System

Abstract

An object of a system according to an embodiment is to efficiently advance learning in an examination study.SOLUTION: A system includes a question setting part, a digitalization part, a plan creation part, a progress management part, and an evaluation part. The question setting unit analyzes the learning progress or the degree of understanding of the user, and sets an optimum question based on the analysis. The digitizing section digitizes the plurality of reference books and makes them accessible to the user when needed. The plan creation unit creates an optimal learning plan based on the user's goal or learning progress. The progress manager manages the user's learning progress in real-time and provides feedback as needed. The evaluation unit evaluates the learning outcome of the user and determines whether to proceed to the next step.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult to efficiently manage multiple reference books and create optimal study plans when studying for exams.

[0005] The system according to the embodiment aims to efficiently advance study for exams. [Means for solving the problem]

[0006] The system according to the embodiment includes a question setting unit, a digitizing unit, a plan creation unit, a progress management unit, and an evaluation unit. The question setting unit analyzes the user's learning progress or level of understanding and sets the most appropriate questions based on that analysis. The digitizing unit digitizes multiple reference books and allows the user to access them when needed. The plan creation unit creates the most appropriate study plan based on the user's goals or learning progress. The progress management unit manages the user's learning progress in real time and provides feedback as needed. The evaluation unit evaluates the user's learning outcomes and determines whether or not to proceed to the next step. [Effects of the Invention]

[0007] The system according to the embodiment allows students to study efficiently for exams. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An AI system according to an embodiment of the present invention is a system for efficiently and effectively studying for exams, eliminating the need to purchase and store reference books. This system uses AI to comprehensively manage everything from creating exam study questions to storing reference books, providing users with an optimal learning environment. This allows the AI ​​system to analyze a user's learning progress and comprehension, create optimal questions based on the results, digitize and store multiple reference books, and enable users to access them whenever needed. It can also create an optimal study plan based on the user's goals and learning progress, manage learning progress in real time, and provide feedback as needed. Furthermore, it can evaluate the user's learning outcomes and determine whether they should proceed to the next step.

[0029] The AI ​​system according to the embodiment includes a question setting unit, a digitization unit, a plan creation unit, a progress management unit, and an evaluation unit. The question setting unit analyzes a user's learning progress and level of understanding and presents optimal questions based on the results. For example, the question setting unit uses a generation AI to analyze the user's learning history and current learning status and presents questions related to weak areas. The question setting unit can also adjust the difficulty of the questions based on the user's learning progress. The digitization unit digitizes multiple reference books and allows the user to access them when needed. For example, the digitization unit scans reference books, converts them into digital data, and stores them in cloud storage. The digitization unit also allows the user to view the digital reference books on a smartphone or tablet. The plan creation unit creates an optimal study plan based on the user's goals and learning progress. For example, the plan creation unit uses a generation AI to analyze the user's goals and current learning status and proposes a study schedule that takes into account the period leading up to the exam date. The progress management unit manages the user's learning progress in real time and provides feedback as needed. For example, the progress management unit analyzes the user's learning history and current progress and sends a reminder if the user is not progressing as planned. The evaluation unit evaluates the user's learning outcomes and determines whether the user should proceed to the next step. For example, the evaluation unit analyzes the user's mock test results and learning history and suggests which areas require further study. As a result, the AI ​​system according to the embodiment eliminates the need to purchase and store reference books for exam preparation, allowing users to study efficiently and effectively. For example, users no longer need to purchase physical reference books, and can access digital reference books anytime, anywhere. Furthermore, AI creates study plans and manages progress, allowing users to study efficiently. Furthermore, AI evaluates learning outcomes and suggests next steps, allowing users to study effectively.

[0030] The question setting unit can analyze not only the user's learning history but also data from daily life to set optimal questions. For example, the question setting unit analyzes the user's sleep data and sets questions after the user has had a sufficient sleep. For example, based on sleep data obtained from a smartwatch, questions are set at the time of day when the user is most refreshed. The question setting unit also analyzes the user's dietary data and sets questions after a nutritionally balanced meal. For example, based on data obtained from a food log app, questions are set at the time of day when the user is most energetic. The question setting unit also analyzes the user's daily activity data and sets questions when the user is in a refreshed state after exercise. For example, based on exercise data obtained from a fitness tracker, questions are set at the time of day when the user can concentrate best. This allows the unit to set optimal questions that match the user's lifestyle.

[0031] The question setting unit can analyze the answering process of how the user answers the questions and provide feedback on how to improve the answering method. For example, the question setting unit records the user's answering process, and the generation AI analyzes the video to identify how to improve the answering method. For example, it analyzes hand movements and writing speed while answering and suggests an efficient answering method. The question setting unit also monitors the user's answering process in real time, and the generation AI analyzes the data to provide feedback. For example, it analyzes thought patterns while answering and suggests a more effective answering method. The question setting unit also analyzes the user's answer history and compares it with past answering methods to identify how to improve. For example, it can identify where the user wasted time based on past answer data and suggest an efficient answering method. This can improve the user's answering method and increase learning effectiveness.

[0032] The digitizing unit can customize the contents of the reference book according to the user's level of understanding and provide an individually optimized digital reference book. For example, the digitizing unit analyzes the user's study history and customizes the contents of the reference book according to the user's level of understanding. For example, it provides a digital reference book that focuses on areas of difficulty. The digitizing unit also generates reference book contents according to the user's level of understanding based on the user's answer data. For example, it provides a digital reference book with added explanations related to questions that the user got wrong. The digitizing unit also continuously customizes the contents of the reference book based on user feedback. For example, it provides a digital reference book with improvements to parts that the user found difficult to understand. This makes it possible to provide the optimal reference book according to the user's level of understanding.

[0033] The digitization unit provides a function that allows users to add notes and highlights to digitized reference books, and can analyze the data to understand learning trends. For example, the digitization unit adds a note function to digital reference books to allow users to take notes freely. For example, notes are added to important points, and the data is analyzed to understand learning trends. The digitization unit also adds a highlight function to digital reference books to allow users to highlight important parts. For example, the highlighted parts are analyzed to understand what the user values. The digitization unit also analyzes the notes and highlights added by the user to identify learning trends. For example, the user's learning trends are understood based on parts where notes are frequently taken or parts highlighted. This makes it possible to understand the user's learning trends and support effective learning.

[0034] The digitization unit can convert the contents of a reference book into audio or video format to support learning through different media. For example, the digitization unit can convert the contents of a digital reference book into audio format to allow users to learn auditorily. For example, the digitization unit can convert text into audio so that users can listen to it while commuting or exercising. The digitization unit can also convert the contents of a digital reference book into video format to allow users to learn visually. For example, important points can be explained in video format to provide a visually easy-to-understand format. The digitization unit can also provide reference books in audio or video format to allow users to study through different media. For example, the digitization unit can provide reference books in three formats - text, audio, and video - to allow users to choose the format that suits their learning style. This makes it possible to support learning through a variety of media that suits the user's learning style.

[0035] The digitization department can automatically translate reference books in different languages ​​to provide an international learning environment. For example, the digitization department builds a system that automatically translates digital reference books and provides them in different languages. For example, it provides reference books in multiple languages ​​such as English, Japanese, and Chinese. The digitization department also provides an international learning environment based on the automatically translated reference books. For example, it allows users of different languages ​​to use the same reference books. The digitization department also develops a system that uses an automatic translation function to provide reference books in different languages ​​in real time. For example, it instantly translates the contents of the reference books according to the language selected by the user. This makes it possible to provide a learning environment for users of different languages.

[0036] The digitization department can link with other learning resources to complement the content of reference books. For example, the digitization department will build a system that links digital reference books with online courses to complement learning content. For example, it will recommend online courses related to the content of reference books. The digitization department will also develop a system that links digital reference books with academic papers to deepen learning content. For example, it will provide papers related to the content of reference books to deepen learning understanding. The digitization department will also integrate digital reference books with other learning resources to provide a comprehensive learning environment. For example, it will provide reference books, online courses, and papers on a single platform, allowing users to study efficiently. This will allow learning content to be complemented by utilizing a variety of learning resources.

[0037] The plan creation unit can propose optimal study times by taking into account the user's lifestyle and daily schedule. The plan creation unit, for example, works with the user's calendar or schedule app to propose study times that take into account the user's daily schedule. For example, the plan creation unit can concentrate study time during times when the user has no important plans. The plan creation unit also analyzes the user's lifestyle and identifies optimal study times. For example, the plan creation unit can schedule study time during times when the user can concentrate best. The plan creation unit also proposes study times that fit the user's lifestyle based on the user's past data. For example, the plan creation unit can find the time period when the user can study most efficiently from their past study history and schedule study time during that time period. This makes it possible to propose optimal study times that fit the user's lifestyle.

[0038] The plan creation unit can collect user feedback on the study plan and continuously improve the plan. For example, the plan creation unit builds a system that collects user feedback on the study plan in real time and improves the plan based on the results. For example, it uses online surveys and comment functions. The plan creation unit also analyzes user feedback and identifies areas for improvement in the study plan. For example, it adjusts the study schedule based on the user's opinions. The plan creation unit also collects user real-time feedback on the study plan and develops a system in which the generation AI immediately suggests improvements. For example, it automatically modifies the study plan based on the feedback. This makes it possible to continuously improve the study plan based on user feedback.

[0039] The plan creation unit can use generation AI to create study plans according to different goals. For example, the plan creation unit sets the user's short-term and long-term goals and generates a study plan according to them. For example, a study plan is created that combines test preparation as a short-term goal and exam preparation as a long-term goal. The plan creation unit also generates study plans according to different goals to enable the user to study efficiently. For example, a study plan is created that combines submitting assignments as a short-term goal and obtaining qualifications as a long-term goal. The plan creation unit also builds a system that generates study plans according to the user's goals and manages progress. For example, the system monitors the progress of short-term and long-term goals in real time and adjusts the study plan. This makes it possible to create a study plan according to the user's short-term and long-term goals.

[0040] The plan creation unit can propose a study plan that references the success stories of other users. The plan creation unit, for example, collects the success stories of other users and builds a system that proposes study plans based on them. For example, it proposes a study plan that references the success stories of users who have the same goal. The plan creation unit also analyzes the study history of other users and generates a study plan based on the success stories. For example, it references the study plan of a user who achieved a high score in the same subject. The plan creation unit also identifies success stories based on feedback from other users and proposes a study plan based on them. For example, it proposes a study plan that incorporates a study method that has many success stories. This makes it possible to propose a study plan that references the success stories of other users.

[0041] The progress management unit can visualize learning progress and provide a dashboard that the user can intuitively understand. The progress management unit, for example, visualizes learning progress in graphs and charts and provides a dashboard that the user can intuitively understand. For example, it displays learning time and progress status in a graph. The progress management unit also updates learning progress in real time and provides a dashboard that allows the user to always check the latest information. For example, it displays learning progress status in real time. The progress management unit also provides a customizable dashboard for visualizing learning progress, allowing the user to freely check their own learning status. For example, it provides a dashboard that allows the user to select the information to display. This makes it possible to provide a dashboard that allows the user to intuitively understand their learning progress.

[0042] The progress management unit can automatically suggest what to study next based on the user's learning progress. The progress management unit, for example, analyzes the user's learning progress and builds a system that automatically suggests what to study next. For example, it prioritizes suggestions for areas that have not been studied yet or areas that the user is weak at. The progress management unit also suggests what to study next in real time based on the user's learning progress. For example, it immediately suggests what to study next based on the user's learning progress. The progress management unit also develops a system that continuously suggests what to study next based on the user's learning history. For example, it suggests what to study next based on past learning data. This makes it possible to automatically suggest what to study next based on the user's learning progress.

[0043] The progress management unit can synchronize learning progress across different devices, allowing users to continue learning anywhere. For example, the progress management unit builds a system that saves learning progress in the cloud and synchronizes it across different devices. For example, learning can be started on a smartphone and continued on a PC. The progress management unit also synchronizes learning progress in real time across different devices, allowing users to continue learning anywhere. For example, learning can be done on a tablet and progress can be checked on a smartphone. The progress management unit also provides a function to automatically synchronize learning progress, eliminating the need for users to manually synchronize. For example, a learning app automatically saves progress in the cloud and synchronizes it across different devices. This allows learning progress to be synchronized across different devices, allowing users to continue learning anywhere.

[0044] The progress management unit can provide a function for sharing progress with other users and promoting competition and cooperation. The progress management unit, for example, provides a function for sharing learning progress with other users and promoting competition and cooperation. For example, comparing learning progress with friends or classmates. The progress management unit also provides a function for cooperating with other users to advance learning by sharing learning progress. For example, sharing progress in group learning and working together to achieve goals. The progress management unit also provides a function for competing with other users and increasing motivation by sharing learning progress. For example, a ranking function can be introduced to compete against each other on learning progress. This allows users to share progress with other users and promote competition and cooperation, thereby increasing motivation to learn.

[0045] The evaluation unit evaluates learning outcomes from multiple angles, and can evaluate not only technical understanding but also application skills and creativity. The evaluation unit, for example, builds a system that evaluates learning outcomes not only technical understanding but also application skills and creativity. For example, it evaluates problem-solving ability and creative ideas. The evaluation unit also sets multiple evaluation criteria to evaluate learning outcomes from multiple angles. For example, it evaluates based on three criteria: technical understanding, application skills, and creativity. The evaluation unit also incorporates the opinions of experts to evaluate learning outcomes from multiple angles. For example, technical understanding is evaluated by an expert, and application skills and creativity are evaluated based on user feedback. In this way, the user's overall abilities can be evaluated by evaluating learning outcomes from multiple angles.

[0046] The evaluation unit can specifically suggest the next content and method to be studied based on the evaluation results. The evaluation unit, for example, analyzes the evaluation results and builds a system that specifically suggests the next content and method to be studied. For example, it suggests a method of focusing on studying weak areas. The evaluation unit also suggests the next content to be studied in real time based on the evaluation results. For example, it immediately suggests the next content to be studied based on the evaluation results. The evaluation unit also develops a system that continuously suggests the next content and method to be studied based on the evaluation results. For example, it suggests the next content and method to be studied based on past evaluation results. In this way, by specifically suggesting the next content and method to be studied based on the evaluation results, the user's learning effectiveness can be improved.

[0047] The evaluation unit can perform a comprehensive evaluation that combines different evaluation criteria. The evaluation unit, for example, builds a system that performs a comprehensive evaluation that combines quantitative evaluation and qualitative evaluation. For example, the evaluation combines test scores (quantitative evaluation) and user feedback (qualitative evaluation). The evaluation unit also combines different evaluation criteria to evaluate overall learning outcomes. For example, the evaluation combines technical understanding (quantitative evaluation) and creativity (qualitative evaluation). The evaluation unit also sets multiple evaluation criteria to perform a comprehensive evaluation and performs evaluation based on each criterion. For example, an evaluation system that incorporates both quantitative and qualitative evaluation is built. This makes it possible to perform a comprehensive evaluation that combines different evaluation criteria, thereby evaluating the user's learning outcomes from multiple angles.

[0048] The evaluation unit can provide a function that allows a user to compare their evaluation results with those of other users and understand their own position. The evaluation unit, for example, builds a system that provides a function that allows a user to compare their evaluation results with those of other users and understand their own position. For example, it compares their evaluation results with those of other users who took the same test. The evaluation unit also evaluates their own learning outcomes based on the evaluation results of other users. For example, it compares their evaluation results with those of users who achieved high scores in the same subject. The evaluation unit also provides a function that displays the evaluation results of other users in real time and allows a user to understand their own position. For example, it introduces a ranking function and compares learning outcomes. This allows a user to understand their own position by comparing their evaluation results with those of other users.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The question-setting unit can analyze not only the user's learning history but also data from daily life to provide optimal questions. For example, the question-setting unit can analyze the user's sleep data and provide questions after the user has had a sufficient amount of sleep. For example, based on sleep data obtained from a smartwatch, questions can be provided at the time of day when the user is most refreshed. The question-setting unit can also analyze the user's dietary data and provide questions after the user has had a nutritionally balanced meal. For example, based on data obtained from a food log app, questions can be provided at the time of day when the user is most energetic. The question-setting unit can also analyze the user's daily activity data and provide questions when the user is refreshed after exercise. For example, based on exercise data obtained from a fitness tracker, questions can be provided at the time of day when the user is most able to concentrate. This allows the unit to provide optimal questions tailored to the user's lifestyle.

[0051] The question-setting unit can analyze the answering process of how the user answers the questions and provide feedback on areas for improvement in the answering method. For example, the user's answering process can be recorded, and the generation AI can analyze the video to identify areas for improvement in the answering method. For example, the hand movements and writing speed during the answering process can be analyzed to suggest an efficient answering method. The question-setting unit can also monitor the user's answering process in real time, and the generation AI can analyze the data to provide feedback. For example, the unit can analyze the thought patterns during the answering process to suggest a more effective answering method. The question-setting unit can also analyze the user's answer history and compare it with past answering methods to identify areas for improvement. For example, the unit can identify areas where the user wasted time based on past answer data and suggest an efficient answering method. This can improve the user's answering method and increase learning effectiveness.

[0052] The digitization unit can customize the contents of the reference book according to the user's level of understanding and provide an individually optimized digital reference book. For example, it analyzes the user's study history and customizes the contents of the reference book according to the user's level of understanding. For example, it provides a digital reference book that focuses on areas of difficulty. The digitization unit also generates reference book contents according to the user's level of understanding based on the user's answer data. For example, it provides a digital reference book with added explanations related to questions that the user got wrong. The digitization unit also continuously customizes the contents of the reference book based on user feedback. For example, it provides a digital reference book with improvements to parts that the user found difficult to understand. This makes it possible to provide the optimal reference book according to the user's level of understanding.

[0053] The digitization unit provides a function that allows users to add notes and highlights to digitized reference books, and can analyze the data to understand learning trends. For example, a note function can be added to a digital reference book to allow users to take notes freely. For example, notes can be added to important points, and the data can be analyzed to understand learning trends. The digitization unit can also add a highlight function to a digital reference book to allow users to highlight important parts. For example, the highlighted parts can be analyzed to understand what the user values. The digitization unit can also analyze the notes and highlights added by the user to identify learning trends. For example, the user's learning trends can be understood based on the parts where notes are frequently taken and the parts highlighted. This makes it possible to understand the user's learning trends and support effective learning.

[0054] The digitization unit can convert the contents of a reference book into audio or video format to support learning through different media. For example, the content of a digital reference book can be converted into audio format to allow users to learn auditorily. For example, text can be converted into audio so that users can listen to it while commuting or exercising. The digitization unit can also convert the content of a digital reference book into video format to allow users to learn visually. For example, important points can be explained in video format to provide a visually easy-to-understand format. The digitization unit can also provide reference books in audio or video format to allow users to learn through different media. For example, reference books can be provided in three formats - text, audio, and video - so that users can choose the format that suits their learning style. This makes it possible to support learning through a variety of media that suits the user's learning style.

[0055] The digitization department can provide an international learning environment by automatically translating reference books in different languages. For example, it will build a system that automatically translates digital reference books and provides them in different languages. For example, it will provide reference books in multiple languages, such as English, Japanese, and Chinese. The digitization department will also provide an international learning environment based on the automatically translated reference books. For example, it will allow users of different languages ​​to use the same reference books. The digitization department will also develop a system that uses an automatic translation function to provide reference books in different languages ​​in real time. For example, it will instantly translate the contents of the reference books according to the language selected by the user. This will allow a learning environment to be provided for users of different languages.

[0056] The digitization department can link with other learning resources to complement the content of reference books. For example, it will build a system that links digital reference books with online courses to complement learning content. For example, it will recommend online courses related to the content of reference books. The digitization department will also develop a system that links digital reference books with academic papers to deepen learning content. For example, it will provide papers related to the content of reference books to deepen learning understanding. The digitization department will also integrate digital reference books with other learning resources to provide a comprehensive learning environment. For example, it will provide reference books, online courses, and papers on a single platform, allowing users to study efficiently. This will allow users to complement learning content by utilizing a variety of learning resources.

[0057] The plan creation unit can propose optimal study times by taking into account the user's lifestyle and daily schedule. For example, it can link with the user's calendar or schedule app to propose study times that take into account the user's daily schedule. For example, it can concentrate study time during times when there are no important plans. The plan creation unit can also analyze the user's lifestyle and identify optimal study times. For example, it can schedule study time during times when the user can concentrate best. The plan creation unit can also propose study times that fit the user's lifestyle based on the user's past data. For example, it can find the time period when the user can study most efficiently from their past study history and schedule study time during that time period. This makes it possible to propose optimal study times that fit the user's lifestyle.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The question generator analyzes the user's learning progress and level of understanding, and based on that, presents the most appropriate questions. For example, the question generator uses a generation AI to analyze the user's learning history and current learning situation, and presents questions related to areas in which the user is weak. The question generator can also adjust the difficulty of the questions according to the user's learning progress. Step 2: The digitization department digitizes multiple reference books so that users can access them whenever they need. For example, the digitization department scans the reference books, converts them into digital data, and stores them in cloud storage. The digitization department also allows users to view the digital reference books on their smartphones or tablets. Step 3: The planning unit creates an optimal study plan based on the user's goals and learning progress. For example, the planning unit uses generation AI to analyze the user's goals and current learning situation and proposes a study schedule that takes into account the time until the exam date. Step 4: The progress management unit manages the user's learning progress in real time and provides feedback as needed. For example, the progress management unit analyzes the user's learning history and current progress, and sends reminders if the user is not progressing as planned. Step 5: The evaluator evaluates the user's learning outcomes and determines whether to proceed to the next step. For example, the evaluator analyzes the user's practice test results and learning history and suggests areas where further study is needed.

[0060] (Example 2) An AI system according to an embodiment of the present invention is a system for efficiently and effectively studying for exams, eliminating the need to purchase and store reference books. This system uses AI to comprehensively manage everything from creating exam study questions to storing reference books, providing users with an optimal learning environment. This allows the AI ​​system to analyze a user's learning progress and comprehension, create optimal questions based on the results, digitize and store multiple reference books, and enable users to access them whenever needed. It can also create an optimal study plan based on the user's goals and learning progress, manage learning progress in real time, and provide feedback as needed. Furthermore, it can evaluate the user's learning outcomes and determine whether they should proceed to the next step.

[0061] The AI ​​system according to the embodiment includes a question setting unit, a digitization unit, a plan creation unit, a progress management unit, and an evaluation unit. The question setting unit analyzes a user's learning progress and level of understanding and presents optimal questions based on the results. For example, the question setting unit uses a generation AI to analyze the user's learning history and current learning status and presents questions related to weak areas. The question setting unit can also adjust the difficulty of the questions based on the user's learning progress. The digitization unit digitizes multiple reference books and allows the user to access them when needed. For example, the digitization unit scans reference books, converts them into digital data, and stores them in cloud storage. The digitization unit also allows the user to view the digital reference books on a smartphone or tablet. The plan creation unit creates an optimal study plan based on the user's goals and learning progress. For example, the plan creation unit uses a generation AI to analyze the user's goals and current learning status and proposes a study schedule that takes into account the period leading up to the exam date. The progress management unit manages the user's learning progress in real time and provides feedback as needed. For example, the progress management unit analyzes the user's learning history and current progress and sends a reminder if the user is not progressing as planned. The evaluation unit evaluates the user's learning outcomes and determines whether the user should proceed to the next step. For example, the evaluation unit analyzes the user's mock test results and learning history and suggests which areas require further study. As a result, the AI ​​system according to the embodiment eliminates the need to purchase and store reference books for exam preparation, allowing users to study efficiently and effectively. For example, users no longer need to purchase physical reference books, and can access digital reference books anytime, anywhere. Furthermore, AI creates study plans and manages progress, allowing users to study efficiently. Furthermore, AI evaluates learning outcomes and suggests next steps, allowing users to study effectively.

[0062] The question setting unit can estimate the user's emotional state and present questions at times when stress is low. The question setting unit, for example, analyzes the user's facial expressions and voice tone to estimate the emotional state in real time. For example, it measures the user's stress level using a camera or microphone and presents questions at times when stress is low. The question setting unit also collects the user's biometric data (heart rate and electrodermal activity) to estimate the emotional state. For example, it monitors the user's stress level using a wearable device and presents questions when the user is relaxed. The question setting unit also analyzes the user's past learning data to identify time periods when stress is low. For example, it finds the time periods when the user can concentrate best from past learning history and presents questions at those time periods. This reduces the user's stress and promotes effective learning.

[0063] The question setting unit can analyze not only the user's learning history but also data from daily life to set optimal questions. For example, the question setting unit analyzes the user's sleep data and sets questions after the user has had a sufficient sleep. For example, based on sleep data obtained from a smartwatch, questions are set at the time of day when the user is most refreshed. The question setting unit also analyzes the user's dietary data and sets questions after a nutritionally balanced meal. For example, based on data obtained from a food log app, questions are set at the time of day when the user is most energetic. The question setting unit also analyzes the user's daily activity data and sets questions when the user is in a refreshed state after exercise. For example, based on exercise data obtained from a fitness tracker, questions are set at the time of day when the user can concentrate best. This allows the unit to set optimal questions that match the user's lifestyle.

[0064] The question setting unit can analyze the answering process of how the user answers the questions and provide feedback on how to improve the answering method. For example, the question setting unit records the user's answering process, and the generation AI analyzes the video to identify how to improve the answering method. For example, it analyzes hand movements and writing speed while answering and suggests an efficient answering method. The question setting unit also monitors the user's answering process in real time, and the generation AI analyzes the data to provide feedback. For example, it analyzes thought patterns while answering and suggests a more effective answering method. The question setting unit also analyzes the user's answer history and compares it with past answering methods to identify how to improve. For example, it can identify where the user wasted time based on past answer data and suggest an efficient answering method. This can improve the user's answering method and increase learning effectiveness.

[0065] The digitizing unit can customize the contents of the reference book according to the user's level of understanding and provide an individually optimized digital reference book. For example, the digitizing unit analyzes the user's study history and customizes the contents of the reference book according to the user's level of understanding. For example, it provides a digital reference book that focuses on areas of difficulty. The digitizing unit also generates reference book contents according to the user's level of understanding based on the user's answer data. For example, it provides a digital reference book with added explanations related to questions that the user got wrong. The digitizing unit also continuously customizes the contents of the reference book based on user feedback. For example, it provides a digital reference book with improvements to parts that the user found difficult to understand. This makes it possible to provide the optimal reference book according to the user's level of understanding.

[0066] The digitization unit provides a function that allows users to add notes and highlights to digitized reference books, and can analyze the data to understand learning trends. For example, the digitization unit adds a note function to digital reference books to allow users to take notes freely. For example, notes are added to important points, and the data is analyzed to understand learning trends. The digitization unit also adds a highlight function to digital reference books to allow users to highlight important parts. For example, the highlighted parts are analyzed to understand what the user values. The digitization unit also analyzes the notes and highlights added by the user to identify learning trends. For example, the user's learning trends are understood based on parts where notes are frequently taken or parts highlighted. This makes it possible to understand the user's learning trends and support effective learning.

[0067] The digitization unit can convert the contents of a reference book into audio or video format to support learning through different media. For example, the digitization unit can convert the contents of a digital reference book into audio format to allow users to learn auditorily. For example, the digitization unit can convert text into audio so that users can listen to it while commuting or exercising. The digitization unit can also convert the contents of a digital reference book into video format to allow users to learn visually. For example, important points can be explained in video format to provide a visually easy-to-understand format. The digitization unit can also provide reference books in audio or video format to allow users to study through different media. For example, the digitization unit can provide reference books in three formats - text, audio, and video - to allow users to choose the format that suits their learning style. This makes it possible to support learning through a variety of media that suits the user's learning style.

[0068] The digitization department can automatically translate reference books in different languages ​​to provide an international learning environment. For example, the digitization department builds a system that automatically translates digital reference books and provides them in different languages. For example, it provides reference books in multiple languages ​​such as English, Japanese, and Chinese. The digitization department also provides an international learning environment based on the automatically translated reference books. For example, it allows users of different languages ​​to use the same reference books. The digitization department also develops a system that uses an automatic translation function to provide reference books in different languages ​​in real time. For example, it instantly translates the contents of the reference books according to the language selected by the user. This makes it possible to provide a learning environment for users of different languages.

[0069] The digitization department can link with other learning resources to complement the content of reference books. For example, the digitization department will build a system that links digital reference books with online courses to complement learning content. For example, it will recommend online courses related to the content of reference books. The digitization department will also develop a system that links digital reference books with academic papers to deepen learning content. For example, it will provide papers related to the content of reference books to deepen learning understanding. The digitization department will also integrate digital reference books with other learning resources to provide a comprehensive learning environment. For example, it will provide reference books, online courses, and papers on a single platform, allowing users to study efficiently. This will allow learning content to be complemented by utilizing a variety of learning resources.

[0070] The digitization unit can use the emotion estimation function to present the contents of the reference book at the time when the user is most relaxed. The digitization unit, for example, analyzes the user's emotional state and presents the contents of the reference book at the time when the user is relaxed. For example, the content of the reference book is notified when the user is relaxed. The digitization unit also identifies the time when the user is relaxed based on the user's biometric data. For example, it analyzes the heart rate and electrodermal activity and provides the content of the reference book when the user is relaxed. The digitization unit also analyzes the user's past data and identifies the time period when the user is relaxed. For example, it finds the time period when the user is most relaxed from the past data and presents the content of the reference book at that time period. This makes it possible to promote effective learning by presenting study content when the user is relaxed.

[0071] The plan creation unit can estimate the user's emotional state and create a low-stress study schedule. The plan creation unit, for example, analyzes the user's emotional state in real time and sets a study schedule for low-stress time periods. For example, the plan creation unit focuses study on time periods when the user is relaxed. The plan creation unit also identifies low-stress time periods based on the user's biometric data and sets a study schedule for those time periods. For example, the plan creation unit analyzes the user's heart rate and electrodermal activity and focuses study on time periods when the user is relaxed. The plan creation unit also analyzes the user's past data, identifies low-stress time periods, and sets a study schedule for those time periods. For example, the plan creation unit finds the time periods when the user is most relaxed from past study history and focuses study on those time periods. This reduces the user's stress and creates an effective study schedule.

[0072] The plan creation unit can propose optimal study times by taking into account the user's lifestyle and daily schedule. The plan creation unit, for example, works with the user's calendar or schedule app to propose study times that take into account the user's daily schedule. For example, the plan creation unit can concentrate study time during times when the user has no important plans. The plan creation unit also analyzes the user's lifestyle and identifies optimal study times. For example, the plan creation unit can schedule study time during times when the user can concentrate best. The plan creation unit also proposes study times that fit the user's lifestyle based on the user's past data. For example, the plan creation unit can find the time period when the user can study most efficiently from their past study history and schedule study time during that time period. This makes it possible to propose optimal study times that fit the user's lifestyle.

[0073] The plan creation unit can collect user feedback on the study plan and continuously improve the plan. For example, the plan creation unit builds a system that collects user feedback on the study plan in real time and improves the plan based on the results. For example, it uses online surveys and comment functions. The plan creation unit also analyzes user feedback and identifies areas for improvement in the study plan. For example, it adjusts the study schedule based on the user's opinions. The plan creation unit also collects user real-time feedback on the study plan and develops a system in which the generation AI immediately suggests improvements. For example, it automatically modifies the study plan based on the feedback. This makes it possible to continuously improve the study plan based on user feedback.

[0074] The plan creation unit can use generation AI to create study plans according to different goals. For example, the plan creation unit sets the user's short-term and long-term goals and generates a study plan according to them. For example, a study plan is created that combines test preparation as a short-term goal and exam preparation as a long-term goal. The plan creation unit also generates study plans according to different goals to enable the user to study efficiently. For example, a study plan is created that combines submitting assignments as a short-term goal and obtaining qualifications as a long-term goal. The plan creation unit also builds a system that generates study plans according to the user's goals and manages progress. For example, the system monitors the progress of short-term and long-term goals in real time and adjusts the study plan. This makes it possible to create a study plan according to the user's short-term and long-term goals.

[0075] The plan creation unit can propose a study plan that references the success stories of other users. The plan creation unit, for example, collects the success stories of other users and builds a system that proposes study plans based on them. For example, it proposes a study plan that references the success stories of users who have the same goal. The plan creation unit also analyzes the study history of other users and generates a study plan based on the success stories. For example, it references the study plan of a user who achieved a high score in the same subject. The plan creation unit also identifies success stories based on feedback from other users and proposes a study plan based on them. For example, it proposes a study plan that incorporates a study method that has many success stories. This makes it possible to propose a study plan that references the success stories of other users.

[0076] The plan creation unit can use the emotion estimation function to present a study plan at a time when the user feels most motivated. The plan creation unit, for example, analyzes the user's emotional state and presents a study plan at a time when the user is most motivated. For example, the plan creation unit notifies the user of a study plan when the user is feeling positive. The plan creation unit also identifies a time when the user is most motivated based on the user's biometric data and presents a study plan at that time. For example, the plan creation unit analyzes the user's heart rate and electrodermal activity and notifies the user of a study plan when the user is most motivated. The plan creation unit also analyzes the user's past data to identify time periods when the user is most motivated and presents a study plan for that time period. For example, the plan creation unit finds a time period when the user feels most motivated from the user's past study history and notifies the user of a study plan for that time period. This allows the study plan to be presented at a time when the user feels most motivated.

[0077] The progress management unit can estimate the user's emotional state and send an encouraging message when motivation is low. The progress management unit, for example, analyzes the user's emotional state in real time and sends an encouraging message when motivation is low. For example, an encouraging message is sent when the user is feeling stressed. The progress management unit also identifies times when motivation is low based on the user's biometric data and sends an encouraging message at that time. For example, it analyzes heart rate and electrodermal activity and sends an encouraging message when motivation is low. The progress management unit also analyzes the user's past data to identify time periods when motivation is likely to drop and sends an encouraging message at that time period. For example, it finds time periods when the user is likely to lose motivation from past learning history and sends an encouraging message at that time period. In this way, by sending an encouraging message when the user's motivation is low, it is possible to maintain the user's motivation to study.

[0078] The progress management unit can visualize learning progress and provide a dashboard that the user can intuitively understand. The progress management unit, for example, visualizes learning progress in graphs and charts and provides a dashboard that the user can intuitively understand. For example, it displays learning time and progress status in a graph. The progress management unit also updates learning progress in real time and provides a dashboard that allows the user to always check the latest information. For example, it displays learning progress status in real time. The progress management unit also provides a customizable dashboard for visualizing learning progress, allowing the user to freely check their own learning status. For example, it provides a dashboard that allows the user to select the information to display. This makes it possible to provide a dashboard that allows the user to intuitively understand their learning progress.

[0079] The progress management unit can automatically suggest what to study next based on the user's learning progress. The progress management unit, for example, analyzes the user's learning progress and builds a system that automatically suggests what to study next. For example, it prioritizes suggestions for areas that have not been studied yet or areas that the user is weak at. The progress management unit also suggests what to study next in real time based on the user's learning progress. For example, it immediately suggests what to study next based on the user's learning progress. The progress management unit also develops a system that continuously suggests what to study next based on the user's learning history. For example, it suggests what to study next based on past learning data. This makes it possible to automatically suggest what to study next based on the user's learning progress.

[0080] The progress management unit can synchronize learning progress across different devices, allowing users to continue learning anywhere. For example, the progress management unit builds a system that saves learning progress in the cloud and synchronizes it across different devices. For example, learning can be started on a smartphone and continued on a PC. The progress management unit also synchronizes learning progress in real time across different devices, allowing users to continue learning anywhere. For example, learning can be done on a tablet and progress can be checked on a smartphone. The progress management unit also provides a function to automatically synchronize learning progress, eliminating the need for users to manually synchronize. For example, a learning app automatically saves progress in the cloud and synchronizes it across different devices. This allows learning progress to be synchronized across different devices, allowing users to continue learning anywhere.

[0081] The progress management unit can provide a function for sharing progress with other users and promoting competition and cooperation. The progress management unit, for example, provides a function for sharing learning progress with other users and promoting competition and cooperation. For example, comparing learning progress with friends or classmates. The progress management unit also provides a function for cooperating with other users to advance learning by sharing learning progress. For example, sharing progress in group learning and working together to achieve goals. The progress management unit also provides a function for competing with other users and increasing motivation by sharing learning progress. For example, a ranking function can be introduced to compete against each other on learning progress. This allows users to share progress with other users and promote competition and cooperation, thereby increasing motivation to learn.

[0082] The progress management unit can use the emotion estimation function to report progress at the timing when the user feels the most sense of accomplishment. For example, the progress management unit analyzes the user's emotional state and reports learning progress at the timing when the user feels a sense of accomplishment. For example, the progress management unit notifies the user of progress when the user is feeling positive emotions. The progress management unit also identifies the timing when the user feels a sense of accomplishment based on the user's biometric data and reports learning progress at that timing. For example, the progress management unit analyzes the user's heart rate and electrodermal activity and notifies the user of progress when the user feels a sense of accomplishment. The progress management unit also analyzes the user's past data to identify time periods when the user is likely to feel a sense of accomplishment and reports learning progress at those time periods. For example, the progress management unit finds time periods when the user is likely to feel a sense of accomplishment from the user's past learning history and notifies the user of progress at those time periods. In this way, by reporting progress at the timing when the user feels the most sense of accomplishment, the user's motivation to learn can be increased.

[0083] The evaluation unit can estimate the user's emotional state and make an evaluation that emphasizes positive feedback. The evaluation unit, for example, analyzes the user's emotional state in real time and makes an evaluation that emphasizes positive feedback. For example, it notifies the user of an evaluation when the user is feeling positive. The evaluation unit also identifies the timing to emphasize positive feedback based on the user's biometric data and makes an evaluation at that timing. For example, it analyzes the user's heart rate and electrodermal activity and notifies the user of an evaluation when the user is feeling positive. The evaluation unit also analyzes the user's past data and identifies the timing to emphasize positive feedback and makes an evaluation at that timing. For example, it finds a time period when the user is feeling positive from the user's past learning history and notifies the user of an evaluation at that time period. In this way, it is possible to increase the user's motivation to learn by emphasizing positive feedback according to the user's emotional state.

[0084] The evaluation unit evaluates learning outcomes from multiple angles, and can evaluate not only technical understanding but also application skills and creativity. The evaluation unit, for example, builds a system that evaluates learning outcomes not only technical understanding but also application skills and creativity. For example, it evaluates problem-solving ability and creative ideas. The evaluation unit also sets multiple evaluation criteria to evaluate learning outcomes from multiple angles. For example, it evaluates based on three criteria: technical understanding, application skills, and creativity. The evaluation unit also incorporates the opinions of experts to evaluate learning outcomes from multiple angles. For example, technical understanding is evaluated by an expert, and application skills and creativity are evaluated based on user feedback. In this way, the user's overall abilities can be evaluated by evaluating learning outcomes from multiple angles.

[0085] The evaluation unit can specifically suggest the next content and method to be studied based on the evaluation results. The evaluation unit, for example, analyzes the evaluation results and builds a system that specifically suggests the next content and method to be studied. For example, it suggests a method of focusing on studying weak areas. The evaluation unit also suggests the next content to be studied in real time based on the evaluation results. For example, it immediately suggests the next content to be studied based on the evaluation results. The evaluation unit also develops a system that continuously suggests the next content and method to be studied based on the evaluation results. For example, it suggests the next content and method to be studied based on past evaluation results. In this way, by specifically suggesting the next content and method to be studied based on the evaluation results, the user's learning effectiveness can be improved.

[0086] The evaluation unit can perform a comprehensive evaluation that combines different evaluation criteria. The evaluation unit, for example, builds a system that performs a comprehensive evaluation that combines quantitative evaluation and qualitative evaluation. For example, the evaluation combines test scores (quantitative evaluation) and user feedback (qualitative evaluation). The evaluation unit also combines different evaluation criteria to evaluate overall learning outcomes. For example, the evaluation combines technical understanding (quantitative evaluation) and creativity (qualitative evaluation). The evaluation unit also sets multiple evaluation criteria to perform a comprehensive evaluation and performs evaluation based on each criterion. For example, an evaluation system that incorporates both quantitative and qualitative evaluation is built. This makes it possible to perform a comprehensive evaluation that combines different evaluation criteria, thereby evaluating the user's learning outcomes from multiple angles.

[0087] The evaluation unit can provide a function that allows a user to compare their evaluation results with those of other users and understand their own position. The evaluation unit, for example, builds a system that provides a function that allows a user to compare their evaluation results with those of other users and understand their own position. For example, it compares their evaluation results with those of other users who took the same test. The evaluation unit also evaluates their own learning outcomes based on the evaluation results of other users. For example, it compares their evaluation results with those of users who achieved high scores in the same subject. The evaluation unit also provides a function that displays the evaluation results of other users in real time and allows a user to understand their own position. For example, it introduces a ranking function and compares learning outcomes. This allows a user to understand their own position by comparing their evaluation results with those of other users.

[0088] The evaluation unit can use the emotion estimation function to present the evaluation result at the timing when the user is most receptive. The evaluation unit, for example, analyzes the user's emotional state and presents the evaluation result at the timing when the user is most receptive. For example, the evaluation unit notifies the user of the evaluation result when the user is relaxed. The evaluation unit also identifies the timing when the user is most receptive based on the user's biometric data and presents the evaluation result at that timing. For example, the evaluation unit analyzes the user's heart rate and electrodermal activity and notifies the user of the evaluation result when the user is relaxed. The evaluation unit also analyzes the user's past data to identify the time period when the user is most receptive and presents the evaluation result at that time period. For example, the evaluation unit finds the time period when the user is relaxed from the user's past learning history and notifies the user of the evaluation result at that time period. In this way, by presenting the evaluation result at the timing when the user is most receptive, it is possible to increase motivation to learn.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The question-setting unit can analyze not only the user's learning history but also data from daily life to provide optimal questions. For example, the question-setting unit can analyze the user's sleep data and provide questions after the user has had a sufficient amount of sleep. For example, based on sleep data obtained from a smartwatch, questions can be provided at the time of day when the user is most refreshed. The question-setting unit can also analyze the user's dietary data and provide questions after the user has had a nutritionally balanced meal. For example, based on data obtained from a food log app, questions can be provided at the time of day when the user is most energetic. The question-setting unit can also analyze the user's daily activity data and provide questions when the user is refreshed after exercise. For example, based on exercise data obtained from a fitness tracker, questions can be provided at the time of day when the user is most able to concentrate. This allows the unit to provide optimal questions tailored to the user's lifestyle.

[0091] The question-setting unit can analyze the answering process of how the user answers the questions and provide feedback on areas for improvement in the answering method. For example, the user's answering process can be recorded, and the generation AI can analyze the video to identify areas for improvement in the answering method. For example, the hand movements and writing speed during the answering process can be analyzed to suggest an efficient answering method. The question-setting unit can also monitor the user's answering process in real time, and the generation AI can analyze the data to provide feedback. For example, the unit can analyze the thought patterns during the answering process to suggest a more effective answering method. The question-setting unit can also analyze the user's answer history and compare it with past answering methods to identify areas for improvement. For example, the unit can identify areas where the user wasted time based on past answer data and suggest an efficient answering method. This can improve the user's answering method and increase learning effectiveness.

[0092] The digitization unit can customize the contents of the reference book according to the user's level of understanding and provide an individually optimized digital reference book. For example, it analyzes the user's study history and customizes the contents of the reference book according to the user's level of understanding. For example, it provides a digital reference book that focuses on areas of difficulty. The digitization unit also generates reference book contents according to the user's level of understanding based on the user's answer data. For example, it provides a digital reference book with added explanations related to questions that the user got wrong. The digitization unit also continuously customizes the contents of the reference book based on user feedback. For example, it provides a digital reference book with improvements to parts that the user found difficult to understand. This makes it possible to provide the optimal reference book according to the user's level of understanding.

[0093] The digitization unit provides a function that allows users to add notes and highlights to digitized reference books, and can analyze the data to understand learning trends. For example, a note function can be added to a digital reference book to allow users to take notes freely. For example, notes can be added to important points, and the data can be analyzed to understand learning trends. The digitization unit can also add a highlight function to a digital reference book to allow users to highlight important parts. For example, the highlighted parts can be analyzed to understand what the user values. The digitization unit can also analyze the notes and highlights added by the user to identify learning trends. For example, the user's learning trends can be understood based on the parts where notes are frequently taken and the parts highlighted. This makes it possible to understand the user's learning trends and support effective learning.

[0094] The digitization unit can convert the contents of a reference book into audio or video format to support learning through different media. For example, the content of a digital reference book can be converted into audio format to allow users to learn auditorily. For example, text can be converted into audio so that users can listen to it while commuting or exercising. The digitization unit can also convert the content of a digital reference book into video format to allow users to learn visually. For example, important points can be explained in video format to provide a visually easy-to-understand format. The digitization unit can also provide reference books in audio or video format to allow users to learn through different media. For example, reference books can be provided in three formats - text, audio, and video - so that users can choose the format that suits their learning style. This makes it possible to support learning through a variety of media that suits the user's learning style.

[0095] The digitization department can provide an international learning environment by automatically translating reference books in different languages. For example, it will build a system that automatically translates digital reference books and provides them in different languages. For example, it will provide reference books in multiple languages, such as English, Japanese, and Chinese. The digitization department will also provide an international learning environment based on the automatically translated reference books. For example, it will allow users of different languages ​​to use the same reference books. The digitization department will also develop a system that uses an automatic translation function to provide reference books in different languages ​​in real time. For example, it will instantly translate the contents of the reference books according to the language selected by the user. This will allow a learning environment to be provided for users of different languages.

[0096] The digitization department can link with other learning resources to complement the content of reference books. For example, it will build a system that links digital reference books with online courses to complement learning content. For example, it will recommend online courses related to the content of reference books. The digitization department will also develop a system that links digital reference books with academic papers to deepen learning content. For example, it will provide papers related to the content of reference books to deepen learning understanding. The digitization department will also integrate digital reference books with other learning resources to provide a comprehensive learning environment. For example, it will provide reference books, online courses, and papers on a single platform, allowing users to study efficiently. This will allow users to complement learning content by utilizing a variety of learning resources.

[0097] The digitization unit can use the emotion estimation function to present the contents of the reference book when the user is most relaxed. For example, the digitization unit analyzes the user's emotional state and presents the contents of the reference book when the user is relaxed. For example, the digitization unit notifies the user of the contents of the reference book when the user is relaxed. The digitization unit also identifies the times when the user is relaxed based on the user's biometric data. For example, the digitization unit analyzes the user's heart rate and electrodermal activity and provides the contents of the reference book when the user is relaxed. The digitization unit also analyzes the user's past data and identifies the time periods when the user is relaxed. For example, the digitization unit finds the time periods when the user is most relaxed from the past data and presents the contents of the reference book during those time periods. This makes it possible to promote effective learning by presenting study content when the user is relaxed.

[0098] The plan creation unit can estimate the user's emotional state and create a low-stress study schedule. For example, the plan creation unit can analyze the user's emotional state in real time and set a study schedule for low-stress time periods. For example, the plan creation unit can focus study time periods when the user is relaxed. The plan creation unit can also identify low-stress time periods based on the user's biometric data and set a study schedule for those time periods. For example, the plan creation unit can analyze the user's heart rate and electrodermal activity and focus study time periods when the user is relaxed. The plan creation unit can also analyze the user's past data to identify low-stress time periods and set a study schedule for those time periods. For example, the plan creation unit can find the time periods when the user is most relaxed from past study history and focus study time periods. This can reduce the user's stress and create an effective study schedule.

[0099] The plan creation unit can propose optimal study times by taking into account the user's lifestyle and daily schedule. For example, it can link with the user's calendar or schedule app to propose study times that take into account the user's daily schedule. For example, it can concentrate study time during times when there are no important plans. The plan creation unit can also analyze the user's lifestyle and identify optimal study times. For example, it can schedule study time during times when the user can concentrate best. The plan creation unit can also propose study times that fit the user's lifestyle based on the user's past data. For example, it can find the time period when the user can study most efficiently from their past study history and schedule study time during that time period. This makes it possible to propose optimal study times that fit the user's lifestyle.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The question generator analyzes the user's learning progress and level of understanding, and based on that, presents the most appropriate questions. For example, the question generator uses a generation AI to analyze the user's learning history and current learning situation, and presents questions related to areas in which the user is weak. The question generator can also adjust the difficulty of the questions according to the user's learning progress. Step 2: The digitization department digitizes multiple reference books so that users can access them whenever they need. For example, the digitization department scans the reference books, converts them into digital data, and stores them in cloud storage. The digitization department also allows users to view the digital reference books on their smartphones or tablets. Step 3: The planning unit creates an optimal study plan based on the user's goals and learning progress. For example, the planning unit uses generation AI to analyze the user's goals and current learning situation and proposes a study schedule that takes into account the time until the exam date. Step 4: The progress management unit manages the user's learning progress in real time and provides feedback as needed. For example, the progress management unit analyzes the user's learning history and current progress, and sends reminders if the user is not progressing as planned. Step 5: The evaluator evaluates the user's learning outcomes and determines whether to proceed to the next step. For example, the evaluator analyzes the user's practice test results and learning history and suggests areas where further study is needed.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

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

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

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0143] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0153] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0154] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0155] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0159] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0160] 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.

[0161] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0162] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0163] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0164] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0166] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0167] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0168] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a question setting unit that analyzes the user's learning progress or understanding level and sets optimal questions based on the analysis; a digitization department that digitizes multiple reference books and allows users to access them when needed; a planning unit that creates an optimal learning plan based on the user's goals or learning progress; A progress management unit that manages the user's learning progress in real time and provides feedback as needed; An evaluation unit that evaluates the user's learning results and determines whether or not to proceed to the next step. A system characterized by:

2. The question setting unit The emotional state of the user is estimated, and questions are presented at a time when the user is least stressed.

2. The system of claim 1.

3. The question setting unit Analyze the user's daily life data as well as their learning history to provide the most appropriate questions.

2. The system of claim 1.

4. The question setting unit The answering process of the user is analyzed to determine how the user answered the questions, and feedback is provided on improvements to the answering method.

2. The system of claim 1.

5. The digitizing unit The contents of the reference book are customized according to the user's level of understanding, and an individually optimized digital reference book is provided.

2. The system of claim 1.

6. The digitizing unit Providing a function that enables the user to add notes and highlights to the digitized reference book, and analyzing the data to understand learning trends 2. The system of claim 1.

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