system

Abstract

The system according to this embodiment aims to maintain learners' interest by linking the study of entrance examination questions with entertainment content. [Solution] The system according to this embodiment comprises an analysis unit, a search unit, a suggestion unit, and a history management unit. The analysis unit analyzes the text of the entrance examination questions. The search unit searches for relevant entertainment content based on the keywords extracted by the analysis unit. The suggestion unit suggests the content found by the search unit to the user. The history management unit manages the user's learning history.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot 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 an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that learning of entrance examination questions was monotonous and it was difficult to continuously attract the interest of learners.

[0005] The system according to the embodiment aims to continuously attract the interest of learners by associating learning of entrance examination questions with entertainment content.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a search unit, a suggestion unit, and a history management unit. The analysis unit analyzes the text of entrance examination questions. The search unit searches for relevant entertainment content based on keywords extracted by the analysis unit. The suggestion unit suggests the content found by the search unit to the user. The history management unit manages the user's learning history. [Effects of the Invention]

[0007] The system according to this embodiment can maintain learners' interest by linking the study of entrance examination questions with entertainment content. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The learning app "Linkopedia" according to an embodiment of the present invention is a system that connects entrance exam questions with entertainment content. In this system, when a user solves an entrance exam question, an analysis unit analyzes the text of the question using AI and extracts relevant keywords. Next, a search unit searches for relevant entertainment content, such as novels, movies, and manga, using AI based on the keywords extracted by the analysis unit. Furthermore, a suggestion unit suggests content that is optimal for each user based on the user's learning history, based on the content found by the search unit. Finally, a history management unit manages the history of problems solved and content viewed by the user and provides it to the suggestion unit. In this way, Linkopedia evolves learning into a more enjoyable and deeply understanding experience by connecting entrance exam questions with entertainment content. As a result, Linkopedia allows users to deepen their learning through relevant entertainment content when solving entrance exam questions. For example, solving questions about kanji or proverbs can lead to discovering novels and movies that use them, or solving history questions can lead to discovering related manga. This evolves learning from problem-solving to an experience that connects with context. Linkopedia offers rich understanding and enjoyable experiences.

[0029] The Linkopedia system according to this embodiment comprises an analysis unit, a search unit, a suggestion unit, and a history management unit. The analysis unit analyzes the text of entrance examination questions and extracts relevant keywords. The analysis unit analyzes the text using, for example, natural language processing technology and extracts important keywords. The analysis unit can extract keywords based on frequency and importance using, for example, a text analysis algorithm. The analysis unit can also understand the meaning of the text and extract relevant keywords using, for example, a machine learning algorithm. The search unit searches for relevant entertainment content based on the keywords extracted by the analysis unit. The search unit searches databases on the internet to find relevant novels, movies, manga, etc. The search unit can filter relevant content based on keywords and provide optimal results. The search unit can also prioritize searching for highly relevant content by considering, for example, the user's preferences and past history. The suggestion unit suggests the content found by the search unit to the user. The suggestion unit suggests the most suitable content for each user based on the user's learning history. The suggestion unit can, for example, analyze user preferences and past history to select the most suitable content. It can also suggest content at the appropriate time, taking into account the user's learning progress. The history management unit manages the history of problems solved and content viewed by the user and provides it to the suggestion unit. The history management unit can, for example, store the user's learning history in a database and provide it to the suggestion unit as needed. The history management unit can, for example, analyze the user's learning history to understand their learning progress. Furthermore, the history management unit can, for example, evaluate the effectiveness of learning based on the user's learning history. As a result, the Linkopedia system according to this embodiment allows users to deepen their learning through related entertainment content when solving entrance exam problems. For example, solving problems about kanji or proverbs can lead to discovering novels and movies in which they are used, or solving history problems can lead to discovering related manga. This evolves learning from mere problem-solving to an experience connected to context.Linkopedia offers rich understanding and enjoyable experiences.

[0030] The analysis unit analyzes the text of entrance examination questions and extracts relevant keywords. For example, the analysis unit uses natural language processing (NLP) techniques to analyze the text and extract important keywords. Specifically, by using morphological analysis and dependency structure analysis as NLP techniques, the relationships between words and phrases in the text can be clarified and important keywords can be extracted. For example, morphological analysis is used to divide the text into words and identify the part of speech of each word. Next, dependency structure analysis is used to analyze the dependencies between each word and understand the sentence structure. This allows for the extraction of important keywords and phrases within the text. The analysis unit can also use text analysis algorithms to extract keywords based on frequency and importance. For example, the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm is used to calculate the frequency and importance of each word and identify important keywords. Furthermore, the analysis unit can use machine learning algorithms to understand the meaning of the text and extract relevant keywords. For example, supervised learning is used to learn from past entrance examination questions and their answers, enabling the extraction of appropriate keywords for new questions. This allows the analysis unit to perform a sophisticated analysis of the entrance examination text and accurately extract relevant keywords.

[0031] The search unit searches for relevant entertainment content based on keywords extracted by the analysis unit. For example, the search unit searches databases on the internet to find relevant novels, movies, manga, etc. Specifically, the search unit sends queries to multiple databases to retrieve relevant content. For example, it can search databases of online bookstores, movie information sites, and digital manga libraries. The search unit can filter relevant content based on keywords to provide optimal results. For example, it selects the most suitable content considering factors such as keyword matching, content ratings, and user preferences. Furthermore, the search unit can prioritize searching for highly relevant content by considering user preferences and past history. For example, it prioritizes displaying highly relevant content based on content the user has previously viewed or rated. In addition, the search unit can provide the latest content by utilizing databases that are updated in real time. This allows the search unit to always provide users with the most up-to-date and relevant entertainment content.

[0032] The suggestion unit proposes content to users that has been retrieved by the search unit. For example, the suggestion unit proposes content that is best suited to each individual user based on the user's learning history. Specifically, the suggestion unit analyzes the user's learning history, preferences, and past browsing history to select the most suitable content. For example, it prioritizes suggesting highly relevant content based on content that the user has previously viewed or rated. The suggestion unit can also propose content at the appropriate time, taking into account the user's learning progress. For example, when a user achieves a specific learning goal, it proposes content to help them move on to the next step. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, by having users rate the suggested content, the suggestion unit can adjust its next suggestions based on that rating. In this way, the suggestion unit can always propose the most suitable entertainment content to users, maximizing the effectiveness of their learning.

[0033] The History Management Department manages the history of problems solved and content viewed by users and provides it to the Proposal Department. For example, the History Management Department stores users' learning history in a database and provides it to the Proposal Department as needed. Specifically, the History Management Department records detailed history of problems solved and content viewed by users and stores it in a database. For example, it records information such as the type and difficulty level of problems solved by the user and the time taken to solve them, in order to understand the progress of learning. The History Management Department can also analyze users' learning history and evaluate the effectiveness of learning. For example, it analyzes changes in the correct answer rate and time taken to solve problems solved by the user to evaluate the effectiveness of learning. Furthermore, the History Management Department can visualize the progress of learning based on the user's learning history. For example, it can display information such as the type and difficulty level of problems solved by the user and the time taken to solve them in graphs and charts, allowing users to understand their learning progress at a glance. In this way, the History Management Department can effectively manage users' learning history and provide it to the Proposal Department, enabling it to propose the most suitable entertainment content to users.

[0034] The analysis unit can analyze the text of entrance examination questions and extract relevant keywords. For example, the analysis unit can analyze the text using natural language processing technology and extract important keywords. For example, the analysis unit can extract keywords based on frequency and importance using text analysis algorithms. Furthermore, the analysis unit can also understand the meaning of the text and extract relevant keywords using machine learning algorithms. This provides a basis for searching for relevant entertainment content by analyzing the text of entrance examination questions and extracting relevant keywords. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the text of the entrance examination questions into a generating AI and have the generating AI perform the extraction of relevant keywords.

[0035] The search unit can search for relevant entertainment content such as novels, movies, and manga based on keywords extracted by the analysis unit. For example, the search unit can search databases on the internet to find relevant novels, movies, and manga. The search unit can also filter relevant content based on keywords to provide optimal results. Furthermore, the search unit can prioritize searching for highly relevant content by considering user preferences and past history. This provides users with diverse learning resources by allowing them to search for relevant entertainment content. Some or all of the above processing in the search unit may be performed using AI, or without AI. For example, the search unit can input extracted keywords into a generating AI and have the generating AI perform a search for relevant entertainment content.

[0036] The suggestion unit can suggest content found by the search unit to the user. For example, the suggestion unit can suggest content best suited to each individual user based on the user's learning history. For example, the suggestion unit can analyze the user's preferences and past history to select the most suitable content. The suggestion unit can also suggest content at the appropriate time, taking into account the user's learning progress. This improves learning motivation by suggesting entertainment content best suited to the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input content found by the search unit into a generation AI and have the generation AI suggest content best suited to the user.

[0037] The history management unit can manage the history of problems solved and content viewed by users and provide it to the suggestion unit. For example, the history management unit can store the user's learning history in a database and provide it to the suggestion unit as needed. For example, the history management unit can analyze the user's learning history to understand the progress of their learning. The history management unit can also evaluate the effectiveness of learning based on the user's learning history. By managing the user's learning history, it becomes possible to provide more personalized content suggestions. Some or all of the above processes in the history management unit may be performed using AI, for example, or without AI. For example, the history management unit can input the user's learning history into a generating AI and have the generating AI perform the history management.

[0038] The analysis unit can change its analysis algorithm based on the difficulty level of the questions when analyzing the text of entrance examination questions. For example, for easy questions, the analysis unit can use a simple algorithm to perform a quick analysis. For difficult questions, the analysis unit can use a complex algorithm to perform a detailed analysis. Furthermore, for questions of moderate difficulty, the analysis unit can use a balanced algorithm. This improves the accuracy of the analysis by changing the analysis algorithm according to the difficulty level of the questions. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the text of the entrance examination questions into a generating AI and have the generating AI execute the changes to the analysis algorithm.

[0039] The analysis unit can estimate the examiner's intent when analyzing the text of entrance examination questions and reflect it in the analysis results. For example, the analysis unit can estimate the examiner's intent and extract relevant keywords. The analysis unit can estimate the examiner's intent and reflect it in the analysis results. Furthermore, the analysis unit can estimate the examiner's intent and adjust the analysis results. This provides more accurate analysis results by considering the examiner's intent. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the text of the entrance examination questions into a generating AI and have the generating AI perform the estimation of the examiner's intent.

[0040] The analysis unit can improve the accuracy of its analysis by referring to the user's past answer history when analyzing the text of entrance examination questions. For example, the analysis unit can refer to the user's past answer history and extract relevant keywords. For example, the analysis unit can refer to the user's past answer history and reflect it in the analysis results. Furthermore, the analysis unit can refer to the user's past answer history and adjust the analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past answer history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past answer history into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0041] The analysis unit can customize the analysis method based on the user's learning style when analyzing the text of entrance examination questions. For example, the analysis unit can customize the analysis method based on the user's learning style. For example, the analysis unit can extract relevant keywords based on the user's learning style. The analysis unit can also adjust the analysis results based on the user's learning style. By customizing the analysis method according to the user's learning style, it provides more effective analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's learning style into a generating AI and have the generating AI perform the customization of the analysis method.

[0042] The search unit can filter search results based on extracted keywords, taking into account content ratings and reviews. For example, the search unit can prioritize displaying highly-rated content. For example, the search unit can prioritize displaying content with reviews that match the user's preferences. The search unit can also, for example, exclude low-rated content. This ensures that high-quality content is provided by considering content ratings and reviews. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input extracted keywords into a generating AI and have the generating AI perform filtering of search results that take ratings and reviews into account.

[0043] The search unit can adjust search results based on extracted keywords, taking into account the publication date of the content. For example, the search unit can prioritize displaying the latest content. For example, the search unit can prioritize displaying classic content. The search unit can also adjust search results to match user preferences, taking into account the publication date. This allows the search unit to provide content that suits the user's preferences by considering the publication date of the content. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input extracted keywords into a generating AI and have the generating AI perform the adjustment of search results taking into account the publication date.

[0044] The search unit, when performing a search based on extracted keywords, can prioritize searching for highly relevant content by considering the user's geographical location. For example, the search unit may prioritize displaying content related to the user's current location. The search unit may also display highly relevant content by considering the user's past location information. Furthermore, the search unit may suggest optimal content based on the user's geographical location information. This allows the system to provide more relevant content by considering the user's geographical location. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit may input the user's geographical location information into a generating AI and have the generating AI perform a search for highly relevant content.

[0045] The search unit can analyze the user's social media activity to find relevant content when performing a search based on extracted keywords. For example, the search unit can analyze the user's interests and preferences on social media and display relevant content. For example, the search unit can suggest optimal content based on the user's social media activity history. The search unit can also consider the activities of the user's followers and friends on social media to display relevant content. This provides more personalized content by considering the user's social media activity. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the user's social media activity data into a generating AI and have the generating AI perform a search for relevant content.

[0046] The suggestion unit can propose the most suitable content by considering the user's learning progress when selecting content to propose. For example, the suggestion unit can analyze the user's learning progress and propose the most suitable content. For example, the suggestion unit can propose relevant content based on the user's learning progress. The suggestion unit can also select the most suitable content by considering the user's learning progress. This supports more effective learning by considering the user's learning progress. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's learning progress into a generating AI and have the generating AI propose the most suitable content.

[0047] The suggestion unit can improve the accuracy of its suggestions by referring to the user's past browsing history when selecting content to suggest. For example, the suggestion unit can refer to the user's past browsing history and suggest the most suitable content. For example, the suggestion unit can refer to the user's past browsing history and suggest relevant content. Furthermore, the suggestion unit can also refer to the user's past browsing history and improve the accuracy of its suggestions. This makes it possible to provide more personalized suggestions by referring to the user's past browsing history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past browsing history into a generating AI and have the generating AI perform the task of improving the accuracy of its suggestions.

[0048] The suggestion unit can customize the content of its suggestions based on the user's interests and preferences when selecting content to suggest. For example, the suggestion unit can analyze the user's interests and preferences and suggest the most suitable content. For example, the suggestion unit can suggest relevant content based on the user's interests and preferences. Furthermore, the suggestion unit can customize the content of its suggestions, taking into account the user's interests and preferences. This allows the suggestion unit to provide more appropriate content by customizing the content of its suggestions based on the user's interests and preferences. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user interest data into a generating AI and have the generating AI customize the content of its suggestions.

[0049] The suggestion unit can adjust its suggestion method based on the user's learning style when selecting content to suggest. For example, the suggestion unit can suggest the most suitable content based on the user's learning style. For example, the suggestion unit can suggest relevant content based on the user's learning style. The suggestion unit can also adjust its suggestion method based on the user's learning style. This supports more effective learning by adjusting the suggestion method based on the user's learning style. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user learning style data into a generating AI and have the generating AI adjust the suggestion method.

[0050] The history management unit can change the display method of the history when managing the history, taking into account the user's learning progress. For example, the history management unit can analyze the user's learning progress and provide the optimal history display method. For example, the history management unit can display relevant history based on the user's learning progress. The history management unit can also change the display method of the history, taking into account the user's learning progress. This makes it possible to manage the history more effectively by changing the display method of the history according to the user's learning progress. Some or all of the above processing in the history management unit may be performed using AI, for example, or without using AI. For example, the history management unit can input the user's learning progress into a generating AI and have the generating AI execute the change in the history display method.

[0051] The history management unit can select the optimal history management method when managing history, taking into account the user's device information. For example, if the user is using a smartphone, the history management unit can provide a history management method that is adapted to the screen size. For example, if the user is using a tablet, the history management unit can provide a history management method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the history management unit can provide a concise and highly visible history management method. This enables more effective history management by providing the optimal history management method according to the user's device information. Some or all of the above processing in the history management unit may be performed using AI, for example, or without AI. For example, the history management unit can input the user's device information into a generating AI and have the generating AI select the optimal history management method.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The Linkopedia system can also include an analysis unit that customizes the analysis method based on the user's learning style. For example, if the user is a visual learner, the analysis unit can perform analysis that emphasizes images and diagrams. If the user is an auditory learner, it can perform analysis that emphasizes audio data. Furthermore, if the user is an experiential learner, it can perform analysis that emphasizes actual experiences and simulations. This allows for more effective learning by providing analysis methods tailored to the user's learning style.

[0054] The Linkopedia system can also include an evaluation unit that assesses the effectiveness of learning based on the user's learning history. For example, the evaluation unit can analyze the user's accuracy rate and time to solve problems to assess learning effectiveness. It can also evaluate the user's understanding of the content they have viewed. Furthermore, based on the user's learning history, it can evaluate learning progress and suggest the next learning steps. This allows for the provision of more effective learning plans by evaluating the user's learning effectiveness.

[0055] The Linkopedia system can also include a visualization unit that visualizes learning progress based on the user's learning history. This visualization unit can, for example, display the user's correct answer rate and answer time for problems solved using graphs and charts. It can also visualize the user's understanding of the content they have viewed. Furthermore, it can display learning progress in a timeline format based on the user's learning history. By visualizing the user's learning progress, this can improve their motivation to learn.

[0056] The Linkopedia system can also include a prediction unit that forecasts learning progress based on the user's learning history. For example, the prediction unit can predict the next problem to solve based on the user's accuracy rate and time taken to solve problems. It can also predict the next content to view based on the user's understanding of the content they have viewed. Furthermore, it can predict learning progress based on the user's learning history and suggest the next learning step. This allows for the provision of a more effective learning plan by predicting the user's learning progress.

[0057] The Linkopedia system can also include a sharing section that allows users to share their learning progress based on their learning history. This sharing section can, for example, share the accuracy rate and time taken to solve problems with other users. It can also share the level of understanding of content viewed by other users. Furthermore, it can share learning progress with other users based on their learning history. This allows for increased motivation to learn by sharing users' learning progress.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The analysis unit analyzes the text of the entrance examination questions and extracts relevant keywords. The analysis unit uses natural language processing technology, text analysis algorithms, and machine learning algorithms to extract keywords based on their frequency and importance. Step 2: The search unit searches for relevant entertainment content based on the keywords extracted by the analysis unit. The search unit searches databases on the internet to find relevant novels, movies, comics, etc. The search unit filters the content based on the keywords and provides the best results considering the user's preferences and past history. Step 3: The suggestion unit suggests content found by the search unit to the user. Based on the user's learning history, the suggestion unit suggests content best suited to each individual user. The suggestion unit suggests content at the appropriate time, taking into account the user's preferences, past history, and learning progress. Step 4: The History Management Department manages the history of problems solved and content viewed by users and provides it to the Suggestion Department. The History Management Department stores the user's learning history in a database and provides it to the Suggestion Department as needed. The History Management Department analyzes the user's learning history to understand their learning progress and evaluate the effectiveness of their learning.

[0060] (Example of form 2) The learning app "Linkopedia" according to an embodiment of the present invention is a system that connects entrance exam questions with entertainment content. In this system, when a user solves an entrance exam question, an analysis unit analyzes the text of the question using AI and extracts relevant keywords. Next, a search unit searches for relevant entertainment content, such as novels, movies, and manga, using AI based on the keywords extracted by the analysis unit. Furthermore, a suggestion unit suggests content that is optimal for each user based on the user's learning history, based on the content found by the search unit. Finally, a history management unit manages the history of problems solved and content viewed by the user and provides it to the suggestion unit. In this way, Linkopedia evolves learning into a more enjoyable and deeply understanding experience by connecting entrance exam questions with entertainment content. As a result, Linkopedia allows users to deepen their learning through relevant entertainment content when solving entrance exam questions. For example, solving questions about kanji or proverbs can lead to discovering novels and movies that use them, or solving history questions can lead to discovering related manga. This evolves learning from problem-solving to an experience that connects with context. Linkopedia offers rich understanding and enjoyable experiences.

[0061] The Linkopedia system according to this embodiment comprises an analysis unit, a search unit, a suggestion unit, and a history management unit. The analysis unit analyzes the text of entrance examination questions and extracts relevant keywords. The analysis unit analyzes the text using, for example, natural language processing technology and extracts important keywords. The analysis unit can extract keywords based on frequency and importance using, for example, a text analysis algorithm. The analysis unit can also understand the meaning of the text and extract relevant keywords using, for example, a machine learning algorithm. The search unit searches for relevant entertainment content based on the keywords extracted by the analysis unit. The search unit searches databases on the internet to find relevant novels, movies, manga, etc. The search unit can filter relevant content based on keywords and provide optimal results. The search unit can also prioritize searching for highly relevant content by considering, for example, the user's preferences and past history. The suggestion unit suggests the content found by the search unit to the user. The suggestion unit suggests the most suitable content for each user based on the user's learning history. The suggestion unit can, for example, analyze user preferences and past history to select the most suitable content. It can also suggest content at the appropriate time, taking into account the user's learning progress. The history management unit manages the history of problems solved and content viewed by the user and provides it to the suggestion unit. The history management unit can, for example, store the user's learning history in a database and provide it to the suggestion unit as needed. The history management unit can, for example, analyze the user's learning history to understand their learning progress. Furthermore, the history management unit can, for example, evaluate the effectiveness of learning based on the user's learning history. As a result, the Linkopedia system according to this embodiment allows users to deepen their learning through related entertainment content when solving entrance exam problems. For example, solving problems about kanji or proverbs can lead to discovering novels and movies in which they are used, or solving history problems can lead to discovering related manga. This evolves learning from mere problem-solving to an experience connected to context.Linkopedia offers rich understanding and enjoyable experiences.

[0062] The analysis unit analyzes the text of entrance examination questions and extracts relevant keywords. For example, the analysis unit uses natural language processing (NLP) techniques to analyze the text and extract important keywords. Specifically, by using morphological analysis and dependency structure analysis as NLP techniques, the relationships between words and phrases in the text can be clarified and important keywords can be extracted. For example, morphological analysis is used to divide the text into words and identify the part of speech of each word. Next, dependency structure analysis is used to analyze the dependencies between each word and understand the sentence structure. This allows for the extraction of important keywords and phrases within the text. The analysis unit can also use text analysis algorithms to extract keywords based on frequency and importance. For example, the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm is used to calculate the frequency and importance of each word and identify important keywords. Furthermore, the analysis unit can use machine learning algorithms to understand the meaning of the text and extract relevant keywords. For example, supervised learning is used to learn from past entrance examination questions and their answers, enabling the extraction of appropriate keywords for new questions. This allows the analysis unit to perform a sophisticated analysis of the entrance examination text and accurately extract relevant keywords.

[0063] The search unit searches for relevant entertainment content based on keywords extracted by the analysis unit. For example, the search unit searches databases on the internet to find relevant novels, movies, manga, etc. Specifically, the search unit sends queries to multiple databases to retrieve relevant content. For example, it can search databases of online bookstores, movie information sites, and digital manga libraries. The search unit can filter relevant content based on keywords to provide optimal results. For example, it selects the most suitable content considering factors such as keyword matching, content ratings, and user preferences. Furthermore, the search unit can prioritize searching for highly relevant content by considering user preferences and past history. For example, it prioritizes displaying highly relevant content based on content the user has previously viewed or rated. In addition, the search unit can provide the latest content by utilizing databases that are updated in real time. This allows the search unit to always provide users with the most up-to-date and relevant entertainment content.

[0064] The suggestion unit proposes content to users that has been retrieved by the search unit. For example, the suggestion unit proposes content that is best suited to each individual user based on the user's learning history. Specifically, the suggestion unit analyzes the user's learning history, preferences, and past browsing history to select the most suitable content. For example, it prioritizes suggesting highly relevant content based on content that the user has previously viewed or rated. The suggestion unit can also propose content at the appropriate time, taking into account the user's learning progress. For example, when a user achieves a specific learning goal, it proposes content to help them move on to the next step. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, by having users rate the suggested content, the suggestion unit can adjust its next suggestions based on that rating. In this way, the suggestion unit can always propose the most suitable entertainment content to users, maximizing the effectiveness of their learning.

[0065] The History Management Department manages the history of problems solved and content viewed by users and provides it to the Proposal Department. For example, the History Management Department stores users' learning history in a database and provides it to the Proposal Department as needed. Specifically, the History Management Department records detailed history of problems solved and content viewed by users and stores it in a database. For example, it records information such as the type and difficulty level of problems solved by the user and the time taken to solve them, in order to understand the progress of learning. The History Management Department can also analyze users' learning history and evaluate the effectiveness of learning. For example, it analyzes changes in the correct answer rate and time taken to solve problems solved by the user to evaluate the effectiveness of learning. Furthermore, the History Management Department can visualize the progress of learning based on the user's learning history. For example, it can display information such as the type and difficulty level of problems solved by the user and the time taken to solve them in graphs and charts, allowing users to understand their learning progress at a glance. In this way, the History Management Department can effectively manage users' learning history and provide it to the Proposal Department, enabling it to propose the most suitable entertainment content to users.

[0066] The analysis unit can analyze the text of entrance examination questions and extract relevant keywords. For example, the analysis unit can analyze the text using natural language processing technology and extract important keywords. For example, the analysis unit can extract keywords based on frequency and importance using text analysis algorithms. Furthermore, the analysis unit can also understand the meaning of the text and extract relevant keywords using machine learning algorithms. This provides a basis for searching for relevant entertainment content by analyzing the text of entrance examination questions and extracting relevant keywords. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the text of the entrance examination questions into a generating AI and have the generating AI perform the extraction of relevant keywords.

[0067] The search unit can search for relevant entertainment content such as novels, movies, and manga based on keywords extracted by the analysis unit. For example, the search unit can search databases on the internet to find relevant novels, movies, and manga. The search unit can also filter relevant content based on keywords to provide optimal results. Furthermore, the search unit can prioritize searching for highly relevant content by considering user preferences and past history. This provides users with diverse learning resources by allowing them to search for relevant entertainment content. Some or all of the above processing in the search unit may be performed using AI, or without AI. For example, the search unit can input extracted keywords into a generating AI and have the generating AI perform a search for relevant entertainment content.

[0068] The suggestion unit can suggest content found by the search unit to the user. For example, the suggestion unit can suggest content best suited to each individual user based on the user's learning history. For example, the suggestion unit can analyze the user's preferences and past history to select the most suitable content. The suggestion unit can also suggest content at the appropriate time, taking into account the user's learning progress. This improves learning motivation by suggesting entertainment content best suited to the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input content found by the search unit into a generation AI and have the generation AI suggest content best suited to the user.

[0069] The history management unit can manage the history of problems solved and content viewed by users and provide it to the suggestion unit. For example, the history management unit can store the user's learning history in a database and provide it to the suggestion unit as needed. For example, the history management unit can analyze the user's learning history to understand the progress of their learning. The history management unit can also evaluate the effectiveness of learning based on the user's learning history. By managing the user's learning history, it becomes possible to provide more personalized content suggestions. Some or all of the above processes in the history management unit may be performed using AI, for example, or without AI. For example, the history management unit can input the user's learning history into a generating AI and have the generating AI perform the history management.

[0070] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can loosen the accuracy of the analysis and broaden the range of relevant keywords. For example, if the user is relaxed, the analysis unit can increase the accuracy of the analysis and narrow the range of relevant keywords. The analysis unit can also adjust the accuracy of the analysis to a moderate level and perform balanced keyword extraction if the user is excited. In this way, by adjusting the accuracy of the analysis according to the user's emotions, more appropriate analysis results are provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the analysis accuracy.

[0071] The analysis unit can change its analysis algorithm based on the difficulty level of the questions when analyzing the text of entrance examination questions. For example, for easy questions, the analysis unit can use a simple algorithm to perform a quick analysis. For difficult questions, the analysis unit can use a complex algorithm to perform a detailed analysis. Furthermore, for questions of moderate difficulty, the analysis unit can use a balanced algorithm. This improves the accuracy of the analysis by changing the analysis algorithm according to the difficulty level of the questions. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the text of the entrance examination questions into a generating AI and have the generating AI execute the changes to the analysis algorithm.

[0072] The analysis unit can estimate the examiner's intent when analyzing the text of entrance examination questions and reflect it in the analysis results. For example, the analysis unit can estimate the examiner's intent and extract relevant keywords. The analysis unit can estimate the examiner's intent and reflect it in the analysis results. Furthermore, the analysis unit can estimate the examiner's intent and adjust the analysis results. This provides more accurate analysis results by considering the examiner's intent. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the text of the entrance examination questions into a generating AI and have the generating AI perform the estimation of the examiner's intent.

[0073] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple display method. For example, if the user is relaxed, the analysis unit can provide a detailed display method. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating display method. In this way, by adjusting the display method of the analysis results according to the user's emotions, a display that is easy for the user to understand is provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0074] The analysis unit can improve the accuracy of its analysis by referring to the user's past answer history when analyzing the text of entrance examination questions. For example, the analysis unit can refer to the user's past answer history and extract relevant keywords. For example, the analysis unit can refer to the user's past answer history and reflect it in the analysis results. Furthermore, the analysis unit can refer to the user's past answer history and adjust the analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past answer history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past answer history into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0075] The analysis unit can customize the analysis method based on the user's learning style when analyzing the text of entrance examination questions. For example, the analysis unit can customize the analysis method based on the user's learning style. For example, the analysis unit can extract relevant keywords based on the user's learning style. The analysis unit can also adjust the analysis results based on the user's learning style. By customizing the analysis method according to the user's learning style, it provides more effective analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's learning style into a generating AI and have the generating AI perform the customization of the analysis method.

[0076] The search unit can estimate the user's emotions and adjust the priority of search results based on the estimated emotions. For example, if the user is stressed, the search unit can prioritize displaying relaxing content. If the user is relaxed, the search unit can prioritize displaying content that allows them to concentrate on learning. Furthermore, if the user is excited, the search unit can prioritize displaying content that captures their interest. This allows for the provision of more appropriate content by adjusting the priority of search results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the search unit may be performed using AI, or not. For example, the search unit can input user emotion data into a generative AI and have the generative AI adjust the priority of search results.

[0077] The search unit can filter search results based on extracted keywords, taking into account content ratings and reviews. For example, the search unit can prioritize displaying highly-rated content. For example, the search unit can prioritize displaying content with reviews that match the user's preferences. The search unit can also, for example, exclude low-rated content. This ensures that high-quality content is provided by considering content ratings and reviews. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input extracted keywords into a generating AI and have the generating AI perform filtering of search results that take ratings and reviews into account.

[0078] The search unit can adjust search results based on extracted keywords, taking into account the publication date of the content. For example, the search unit can prioritize displaying the latest content. For example, the search unit can prioritize displaying classic content. The search unit can also adjust search results to match user preferences, taking into account the publication date. This allows the search unit to provide content that suits the user's preferences by considering the publication date of the content. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input extracted keywords into a generating AI and have the generating AI perform the adjustment of search results taking into account the publication date.

[0079] The search unit can estimate the user's emotions and adjust how search results are displayed based on the estimated emotions. For example, if the user is stressed, the search unit can provide a simple display. If the user is relaxed, the search unit can provide a detailed display. Furthermore, if the user is excited, the search unit can provide a visually stimulating display. By adjusting the display of search results according to the user's emotions, the system provides a user-friendly display. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the search unit may be performed using AI, or not. For example, the search unit can input user emotion data into a generative AI and have the generative AI adjust how search results are displayed.

[0080] The search unit, when performing a search based on extracted keywords, can prioritize searching for highly relevant content by considering the user's geographical location. For example, the search unit may prioritize displaying content related to the user's current location. The search unit may also display highly relevant content by considering the user's past location information. Furthermore, the search unit may suggest optimal content based on the user's geographical location information. This allows the system to provide more relevant content by considering the user's geographical location. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit may input the user's geographical location information into a generating AI and have the generating AI perform a search for highly relevant content.

[0081] The search unit can analyze the user's social media activity to find relevant content when performing a search based on extracted keywords. For example, the search unit can analyze the user's interests and preferences on social media and display relevant content. For example, the search unit can suggest optimal content based on the user's social media activity history. The search unit can also consider the activities of the user's followers and friends on social media to display relevant content. This provides more personalized content by considering the user's social media activity. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the user's social media activity data into a generating AI and have the generating AI perform a search for relevant content.

[0082] The suggestion unit can estimate the user's emotions and adjust the way the suggestion is presented based on the estimated emotions. For example, if the user is stressed, the suggestion unit can provide a simple and easy-to-understand presentation. For example, if the user is relaxed, the suggestion unit can provide a presentation that includes detailed information. Furthermore, if the user is excited, the suggestion unit can provide a visually stimulating presentation. In this way, by adjusting the presentation of the suggestion according to the user's emotions, the suggestion unit provides a presentation that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the suggestion.

[0083] The suggestion unit can propose the most suitable content by considering the user's learning progress when selecting content to propose. For example, the suggestion unit can analyze the user's learning progress and propose the most suitable content. For example, the suggestion unit can propose relevant content based on the user's learning progress. The suggestion unit can also select the most suitable content by considering the user's learning progress. This supports more effective learning by considering the user's learning progress. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's learning progress into a generating AI and have the generating AI propose the most suitable content.

[0084] The suggestion unit can improve the accuracy of its suggestions by referring to the user's past browsing history when selecting content to suggest. For example, the suggestion unit can refer to the user's past browsing history and suggest the most suitable content. For example, the suggestion unit can refer to the user's past browsing history and suggest relevant content. Furthermore, the suggestion unit can also refer to the user's past browsing history and improve the accuracy of its suggestions. This makes it possible to provide more personalized suggestions by referring to the user's past browsing history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past browsing history into a generating AI and have the generating AI perform the task of improving the accuracy of its suggestions.

[0085] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can make suggestions at a time when the user can relax. If the user is relaxed, the suggestion unit can make suggestions at a time when the user can concentrate on learning. Also, if the user is excited, the suggestion unit can make suggestions at a time that will capture the user's interest. By adjusting the timing of suggestions according to the user's emotions, more effective suggestions become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into the generative AI and have the generative AI adjust the timing of suggestions.

[0086] The suggestion unit can customize the content of its suggestions based on the user's interests and preferences when selecting content to suggest. For example, the suggestion unit can analyze the user's interests and preferences and suggest the most suitable content. For example, the suggestion unit can suggest relevant content based on the user's interests and preferences. Furthermore, the suggestion unit can customize the content of its suggestions, taking into account the user's interests and preferences. This allows the suggestion unit to provide more appropriate content by customizing the content of its suggestions based on the user's interests and preferences. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user interest data into a generating AI and have the generating AI customize the content of its suggestions.

[0087] The suggestion unit can adjust its suggestion method based on the user's learning style when selecting content to suggest. For example, the suggestion unit can suggest the most suitable content based on the user's learning style. For example, the suggestion unit can suggest relevant content based on the user's learning style. The suggestion unit can also adjust its suggestion method based on the user's learning style. This supports more effective learning by adjusting the suggestion method based on the user's learning style. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user learning style data into a generating AI and have the generating AI adjust the suggestion method.

[0088] The history management unit can estimate the user's emotions and adjust the history management method based on the estimated emotions. For example, if the user is stressed, the history management unit can provide a simple history management method. For example, if the user is relaxed, the history management unit can provide a detailed history management method. Furthermore, if the user is excited, the history management unit can provide a visually stimulating history management method. This allows for more appropriate history management by adjusting the history management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the history management unit may be performed using AI, for example, or without AI. For example, the history management unit can input user emotion data into the generative AI and have the generative AI adjust the history management method.

[0089] The history management unit can change the display method of the history when managing the history, taking into account the user's learning progress. For example, the history management unit can analyze the user's learning progress and provide the optimal history display method. For example, the history management unit can display relevant history based on the user's learning progress. The history management unit can also change the display method of the history, taking into account the user's learning progress. This makes it possible to manage the history more effectively by changing the display method of the history according to the user's learning progress. Some or all of the above processing in the history management unit may be performed using AI, for example, or without using AI. For example, the history management unit can input the user's learning progress into a generating AI and have the generating AI execute the change in the history display method.

[0090] The history management unit can estimate the user's emotions and adjust the display order of the history based on the estimated emotions. For example, if the user is stressed, the history management unit will prioritize displaying recent history. For example, if the user is relaxed, the history management unit can display detailed history. Furthermore, if the user is excited, the history management unit can display visually stimulating history. By adjusting the display order of the history according to the user's emotions, a more appropriate history display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the history management unit may be performed using AI, for example, or without AI. For example, the history management unit can input user emotion data into the generative AI and have the generative AI adjust the display order of the history.

[0091] The history management unit can select the optimal history management method when managing history, taking into account the user's device information. For example, if the user is using a smartphone, the history management unit can provide a history management method that is adapted to the screen size. For example, if the user is using a tablet, the history management unit can provide a history management method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the history management unit can provide a concise and highly visible history management method. This enables more effective history management by providing the optimal history management method according to the user's device information. Some or all of the above processing in the history management unit may be performed using AI, for example, or without AI. For example, the history management unit can input the user's device information into a generating AI and have the generating AI select the optimal history management method.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The Linkopedia system can also include an analysis unit that customizes the analysis method based on the user's learning style. For example, if the user is a visual learner, the analysis unit can perform analysis that emphasizes images and diagrams. If the user is an auditory learner, it can perform analysis that emphasizes audio data. Furthermore, if the user is an experiential learner, it can perform analysis that emphasizes actual experiences and simulations. This allows for more effective learning by providing analysis methods tailored to the user's learning style.

[0094] The Linkopedia system can also include a progress adjustment unit that estimates the user's emotions and adjusts the learning progress based on those emotions. For example, if the user is feeling stressed, the progress adjustment unit can slow down the learning progress and provide relaxing content. If the user is relaxed, the learning progress can proceed at the normal pace. If the user is excited, the progress can be accelerated and engaging content can be provided. This allows for adjustment of the learning progress according to the user's emotions, supporting more effective learning.

[0095] The Linkopedia system can also include an evaluation unit that assesses the effectiveness of learning based on the user's learning history. For example, the evaluation unit can analyze the user's accuracy rate and time to solve problems to assess learning effectiveness. It can also evaluate the user's understanding of the content they have viewed. Furthermore, based on the user's learning history, it can evaluate learning progress and suggest the next learning steps. This allows for the provision of more effective learning plans by evaluating the user's learning effectiveness.

[0096] The Linkopedia system can also include a suggestion adjustment unit that estimates the user's emotions and adjusts the content suggestion method based on those emotions. For example, if the user is stressed, the suggestion adjustment unit can provide a simple and easy-to-understand suggestion method. If the user is relaxed, it can provide a suggestion method that includes detailed information. If the user is excited, it can also provide a visually stimulating suggestion method. This allows for more effective content suggestion by providing suggestions that match the user's emotions.

[0097] The Linkopedia system can also include a visualization unit that visualizes learning progress based on the user's learning history. This visualization unit can, for example, display the user's correct answer rate and answer time for problems solved using graphs and charts. It can also visualize the user's understanding of the content they have viewed. Furthermore, it can display learning progress in a timeline format based on the user's learning history. By visualizing the user's learning progress, this can improve their motivation to learn.

[0098] The Linkopedia system can also include a feedback adjustment unit that estimates the user's emotions and adjusts learning feedback based on those emotions. For example, if the user is stressed, the feedback adjustment unit can provide positive feedback. If the user is relaxed, it can provide detailed feedback. Furthermore, if the user is excited, it can provide visually stimulating feedback. This allows for improved learning effectiveness by providing feedback tailored to the user's emotions.

[0099] The Linkopedia system can also include a prediction unit that forecasts learning progress based on the user's learning history. For example, the prediction unit can predict the next problem to solve based on the user's accuracy rate and time taken to solve problems. It can also predict the next content to view based on the user's understanding of the content they have viewed. Furthermore, it can predict learning progress based on the user's learning history and suggest the next learning step. This allows for the provision of a more effective learning plan by predicting the user's learning progress.

[0100] The Linkopedia system can also include a motivation enhancement unit that estimates the user's emotions and improves learning motivation based on those emotions. For example, if the user is feeling stressed, the motivation enhancement unit can provide relaxing content. If the user is relaxed, it can provide content that helps them concentrate on learning. It can also provide engaging content if the user is excited. In this way, by providing content tailored to the user's emotions, learning motivation can be improved.

[0101] The Linkopedia system can also include a sharing section that allows users to share their learning progress based on their learning history. This sharing section can, for example, share the accuracy rate and time taken to solve problems with other users. It can also share the level of understanding of content viewed by other users. Furthermore, it can share learning progress with other users based on their learning history. This allows for increased motivation to learn by sharing users' learning progress.

[0102] The Linkopedia system can further include a goal-setting unit that estimates the user's emotions and sets learning goals based on those emotions. For example, if the user is feeling stressed, the goal-setting unit can set easily achievable goals. If the user is relaxed, it can set normal goals. Furthermore, if the user is excited, it can set challenging goals. This allows for improved learning motivation by setting goals that match the user's emotions.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The analysis unit analyzes the text of the entrance examination questions and extracts relevant keywords. The analysis unit uses natural language processing technology, text analysis algorithms, and machine learning algorithms to extract keywords based on their frequency and importance. Step 2: The search unit searches for relevant entertainment content based on the keywords extracted by the analysis unit. The search unit searches databases on the internet to find relevant novels, movies, comics, etc. The search unit filters the content based on the keywords and provides the best results considering the user's preferences and past history. Step 3: The suggestion unit suggests content found by the search unit to the user. Based on the user's learning history, the suggestion unit suggests content best suited to each individual user. The suggestion unit suggests content at the appropriate time, taking into account the user's preferences, past history, and learning progress. Step 4: The History Management Department manages the history of problems solved and content viewed by users and provides it to the Suggestion Department. The History Management Department stores the user's learning history in a database and provides it to the Suggestion Department as needed. The History Management Department analyzes the user's learning history to understand their learning progress and evaluate the effectiveness of their learning.

[0105] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0106] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0107] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0108] Each of the multiple elements described above, including the analysis unit, search unit, suggestion unit, and history management unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and analyzes the text of the entrance examination questions and extracts relevant keywords. The search unit is implemented by the processor 46 of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and searches for relevant entertainment content based on the extracted keywords. The suggestion unit is implemented by the processor 46 of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and suggests optimal content based on the user's learning history. The history management unit is implemented by the processor 46 of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and manages the user's learning history and provides it to the suggestion unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0115] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0116] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0117] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0119] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0121] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0123] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the analysis unit, search unit, suggestion unit, and history management unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and analyzes the text of the entrance examination questions and extracts relevant keywords. The search unit is implemented by the processor 46 of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and searches for relevant entertainment content based on the extracted keywords. The suggestion unit is implemented by the processor 46 of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and suggests optimal content based on the user's learning history. The history management unit is implemented by the processor 46 of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and manages the user's learning history and provides it to the suggestion unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the analysis unit, search unit, suggestion unit, and history management unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and analyzes the text of the entrance examination questions and extracts relevant keywords. The search unit is implemented by the processor 46 of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and searches for relevant entertainment content based on the extracted keywords. The suggestion unit is implemented by the processor 46 of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and suggests optimal content based on the user's learning history. The history management unit is implemented by the processor 46 of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and manages the user's learning history and provides it to the suggestion unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0149] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the analysis unit, search unit, suggestion unit, and history management unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 or the specific processing unit 290 of the data processing unit 12, and analyzes the text of the entrance examination questions and extracts relevant keywords. The search unit is implemented by the processor 46 of the robot 414 or the specific processing unit 290 of the data processing unit 12, and searches for relevant entertainment content based on the extracted keywords. The suggestion unit is implemented by the processor 46 of the robot 414 or the specific processing unit 290 of the data processing unit 12, and suggests optimal content based on the user's learning history. The history management unit is implemented by the processor 46 of the robot 414 or the specific processing unit 290 of the data processing unit 12, and manages the user's learning history and provides it to the suggestion unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0158] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0160] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0161] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0162] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0166] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0167] 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.

[0168] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0169] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0170] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0171] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0173] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0174] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0175] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0176] (Note 1) The analysis unit analyzes the text of the entrance examination questions, A search unit searches for relevant entertainment content based on keywords extracted by the analysis unit, A suggestion unit that suggests content found by the search unit to the user, It includes a history management unit that manages the user's learning history. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze the text of the entrance exam questions and extract relevant keywords. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned search unit, Based on the keywords extracted by the aforementioned analysis unit, the system searches for related entertainment content such as novels, movies, and manga. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, The search unit then suggests the content found by the search unit to the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned history management unit, The system manages the history of problems solved and content viewed by users and provides this information to the proposal unit. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, When analyzing entrance exam questions, the analysis algorithm is changed based on the difficulty level of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, When analyzing entrance exam questions, we try to estimate the intentions of the question setters and reflect them in the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, When analyzing entrance exam questions, we improve the accuracy of the analysis by referring to the user's past answer history. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing entrance exam questions, the analysis method is customized based on the user's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned search unit, It estimates the user's sentiment and adjusts the priority of search results based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned search unit, When searching based on extracted keywords, the search results are filtered by considering content ratings and reviews. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned search unit, When searching based on extracted keywords, the search results are adjusted to take into account the publication date of the content. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned search unit, It estimates the user's sentiment and adjusts how search results are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned search unit, When searching based on extracted keywords, the system prioritizes finding highly relevant content by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned search unit, When searching based on extracted keywords, the system analyzes the user's social media activity to find relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When selecting content to propose, we consider the user's learning progress to suggest the most suitable content. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When selecting content to suggest, we improve the accuracy of suggestions by referring to the user's past browsing history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the timing of suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When selecting content to propose, customize the suggestions based on the user's interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When selecting content to suggest, we adjust the suggestion method based on the user's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned history management unit, We estimate the user's emotions and adjust how history is managed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned history management unit, When managing history, the display method of the history is changed to take into account the user's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned history management unit, It estimates the user's emotions and adjusts the display order of the history based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned history management unit, When managing history, the optimal history management method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis unit analyzes the text of the entrance examination questions, A search unit searches for relevant entertainment content based on keywords extracted by the analysis unit, A suggestion unit that suggests content found by the search unit to the user, It includes a history management unit that manages the user's learning history. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze the text of the entrance exam questions and extract relevant keywords. The system according to feature 1.

3. The aforementioned search unit, Based on the keywords extracted by the aforementioned analysis unit, the system searches for related entertainment content such as novels, movies, and manga. The system according to feature 1.

4. The aforementioned proposal section is, The search unit then suggests the content found by the search unit to the user. The system according to feature 1.

5. The aforementioned history management unit, The system manages the history of problems solved and content viewed by users and provides this information to the proposal unit. The system according to feature 1.

6. The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system according to feature 1.

7. The aforementioned analysis unit, When analyzing entrance exam questions, the analysis algorithm is changed based on the difficulty level of the questions. The system according to feature 1.

8. The aforementioned analysis unit, When analyzing entrance exam questions, we try to estimate the intentions of the question setters and reflect them in the analysis results. The system according to feature 1.

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