System

Abstract

An object of a system according to an embodiment is to provide an optimum study plan in consideration of a proficiency level of a user and the number of remaining days to an examination day.SOLUTION: A system according to an embodiment includes a learning plan generation unit, a learning tool provision unit, a proficiency degree evaluation unit, and a learning advice unit. The learning plan generation unit receives the current proficiency level of the user and the number of remaining days until the examination date as input information, and automatically generates an optimal learning plan based on the input information. The learning tool providing unit provides various tools for supporting learning of the user. The learning level evaluation unit periodically evaluates the learning progress of the user and feeds back the result. The learning advice unit provides optimal learning advice to the user in consideration of the number of remaining days until the test day.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 of making it difficult to automatically generate an efficient study plan that takes into account the user's level of proficiency and the number of days remaining until the exam date.

[0005] The system according to the embodiment aims to provide an optimal study plan taking into consideration the user's level of proficiency and the number of days remaining until the exam date. [Means for solving the problem]

[0006] The system according to the embodiment includes a study plan generation unit, a study tool provision unit, a proficiency assessment unit, and a study advice unit. The study plan generation unit receives input information such as the user's current proficiency and the number of days remaining until the exam date, and automatically generates an optimal study plan based on this information. The study tool provision unit provides various tools to support the user's study. The proficiency assessment unit periodically assesses the user's study progress and provides feedback on the results. The study advice unit provides the user with optimal study advice, taking into account the number of days remaining until the exam date. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal study plan taking into consideration the user's level of proficiency and the number of days remaining until the exam date. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A qualification acquisition support system according to an embodiment of the present invention is a system that efficiently supports users in obtaining qualifications. This system provides study advice that takes into account the user's level of proficiency and the number of days remaining until the exam date. This allows the qualification acquisition support system to efficiently support users in obtaining qualifications.

[0029] A qualification acquisition support system according to an embodiment includes a study plan generation unit, a study tool provision unit, a proficiency assessment unit, and a study advice unit. The study plan generation unit receives input information such as the user's current proficiency level and the number of days remaining until the exam date, and automatically generates an optimal study plan based on that information. For example, when a user inputs information such as "30 days until the exam, and my current proficiency level is intermediate," the generation AI analyzes the information and suggests daily study content and time allocation. The generation AI generates a study plan based on prompts containing instructions on what the user wants the generation AI to do. The study tool provision unit provides various tools to support the user's learning. For example, the generation AI automatically selects and provides question sets, mock exams, explanatory videos, etc. to the user. The generation AI provides appropriate tools based on prompts containing instructions on the type and content of the learning tools the user needs. The proficiency assessment unit periodically evaluates the user's learning progress and provides feedback based on the results. For example, the generation AI analyzes the user's accuracy rate for questions answered and the results of mock exams to evaluate the user's current proficiency. Based on the evaluation results, the generation AI suggests what content to study next and areas for improvement. The generation AI performs evaluation and feedback based on data on the user's learning progress. The study advice unit provides optimal study advice to the user, taking into account the number of days remaining until the exam date. For example, as the exam date approaches, the generation AI suggests areas to focus on and timing for review. The generation AI provides study advice based on information regarding the number of days remaining until the exam date. This allows the qualification acquisition support system according to the embodiment to efficiently support users in obtaining qualifications. For example, users can effectively utilize the limited time until the exam date and study efficiently. Furthermore, the generation AI provides individual study plans and tools, allowing users to study in a way that is optimal for them. Furthermore, proficiency assessments, feedback, and support for maintaining motivation allow users to continue studying continuously.

[0030] The study plan generation unit can analyze a user's past study history and performance data to generate a plan optimized for each individual learning style. For example, the generation AI in the study plan generation unit analyzes the user's past study history to identify which study method was most effective. For example, it suggests the most efficient study method for the user based on past test results and study time. The generation AI also analyzes the user's performance data to generate a plan optimized for a specific learning style. For example, it suggests learning materials that make extensive use of diagrams and graphs for users who are effective visual learners. The generation AI also integrates the user's study history and performance data to generate a plan optimized for each individual learning style. For example, it suggests optimal study times and break timings based on past study patterns. This makes it possible to provide a plan optimized for each user's individual learning style.

[0031] The study plan generation unit can optimally allocate study time by taking into account the user's lifestyle and daily schedule. In the study plan generation unit, for example, the generation AI analyzes the user's lifestyle and suggests optimal study times. For example, it determines whether the user is a morning person or a night person and provides a study schedule accordingly. The generation AI also takes into account the user's daily schedule and optimally allocates study time. For example, it suggests time periods when the user can study efficiently between work or housework. The generation AI also integrates the user's lifestyle and daily schedule and optimally allocates study time. For example, it suggests short, effective study times using the user's break time or commute time. This makes it possible to provide a study plan that matches the user's lifestyle and daily schedule.

[0032] The learning tool providing unit can provide interactive learning tools optimized for the user's learning style. In the learning tool providing unit, for example, the generation AI analyzes the user's learning style and provides optimized interactive learning tools. For example, practical learning is promoted using simulation-style teaching materials. The generation AI also provides game-style teaching materials based on the user's learning style. For example, tools that allow for fun learning through quizzes and puzzles are suggested. The generation AI also provides interactive learning tools taking the user's learning style into consideration. For example, a learning environment in which the user can actively participate is provided using interactive teaching materials. This makes it possible to provide interactive learning tools optimized for the user's learning style.

[0033] The learning tool providing unit can provide real-time feedback according to the user's learning progress and suggest immediate areas for improvement. In the learning tool providing unit, for example, the generation AI analyzes the user's learning progress in real time and provides immediate feedback. For example, it may present the correct answer rate and areas for improvement immediately after solving a problem. The generation AI also provides real-time feedback based on the user's learning progress and suggests what to study next. For example, it may instantly display an explanation for a problem that was answered incorrectly. The generation AI also evaluates the user's learning progress in real time and suggest immediate areas for improvement. For example, it may suggest the next task to tackle according to the learning progress. This makes it possible to provide real-time feedback and areas for improvement according to the user's learning progress.

[0034] The learning tool providing unit can adapt the user's learning tools to different devices, enabling multi-platform learning. For example, the learning tool providing unit uses a generation AI to adapt the user's learning tools to smartphones, providing an environment where they can learn anytime, anywhere. For example, it develops an app for smartphones. The generation AI also adapts the user's learning tools to tablets, enabling learning on a large screen. For example, it provides an interface for tablets. The generation AI also adapts the user's learning tools to VR headsets, providing an immersive learning experience. For example, it proposes simulation learning using VR. This allows the user to continue learning on different devices.

[0035] The learning tool providing unit can add a function that allows a user to share their learning tools with other users and promote collaborative learning and competition. The learning tool providing unit, for example, allows the generating AI to share the user's learning tools with other learners, promoting collaborative learning. For example, it provides an online group learning function. The generating AI also adds a function that allows the user to share their learning tools and promote competition with other learners. For example, it introduces a ranking function, allowing learners to compete over their learning progress. The generating AI also shares the user's learning tools with other learners, promoting collaborative learning and competition. For example, it provides a function for collaborative problem-solving and a game-style learning tool that stimulates competitive spirit. This allows users to engage in collaborative learning and competition with other users.

[0036] The proficiency assessment unit can analyze the user's learning history and suggest optimal learning tools based on past learning data. In the proficiency assessment unit, for example, the generation AI analyzes the user's learning history and suggests optimal learning tools based on past learning data. For example, it re-suggests tools that were effective in the past. The generation AI also suggests optimal tools according to the user's learning progress based on the user's learning history. For example, it provides learning materials specialized in a specific field. The generation AI also analyzes the user's learning history and suggests optimal learning tools based on past learning data. For example, it provides tools to strengthen weak points based on past test results. This makes it possible to suggest optimal learning tools based on the user's past learning data.

[0037] The proficiency assessment unit can automatically generate a customized learning toolset according to the user's learning goals. In the proficiency assessment unit, for example, the generation AI analyzes the user's learning goals and automatically generates a customized learning toolset accordingly. For example, it provides learning materials specialized for a specific qualification exam. The generation AI also automatically generates an optimal learning toolset based on the user's learning goals. For example, it provides tools to strengthen the skills necessary to achieve the goals. The generation AI also automatically generates a customized learning toolset taking the user's learning goals into consideration. For example, it provides intensive learning tools to achieve the goals in a short period of time. This makes it possible to provide a customized learning toolset according to the user's learning goals.

[0038] The study advice unit can analyze the user's study history and suggest optimal study tools based on past study data. In the study advice unit, for example, the generation AI analyzes the user's study history and suggests optimal study tools based on past study data. For example, it re-suggests tools that were effective in the past. The generation AI also suggests optimal tools according to the user's study progress based on the user's study history. For example, it provides learning materials specialized in a specific field. The generation AI also analyzes the user's study history and suggests optimal study tools based on past study data. For example, it provides tools to strengthen weak points based on past test results. This makes it possible to suggest optimal study tools based on the user's past study data.

[0039] The learning advice unit can automatically generate a customized learning toolset according to the user's learning goals. In the learning advice unit, for example, the generation AI analyzes the user's learning goals and automatically generates a customized learning toolset accordingly. For example, it provides learning materials specialized for a specific qualification exam. The generation AI also automatically generates an optimal learning toolset based on the user's learning goals. For example, it provides tools to strengthen the skills necessary to achieve the goals. The generation AI also automatically generates a customized learning toolset taking the user's learning goals into consideration. For example, it provides intensive learning tools to achieve the goals in a short period of time. In this way, a customized learning toolset according to the user's learning goals can be provided.

[0040] The learning advice unit can apply the user's learning tools to tools for different qualification exams or skill acquisition, and provide an integrated toolset for achieving multiple goals simultaneously. In the learning advice unit, for example, the generation AI analyzes the user's learning tools and applies them to tools for different qualification exams or skill acquisition. For example, an efficient learning toolset is provided for a user aiming to take multiple qualification exams at the same time. The generation AI also integrates the user's learning tools and provides a toolset for achieving multiple goals simultaneously. For example, it provides tools for balancing studying for a qualification exam and acquiring new skills. The generation AI also integrates tools for different qualification exams or skill acquisition, and provides an optimal learning toolset for the user. For example, it provides tools for efficiently learning common learning content. This makes it possible to provide an integrated toolset for the user to achieve multiple goals simultaneously.

[0041] The learning advice unit can add a function that allows a user to share their learning tools with other users and promotes collaborative learning and competition. The learning advice unit, for example, allows the generation AI to share the user's learning tools with other users, promoting collaborative learning. For example, it provides an online group learning function. The generation AI also adds a function that allows the user to share their learning tools and promote competition with other users. For example, it introduces a ranking function, allowing users to compete over their learning progress. The generation AI also shares the user's learning tools with other users, promoting collaborative learning and competition. For example, it provides a function for solving problems collaboratively, or a game-style learning tool that stimulates competitive spirit. This allows users to engage in collaborative learning and competition with other users.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The qualification acquisition support system can further include a health management unit that monitors the user's health condition. The health management unit analyzes the user's heart rate and sleep patterns to suggest the optimal time to study. For example, considering that concentration increases when the heart rate is stable, it recommends studying at that time. It can also analyze sleep patterns and suggest starting to study when the user has had enough rest. This makes it possible to provide a study plan based on the user's health condition.

[0044] The qualification acquisition support system can further include an environment assessment unit that monitors the user's learning environment. The environment assessment unit analyzes the noise level and lighting brightness of the user's learning environment and suggests the optimal learning environment. For example, if the noise level is high, it will recommend studying in a quiet place. Also, if the lighting is dim, it will suggest studying in a bright place. This makes it possible to provide the user with an optimal learning plan based on their learning environment.

[0045] The qualification acquisition support system can further include a content generation unit that customizes the user's learning content. The content generation unit customizes the learning content based on the user's interests. For example, if the user is interested in a particular field, learning materials related to that field will be provided preferentially. The content generation unit also makes efforts to make the learning content enjoyable based on the user's interests. For example, learning materials in the form of an interesting story will be provided. This makes it possible to provide learning content based on the user's interests.

[0046] The qualification acquisition support system may further include a progress display unit that visually displays the user's learning progress. The progress display unit visually displays the user's learning progress in graphs and charts, improving motivation. For example, it may display the learning achievement level in a pie chart, allowing the user to check the progress toward goal achievement at a glance. It may also display the learning progress in a timeline format, allowing the user to look back on past learning history. This makes it easier to visually grasp the user's learning progress.

[0047] The qualification acquisition support system can further include an achievement sharing unit that shares users' learning results. The achievement sharing unit allows users to share their learning results with other users and encourage each other. For example, users can share their learning achievements on social media and receive messages of encouragement from other users. Sharing their learning results also allows them to compete with other users and increase their motivation. This allows users to share their learning results and encourage each other.

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

[0049] Step 1: The study plan generator receives input information such as the user's current level of proficiency and the number of days remaining until the exam date, and automatically generates an optimal study plan based on that information. For example, if a user inputs information such as "30 days until the exam, and current proficiency level is intermediate," the generation AI analyzes that information and suggests daily study content and time allocation. The generation AI generates a study plan based on prompts containing instructions on what the user wants the generation AI to do. Step 2: The learning tool provider provides various tools to support the user's learning. For example, the generation AI automatically selects and provides question sets, practice tests, explanatory videos, etc. to the user. The generation AI provides appropriate tools based on prompts containing instructions regarding the type and content of the learning tools the user needs. Step 3: The proficiency assessment unit periodically evaluates the user's learning progress and provides feedback on the results. For example, the generation AI analyzes the accuracy rate of questions the user has answered and the results of mock exams to assess the user's current level of proficiency. Based on the evaluation results, it suggests what content to study next and areas for improvement. The generation AI provides evaluation and feedback based on data on the user's learning progress. Step 4: The study advice unit provides optimal study advice to the user, taking into account the number of days remaining until the exam. For example, as the exam date approaches, the generation AI suggests areas to focus on and timing for review. The generation AI provides study advice based on information about the number of days remaining until the exam date.

[0050] (Example 2) A qualification acquisition support system according to an embodiment of the present invention is a system that efficiently supports users in obtaining qualifications. This system provides study advice that takes into account the user's level of proficiency and the number of days remaining until the exam date. This allows the qualification acquisition support system to efficiently support users in obtaining qualifications.

[0051] A qualification acquisition support system according to an embodiment includes a study plan generation unit, a study tool provision unit, a proficiency assessment unit, and a study advice unit. The study plan generation unit receives input information such as the user's current proficiency level and the number of days remaining until the exam date, and automatically generates an optimal study plan based on that information. For example, when a user inputs information such as "30 days until the exam, and my current proficiency level is intermediate," the generation AI analyzes the information and suggests daily study content and time allocation. The generation AI generates a study plan based on prompts containing instructions on what the user wants the generation AI to do. The study tool provision unit provides various tools to support the user's learning. For example, the generation AI automatically selects and provides question sets, mock exams, explanatory videos, etc. to the user. The generation AI provides appropriate tools based on prompts containing instructions on the type and content of the learning tools the user needs. The proficiency assessment unit periodically evaluates the user's learning progress and provides feedback based on the results. For example, the generation AI analyzes the user's accuracy rate for questions answered and the results of mock exams to evaluate the user's current proficiency. Based on the evaluation results, the generation AI suggests what content to study next and areas for improvement. The generation AI performs evaluation and feedback based on data on the user's learning progress. The study advice unit provides optimal study advice to the user, taking into account the number of days remaining until the exam date. For example, as the exam date approaches, the generation AI suggests areas to focus on and timing for review. The generation AI provides study advice based on information regarding the number of days remaining until the exam date. This allows the qualification acquisition support system according to the embodiment to efficiently support users in obtaining qualifications. For example, users can effectively utilize the limited time until the exam date and study efficiently. Furthermore, the generation AI provides individual study plans and tools, allowing users to study in a way that is optimal for them. Furthermore, proficiency assessments, feedback, and support for maintaining motivation allow users to continue studying continuously.

[0052] The study plan generation unit can analyze a user's past study history and performance data to generate a plan optimized for each individual learning style. For example, the generation AI in the study plan generation unit analyzes the user's past study history to identify which study method was most effective. For example, it suggests the most efficient study method for the user based on past test results and study time. The generation AI also analyzes the user's performance data to generate a plan optimized for a specific learning style. For example, it suggests learning materials that make extensive use of diagrams and graphs for users who are effective visual learners. The generation AI also integrates the user's study history and performance data to generate a plan optimized for each individual learning style. For example, it suggests optimal study times and break timings based on past study patterns. This makes it possible to provide a plan optimized for each user's individual learning style.

[0053] The study plan generation unit can optimally allocate study time by taking into account the user's lifestyle and daily schedule. In the study plan generation unit, for example, the generation AI analyzes the user's lifestyle and suggests optimal study times. For example, it determines whether the user is a morning person or a night person and provides a study schedule accordingly. The generation AI also takes into account the user's daily schedule and optimally allocates study time. For example, it suggests time periods when the user can study efficiently between work or housework. The generation AI also integrates the user's lifestyle and daily schedule and optimally allocates study time. For example, it suggests short, effective study times using the user's break time or commute time. This makes it possible to provide a study plan that matches the user's lifestyle and daily schedule.

[0054] The study plan generation unit can use the emotion estimation function to evaluate the user's motivation to learn and stress level in real time and dynamically adjust the study plan accordingly. The study plan generation unit, for example, uses the emotion estimation function to evaluate the user's motivation to learn in real time and dynamically adjust the study plan accordingly. For example, if the motivation to learn is low, a goal that can be achieved in a short period of time is set. The emotion estimation function is also used to evaluate the user's stress level in real time and dynamically adjust the study plan accordingly. For example, if stress is high, a study method that allows relaxation is suggested. The emotion estimation function is also used to comprehensively evaluate the user's motivation to learn and stress level and dynamically adjust the study plan accordingly. For example, if the motivation to learn is high, tasks that require concentration are suggested, and if stress is high, tasks that allow relaxation are suggested. In this way, a study plan can be provided that corresponds to the user's motivation to learn and stress level.

[0055] The learning tool providing unit can provide interactive learning tools optimized for the user's learning style. In the learning tool providing unit, for example, the generation AI analyzes the user's learning style and provides optimized interactive learning tools. For example, practical learning is promoted using simulation-style teaching materials. The generation AI also provides game-style teaching materials based on the user's learning style. For example, tools that allow for fun learning through quizzes and puzzles are suggested. The generation AI also provides interactive learning tools taking the user's learning style into consideration. For example, a learning environment in which the user can actively participate is provided using interactive teaching materials. This makes it possible to provide interactive learning tools optimized for the user's learning style.

[0056] The learning tool providing unit can provide real-time feedback according to the user's learning progress and suggest immediate areas for improvement. In the learning tool providing unit, for example, the generation AI analyzes the user's learning progress in real time and provides immediate feedback. For example, it may present the correct answer rate and areas for improvement immediately after solving a problem. The generation AI also provides real-time feedback based on the user's learning progress and suggests what to study next. For example, it may instantly display an explanation for a problem that was answered incorrectly. The generation AI also evaluates the user's learning progress in real time and suggest immediate areas for improvement. For example, it may suggest the next task to tackle according to the learning progress. This makes it possible to provide real-time feedback and areas for improvement according to the user's learning progress.

[0057] The learning tool providing unit can use the emotion estimation function to monitor the user's emotional state while studying and suggest breaks or refreshment at appropriate times. The learning tool providing unit can, for example, use the emotion estimation function to monitor the user's emotional state while studying and suggest breaks at appropriate times. For example, the learning tool providing unit can encourage the user to take a break when their concentration levels drop. The learning tool providing unit can also use the emotion estimation function to evaluate the user's emotional state while studying and suggest refreshment. For example, the learning tool providing unit can suggest relaxation methods when stress levels rise. The learning tool providing unit can also use the emotion estimation function to monitor the user's emotional state while studying and suggest breaks or refreshment at appropriate times. For example, the learning tool providing unit can encourage the user to take a short break when fatigue accumulates. This makes it possible to suggest breaks or refreshment according to the user's emotional state while studying.

[0058] The learning tool providing unit can adapt the user's learning tools to different devices, enabling multi-platform learning. For example, the learning tool providing unit uses a generation AI to adapt the user's learning tools to smartphones, providing an environment where they can learn anytime, anywhere. For example, it develops an app for smartphones. The generation AI also adapts the user's learning tools to tablets, enabling learning on a large screen. For example, it provides an interface for tablets. The generation AI also adapts the user's learning tools to VR headsets, providing an immersive learning experience. For example, it proposes simulation learning using VR. This allows the user to continue learning on different devices.

[0059] The learning tool providing unit can add a function that allows a user to share their learning tools with other users and promote collaborative learning and competition. The learning tool providing unit, for example, allows the generating AI to share the user's learning tools with other learners, promoting collaborative learning. For example, it provides an online group learning function. The generating AI also adds a function that allows the user to share their learning tools and promote competition with other learners. For example, it introduces a ranking function, allowing learners to compete over their learning progress. The generating AI also shares the user's learning tools with other learners, promoting collaborative learning and competition. For example, it provides a function for collaborative problem-solving and a game-style learning tool that stimulates competitive spirit. This allows users to engage in collaborative learning and competition with other users.

[0060] The learning tool providing unit can use the emotion estimation function to evaluate the satisfaction and effectiveness a user feels with a specific learning tool and improve the tool based on that. The learning tool providing unit, for example, uses the emotion estimation function to evaluate the satisfaction a user feels with a specific learning tool and improve the tool based on that. For example, it improves a tool with low satisfaction. It also uses the emotion estimation function to evaluate the effectiveness a user feels with a specific learning tool and improves the tool based on that. For example, it improves a tool with low effectiveness. It also uses the emotion estimation function to comprehensively evaluate the satisfaction and effectiveness a user feels with a specific learning tool and improves the tool based on that. For example, it preferentially provides tools with high satisfaction and effectiveness. This makes it possible to improve learning tools based on user satisfaction and effectiveness.

[0061] The proficiency assessment unit can analyze the user's learning history and suggest optimal learning tools based on past learning data. In the proficiency assessment unit, for example, the generation AI analyzes the user's learning history and suggests optimal learning tools based on past learning data. For example, it re-suggests tools that were effective in the past. The generation AI also suggests optimal tools according to the user's learning progress based on the user's learning history. For example, it provides learning materials specialized in a specific field. The generation AI also analyzes the user's learning history and suggests optimal learning tools based on past learning data. For example, it provides tools to strengthen weak points based on past test results. This makes it possible to suggest optimal learning tools based on the user's past learning data.

[0062] The proficiency assessment unit can automatically generate a customized learning toolset according to the user's learning goals. In the proficiency assessment unit, for example, the generation AI analyzes the user's learning goals and automatically generates a customized learning toolset accordingly. For example, it provides learning materials specialized for a specific qualification exam. The generation AI also automatically generates an optimal learning toolset based on the user's learning goals. For example, it provides tools to strengthen the skills necessary to achieve the goals. The generation AI also automatically generates a customized learning toolset taking the user's learning goals into consideration. For example, it provides intensive learning tools to achieve the goals in a short period of time. This makes it possible to provide a customized learning toolset according to the user's learning goals.

[0063] The proficiency assessment unit can use the emotion estimation function to monitor the user's emotional state during study in real time and provide appropriate tools in a timely manner. The proficiency assessment unit, for example, uses the emotion estimation function to monitor the user's emotional state during study in real time and provide appropriate tools in a timely manner. For example, a refreshment tool is provided when concentration levels drop. The emotion estimation function can also be used to evaluate the user's emotional state during study and provide appropriate tools. For example, learning materials that can help the user relax when stress levels rise. The emotion estimation function can also be used to monitor the user's emotional state during study in real time and provide appropriate tools in a timely manner. For example, an encouraging message can be sent when motivation levels drop. This makes it possible to provide tools in a timely manner that correspond to the user's emotional state during study.

[0064] The study advice unit can analyze the user's study history and suggest optimal study tools based on past study data. In the study advice unit, for example, the generation AI analyzes the user's study history and suggests optimal study tools based on past study data. For example, it re-suggests tools that were effective in the past. The generation AI also suggests optimal tools according to the user's study progress based on the user's study history. For example, it provides learning materials specialized in a specific field. The generation AI also analyzes the user's study history and suggests optimal study tools based on past study data. For example, it provides tools to strengthen weak points based on past test results. This makes it possible to suggest optimal study tools based on the user's past study data.

[0065] The learning advice unit can automatically generate a customized learning toolset according to the user's learning goals. In the learning advice unit, for example, the generation AI analyzes the user's learning goals and automatically generates a customized learning toolset accordingly. For example, it provides learning materials specialized for a specific qualification exam. The generation AI also automatically generates an optimal learning toolset based on the user's learning goals. For example, it provides tools to strengthen the skills necessary to achieve the goals. The generation AI also automatically generates a customized learning toolset taking the user's learning goals into consideration. For example, it provides intensive learning tools to achieve the goals in a short period of time. In this way, a customized learning toolset according to the user's learning goals can be provided.

[0066] The study advice unit can use the emotion estimation function to monitor the user's emotional state while studying in real time and provide appropriate tools in a timely manner. The study advice unit, for example, uses the emotion estimation function to monitor the user's emotional state while studying in real time and provide appropriate tools in a timely manner. For example, a refreshment tool is provided when concentration levels drop. The emotion estimation function can also be used to evaluate the user's emotional state while studying and provide appropriate tools. For example, learning materials that can help the user relax when stress levels rise. The emotion estimation function can also be used to monitor the user's emotional state while studying in real time and provide appropriate tools in a timely manner. For example, an encouraging message can be sent when motivation levels drop. This makes it possible to provide tools in a timely manner that correspond to the user's emotional state while studying.

[0067] The learning advice unit can apply the user's learning tools to tools for different qualification exams or skill acquisition, and provide an integrated toolset for achieving multiple goals simultaneously. In the learning advice unit, for example, the generation AI analyzes the user's learning tools and applies them to tools for different qualification exams or skill acquisition. For example, an efficient learning toolset is provided for a user aiming to take multiple qualification exams at the same time. The generation AI also integrates the user's learning tools and provides a toolset for achieving multiple goals simultaneously. For example, it provides tools for balancing studying for a qualification exam and acquiring new skills. The generation AI also integrates tools for different qualification exams or skill acquisition, and provides an optimal learning toolset for the user. For example, it provides tools for efficiently learning common learning content. This makes it possible to provide an integrated toolset for the user to achieve multiple goals simultaneously.

[0068] The learning advice unit can add a function that allows a user to share their learning tools with other users and promotes collaborative learning and competition. The learning advice unit, for example, allows the generation AI to share the user's learning tools with other users, promoting collaborative learning. For example, it provides an online group learning function. The generation AI also adds a function that allows the user to share their learning tools and promote competition with other users. For example, it introduces a ranking function, allowing users to compete over their learning progress. The generation AI also shares the user's learning tools with other users, promoting collaborative learning and competition. For example, it provides a function for solving problems collaboratively, or a game-style learning tool that stimulates competitive spirit. This allows users to engage in collaborative learning and competition with other users.

[0069] The study advice unit can use the emotion estimation function to evaluate the satisfaction and effectiveness a user feels with a specific study tool and improve the tool based on that. The study advice unit, for example, uses the emotion estimation function to evaluate the satisfaction a user feels with a specific study tool and improve the tool based on that. For example, it improves a tool with low satisfaction. It also uses the emotion estimation function to evaluate the effectiveness a user feels with a specific study tool and improves the tool based on that. For example, it improves a tool with low effectiveness. It also uses the emotion estimation function to comprehensively evaluate the satisfaction and effectiveness a user feels with a specific study tool and improves the tool based on that. For example, it preferentially provides tools with high satisfaction and effectiveness. This makes it possible to improve study tools based on user satisfaction and effectiveness.

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

[0071] The qualification acquisition support system can further include a health management unit that monitors the user's health condition. The health management unit analyzes the user's heart rate and sleep patterns to suggest the optimal time to study. For example, considering that concentration increases when the heart rate is stable, it recommends studying at that time. It can also analyze sleep patterns and suggest starting to study when the user has had enough rest. This makes it possible to provide a study plan based on the user's health condition.

[0072] The qualification acquisition support system can further include an environment assessment unit that monitors the user's learning environment. The environment assessment unit analyzes the noise level and lighting brightness of the user's learning environment and suggests the optimal learning environment. For example, if the noise level is high, it will recommend studying in a quiet place. Also, if the lighting is dim, it will suggest studying in a bright place. This makes it possible to provide the user with an optimal learning plan based on their learning environment.

[0073] The qualification acquisition support system can further include a content generation unit that customizes the user's learning content. The content generation unit customizes the learning content based on the user's interests. For example, if the user is interested in a particular field, learning materials related to that field will be provided preferentially. The content generation unit also makes efforts to make the learning content enjoyable based on the user's interests. For example, learning materials in the form of an interesting story will be provided. This makes it possible to provide learning content based on the user's interests.

[0074] The qualification acquisition support system may further include a progress display unit that visually displays the user's learning progress. The progress display unit visually displays the user's learning progress in graphs and charts, improving motivation. For example, it may display the learning achievement level in a pie chart, allowing the user to check the progress toward goal achievement at a glance. It may also display the learning progress in a timeline format, allowing the user to look back on past learning history. This makes it easier to visually grasp the user's learning progress.

[0075] The qualification acquisition support system can further include an achievement sharing unit that shares users' learning results. The achievement sharing unit allows users to share their learning results with other users and encourage each other. For example, users can share their learning achievements on social media and receive messages of encouragement from other users. Sharing their learning results also allows them to compete with other users and increase their motivation. This allows users to share their learning results and encourage each other.

[0076] The qualification acquisition support system can further include a motivation maintenance unit that estimates the user's emotions and maintains motivation to study. The motivation maintenance unit estimates the user's emotions and sends encouraging messages when motivation is low. For example, when motivation to study is low, it shares encouraging words and experiences of success. Furthermore, when emotions are estimated and motivation is high, it suggests further challenges. This makes it possible to maintain motivation based on the user's emotions.

[0077] The qualification acquisition support system can further include a stress reduction unit that estimates the user's emotions and reduces the stress of studying. The stress reduction unit estimates the user's emotions and suggests relaxation methods when stress is high. For example, when stress is high, it suggests deep breathing and stretching methods. Furthermore, when stress is low, it estimates the emotions and suggests study methods to improve concentration. In this way, stress can be reduced based on the user's emotions.

[0078] The qualification acquisition support system can further include an effect maximization unit that estimates the user's emotions and maximizes the effectiveness of learning. The effect maximization unit estimates the user's emotions and adjusts the study plan so that the effectiveness of learning is maximized. For example, when the user's emotions are stable, it suggests tasks that require concentration. When the user's emotions are unstable, it suggests a learning method that allows the user to relax. In this way, it is possible to maximize the effectiveness of learning based on the user's emotions.

[0079] The qualification acquisition support system can further include a satisfaction improvement unit that estimates the user's emotions and improves the user's learning satisfaction. The satisfaction improvement unit estimates the user's emotions and suggests improvements if the user's learning satisfaction is low. For example, if the user's satisfaction is low, the unit suggests changing the learning method. Furthermore, if the user's emotions are estimated and the user's satisfaction is high, the unit suggests continuing the learning method. This makes it possible to improve the user's learning satisfaction based on the user's emotions.

[0080] The qualification acquisition support system can further include an effect evaluation unit that estimates the user's emotions and evaluates the effectiveness of the learning. The effect evaluation unit estimates the user's emotions and evaluates the degree of effectiveness of the learning. For example, when the user's emotions are stable, the effect of the learning is evaluated as high. On the other hand, when the user's emotions are unstable, the effect of the learning is evaluated as low. In this way, the effect of the learning can be evaluated based on the user's emotions.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The study plan generator receives input information such as the user's current level of proficiency and the number of days remaining until the exam date, and automatically generates an optimal study plan based on that information. For example, if a user inputs information such as "30 days until the exam, and current proficiency level is intermediate," the generation AI analyzes that information and suggests daily study content and time allocation. The generation AI generates a study plan based on prompts containing instructions on what the user wants the generation AI to do. Step 2: The learning tool provider provides various tools to support the user's learning. For example, the generation AI automatically selects and provides question sets, practice tests, explanatory videos, etc. to the user. The generation AI provides appropriate tools based on prompts containing instructions regarding the type and content of the learning tools the user needs. Step 3: The proficiency assessment unit periodically evaluates the user's learning progress and provides feedback on the results. For example, the generation AI analyzes the accuracy rate of questions the user has answered and the results of mock exams to assess the user's current level of proficiency. Based on the evaluation results, it suggests what content to study next and areas for improvement. The generation AI provides evaluation and feedback based on data on the user's learning progress. Step 4: The study advice unit provides optimal study advice to the user, taking into account the number of days remaining until the exam. For example, as the exam date approaches, the generation AI suggests areas to focus on and timing for review. The generation AI provides study advice based on information about the number of days remaining until the exam date.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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. It is a system equipped with generative AI, The generated AI is a study plan generation unit that receives input information such as the user's current proficiency level and the number of days remaining until the test date, and automatically generates an optimal study plan based on the information; a learning tool providing unit that provides various tools to support the user's learning; a proficiency assessment unit that periodically assesses the user's learning progress and provides feedback on the assessment results; a study advice unit that provides optimal study advice to the user in consideration of the number of days remaining until the test date. A system characterized by:

2. The learning plan generation unit Analyze the user's past learning history and performance data to generate a plan optimized for their individual learning style.

2. The system of claim 1.

3. The learning plan generation unit Considering the user's daily schedule and daily rhythm, optimally allocate study time 2. The system of claim 1.

4. The learning plan generation unit Evaluate the user's motivation and stress level in real time and dynamically adjust the learning plan accordingly.

2. The system of claim 1.

5. The learning tool providing unit Providing interactive learning tools optimized for the user's learning style 2. The system of claim 1.

6. The learning tool providing unit Providing real-time feedback and suggesting immediate improvements based on the user's learning progress 2. The system of claim 1.

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