning style and adjust the data analysis method based on the estimated learning style. For example, if the user is a visual learner, the analysis unit can provide analysis results that make extensive use of graphs and charts. If the user is an auditory learner, the analysis unit can also provide analysis results that include audio commentary. Furthermore, if the user is an experiential learner, the analysis unit can also provide interactive analysis results. This can improve learning effectiveness by providing an analysis method that suits the user's learning style.

[0091] The generation unit can also monitor the user's learning progress in real time and generate content according to the progress. For example, if the user is struggling with a particular topic, the generation unit can generate supplemental content related to that topic. Also, if the user is making good progress in their learning, the generation unit can generate content for the next level. Furthermore, the generation unit can generate review content for topics that the user has completed learning. This makes it possible to maximize the learning effect by providing content according to the user's learning progress.

[0092] The providing unit can also detect the user's learning environment and select the optimal content providing method. For example, if the user is in a quiet environment, the providing unit can provide content including audio commentary. If the user is on the move, the providing unit can also provide visual content. Furthermore, if the user is learning in a group, the providing unit can also provide collaborative content. This makes it possible to improve learning effectiveness by selecting a content providing method that suits the user's learning environment.

[0093] The collection unit can also estimate the user's emotions and evaluate the reliability of the data based on the estimated emotions. For example, if the user is excited, the collection unit carefully evaluates data that is easily affected by emotions. The collection unit can also highly evaluate the reliability of the data if the user is relaxed. Furthermore, the collection unit can re-evaluate the reliability of the data if the user is feeling stressed. This allows for more accurate data collection by evaluating the reliability of the data according to the user's emotions.

[0094] The analysis unit can also estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated emotions. For example, if the user is excited, the analysis unit can present a visually stimulating analysis result. If the user is relaxed, the analysis unit can also present a calm analysis result. Furthermore, if the user is feeling stressed, the analysis unit can present a simple, highly visible analysis result. This can improve the learning effect by providing a presentation method of the analysis results according to the user's emotions.

[0095] The generation unit can also analyze the user's learning history and generate new content based on what the user has learned in the past. For example, the generation unit can analyze topics that the user has previously studied and generate new related content. The generation unit can also identify topics that the user has previously struggled with and generate supplemental content related to those topics. Furthermore, the generation unit can prioritize generating topics in which the user has previously shown interest. This allows for the provision of a more personalized learning experience by generating content based on the user's learning history.

[0096] The providing unit can also estimate the user's emotions and adjust the timing of content provision based on the estimated emotions. For example, if the user is excited, the providing unit can provide the content immediately. Alternatively, if the user is relaxed, the providing unit can provide the content at regular intervals. Furthermore, if the user is feeling stressed, the providing unit can reduce the frequency of content provision. In this way, by adjusting the timing of content provision according to the user's emotions, it is possible to improve the learning effect.

[0097] The collection unit can also analyze the data provision frequency of the educational institution and dynamically adjust the collection schedule. For example, the collection unit can monitor the data provision frequency of the educational institution in real time and dynamically set an optimal collection schedule. The collection unit can also automatically adjust the collection schedule in response to fluctuations in the data provision frequency of the educational institution. Furthermore, the collection unit can set a future collection schedule based on a prediction of the data provision frequency of the educational institution. This enables efficient data collection by dynamically analyzing the data provision frequency.

[0098] The analysis unit can also estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is excited, the analysis unit can prioritize analyzing data with high entertainment value. Also, if the user is relaxed, the analysis unit can prioritize analyzing data with high educational value. Furthermore, if the user is feeling stressed, the analysis unit can prioritize analyzing data with a relaxing effect. In this way, by determining the priority of analysis according to the user's emotions, more effective data analysis is possible.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The collection department collects data from educational institutions. Data from educational institutions includes textbook content from textbook companies, exhibit information from museums, and research data from universities. For example, the collection department obtains textbook content from textbook companies' databases, exhibit information from museum databases, and research results from university databases. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using data mining techniques, statistical analysis techniques, and machine learning algorithms, for example, to extract information that children are interested in, analyze data trends, and identify topics. Step 3: The generator generates interactive learning content based on the data analyzed by the analyzer. The generator generates interactive quizzes, games, educational videos, etc., allowing children to learn while having fun. Step 4: The provider provides the content generated by the generator. The provider provides the content through digital textbooks, online platforms, and mobile apps, making it accessible to children.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects data from educational institutions; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates interactive learning content based on the data analyzed by the analysis unit; a providing unit that provides the content generated by the generating unit. A system characterized by:

2. The collecting unit Collect data from textbook companies, museums, and universities 2. The system of claim 1.

3. The analysis unit Analyze the collected data and extract information that interests children 2. The system of claim 1.

4. The generation unit Generate interactive quizzes, games, and video content based on the analyzed data 2. The system of claim 1.

5. The providing unit Providing generated content through digital textbooks and online platforms 2. The system of claim 1.

6. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

7. The collecting unit Analyze the frequency of data provided by educational institutions and set a collection schedule 2. The system of claim 1.

8. The collecting unit Evaluate the reliability of data when collecting it and prioritize collecting reliable data 2. The system of claim 1.

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