system

Abstract

The system according to this embodiment aims to maintain the user's motivation to learn by providing an individualized learning plan that is tailored to the user's learning progress. [Solution] The system according to the embodiment comprises a goal setting unit, an input unit, an analysis unit, a proposal unit, and a management unit. The goal setting unit sets the user's goals. The input unit inputs the results of mock exams and school tests. The analysis unit analyzes the user's learning progress based on the results input by the input unit. The proposal unit proposes a learning plan based on the analysis results obtained by the analysis unit. The management unit manages the learning progress based on the learning plan proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional technologies have problems such as lack of maintenance of learning motivation, lack of effective learning methods, difficulty in progress management, and burden of guidance on guardians.

[0005] The system according to the embodiment aims to provide an individual learning plan according to the learning progress of the user and maintain the learning motivation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a goal setting unit, an input unit, an analysis unit, a proposal unit, and a management unit. The goal setting unit sets the user's goals. The input unit inputs the results of mock exams and school tests. The analysis unit analyzes the user's learning progress based on the results input by the input unit. The proposal unit proposes a learning plan based on the analysis results obtained by the analysis unit. The management unit manages the learning progress based on the learning plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide an individualized learning plan tailored to the user's learning progress, thereby maintaining their motivation to learn. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor​​​The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The autonomous learning support system according to an embodiment of the present invention is an AI agent that provides autonomous learning support for elementary, junior high, and high school students, university entrance exam candidates, their parents, and working adults. This autonomous learning support system allows users to set specific goals and input photos of mock exam or school test results, after which the AI ​​analyzes the distance between the user and their goals. Next, the AI ​​presents milestones to the user's goals and proposes a learning plan that takes into account the user's classes, learning progress, and areas of weakness. Furthermore, it provides feedback based on the user's learning status and results, and automatically suggests the next learning step. This mechanism enables users to reach their goals with a personalized, highly effective learning plan tailored to their academic ability, strengths and weaknesses, and learning progress. For example, it can strengthen specific problem areas based on test results and autonomously adjust the learning plan if delays occur. To address challenges such as maintaining motivation, lack of effective learning methods, difficulty in progress management, and the burden of guidance on parents, this AI agent has a function to autonomously modify the learning plan according to learning progress, visualizes progress, and provides interactive elements to maintain motivation. Furthermore, it provides specific feedback based on learning outcomes and automatically suggests the next learning steps. This enables the autonomous learning support system to reach its goals with the most effective learning plan tailored to the user's academic ability, strengths and weaknesses, and learning progress.

[0029] The autonomous learning support system according to this embodiment comprises a goal setting unit, an input unit, an analysis unit, a proposal unit, and a management unit. The goal setting unit sets the user's goals. The goal setting unit provides, for example, an interface for the user to set specific learning goals. The user can set goals such as passing the University of Tokyo entrance exam or obtaining the G-Test certification. The input unit inputs the results of mock exams and school tests. The input unit allows the user to, for example, take a picture of the results of mock exams or school tests and upload it. The input unit also allows the user to manually input test results. The analysis unit analyzes the user's learning progress based on the results entered by the input unit. The analysis unit, for example, uses AI to analyze the user's test results and evaluates the user's academic ability and strengths and weaknesses. The proposal unit proposes a learning plan based on the analysis results obtained by the analysis unit. The proposal unit proposes, for example, a plan that takes into account the user's classes, learning progress, and areas of weakness. The proposal unit uses AI to analyze the user's learning situation and generates an optimal learning plan. The management unit manages the learning progress based on the learning plan proposed by the proposal unit. The management department provides feedback, for example, based on the user's learning status and learning outcomes. The management department uses AI to monitor the user's learning progress in real time and revise the learning plan as needed. As a result, the autonomous learning support system according to the embodiment can efficiently set user goals, analyze learning progress, propose learning plans, and manage learning progress.

[0030] The goal-setting section allows users to set their own goals. For example, it provides an interface for users to set specific learning goals. Users can set goals such as passing the University of Tokyo entrance exam or obtaining the G-certification. The goal-setting section allows users to input deadlines and specific achievement criteria when setting goals. For example, if a user sets passing the University of Tokyo entrance exam as their goal, the goal-setting section provides an interface for inputting detailed information such as the subjects, scores, and study time required for passing. Furthermore, the goal-setting section has a function to visualize the progress towards the goals set by the user, allowing them to see at a glance how far they have progressed towards their goals. In addition, the goal-setting section has a function to send periodic reminders and encouraging messages to help users maintain their motivation to achieve their goals. This allows users to stay constantly aware of their goals and increase their motivation to learn.

[0031] The input section allows users to input results from mock exams and school tests. For example, users can upload photos of their mock exam or school test results. The input section also allows for manual input of test results. When users input test results, the input section provides an interface for entering detailed information such as the test type, date, subject, and score. For example, when a user inputs mock exam results, the input section provides a form for entering the mock exam name, date, and scores for each subject. Furthermore, when uploading photos of test results, the input section uses image recognition technology to automatically read the test results and complete the input content. This allows users to input accurate test results without effort. In addition, the input section saves the entered data as a history so that users can refer to past test results at any time as needed. This makes it easier for users to understand their learning progress and helps them plan their next studies.

[0032] The analysis unit analyzes the user's learning progress based on the results entered by the input unit. For example, the analysis unit uses AI to analyze the user's test results and evaluate the user's academic ability and strengths and weaknesses. Specifically, the AI ​​compares the user's test results with past data and analyzes the trend in scores and strengths and weaknesses in each subject. For example, if a user consistently scores high on math tests, the AI ​​will evaluate that subject as a strength, and conversely, if a user consistently scores low on English tests, it will evaluate that subject as a weakness. The AI ​​also analyzes in detail the strengths and weaknesses in specific areas and question formats based on the user's test results. For example, it may provide specific analysis results such as being good at calculation problems in math tests but weak at application problems. Furthermore, the analysis unit monitors the user's learning progress in real time and evaluates the learning progress and goal achievement. This allows the user to accurately understand their learning situation and use this information to create their next learning plan.

[0033] The Proposal Department proposes a learning plan based on the analysis results obtained by the Analysis Department. For example, the Proposal Department proposes a plan that takes into account the user's classes, learning progress, and areas of difficulty. The Proposal Department uses AI to analyze the user's learning situation and generate an optimal learning plan. Specifically, the AI ​​creates an efficient learning schedule based on the user's strengths and weaknesses and learning progress. For example, if a user has difficulty with English listening, the Proposal Department will propose a learning plan that focuses on strengthening listening skills. It also sets learning priorities according to the user's goal achievement deadline and provides a concrete action plan to efficiently advance learning. Furthermore, the Proposal Department proposes the most suitable learning methods and materials according to the user's learning style and preferences. For example, for users who prefer visual learning, it will propose video materials and materials that make extensive use of diagrams, and for users who prefer auditory learning, it will propose audio materials and podcasts. This allows users to efficiently advance their learning using a learning method that suits them.

[0034] The Management Department manages learning progress based on the learning plan proposed by the Proposal Department. The Management Department provides feedback, for example, based on the user's learning status and learning results. The Management Department uses AI to monitor the user's learning progress in real time and revise the learning plan as needed. Specifically, the AI ​​periodically evaluates the user's learning status and monitors their progress toward achieving their goals. For example, it evaluates whether the user is learning according to plan and whether their learning results are improving, and modifies the learning plan as necessary. The Management Department also provides regular feedback to the user, informing them of their learning progress and areas for improvement. For example, it provides specific advice on how the user should approach subjects they find difficult and how to adjust their learning pace. Furthermore, the Management Department has a function to send rewards and encouraging messages according to the level of achievement in order to maintain the user's motivation to learn. This allows users to always be aware of their learning status and to learn efficiently toward achieving their goals.

[0035] The proposal function can suggest study plans that take into account the user's classes, learning progress, and areas of difficulty. For example, the proposal function can suggest a plan that efficiently allocates study time, taking into account the user's class schedule. The proposal function can also evaluate the user's learning progress and suggest a learning plan that matches that progress. The proposal function can identify the user's areas of difficulty and suggest a plan that focuses on those areas. In this way, the proposal function can suggest study plans that are tailored to the user's individual learning situation.

[0036] The management department can provide feedback based on the user's learning progress and achievements. For example, the management department can monitor the user's learning progress in real time and provide appropriate feedback according to the progress. The management department can also evaluate the user's learning achievements and provide feedback based on those achievements. The management department can also suggest improvements to learning methods and content based on the user's learning progress. In this way, the management department can provide feedback tailored to the user's learning progress.

[0037] The management department can suggest the next learning steps and revise the learning plan. For example, the management department can suggest the next learning content based on the user's learning progress. The management department can also periodically review the user's learning plan and adjust it according to the progress. The management department can also evaluate the user's learning status and suggest revisions to the learning plan as needed. This allows the management department to suggest the next steps according to the user's learning progress and revise the plan.

[0038] The suggestion function can propose strengthening specific problem areas based on test results. For example, it can analyze a user's test results and propose a learning plan to strengthen specific problem areas. It can also identify problem areas where the user struggles and propose a plan to focus on learning those areas. It can also propose a learning plan to further strengthen problem areas where the user excels. In this way, the suggestion function can strengthen specific problem areas based on test results.

[0039] The suggestion unit can autonomously adjust the learning plan if delays occur. For example, the suggestion unit can monitor the user's learning progress in real time and automatically adjust the learning plan if delays occur. The suggestion unit can also periodically review the user's learning plan and adjust it according to the progress. The suggestion unit can also evaluate the user's learning status and suggest revisions to the learning plan as needed. In this way, the suggestion unit can autonomously adjust to delays in the learning plan.

[0040] The goal-setting unit can analyze the user's past goal achievement history and select the optimal goal-setting method. For example, the goal-setting unit can suggest easily achievable goals based on data of goals the user has achieved in the past. The goal-setting unit can also analyze the reasons for goals the user has failed to achieve in the past and set goals that reflect improvements. The goal-setting unit can also suggest the optimal goal achievement period based on the user's past goal achievement history. In this way, the goal-setting unit can set optimal goals based on the user's past goal achievement history.

[0041] The goal-setting unit can customize goals based on the user's current learning status and areas of interest. For example, the goal-setting unit can analyze the user's current learning status and set achievable goals. The goal-setting unit can also set engaging goals based on the user's areas of interest. The goal-setting unit can also set goals that gradually increase in difficulty according to the user's learning progress. In this way, the goal-setting unit can customize goals based on the user's current learning status and areas of interest.

[0042] The goal-setting unit can prioritize setting highly relevant goals by considering the user's geographical location information when setting goals. For example, the goal-setting unit can set highly relevant goals based on information about educational institutions in the user's area. The goal-setting unit can also set goals that make it easier to secure study time based on the user's commute route to school or work. The goal-setting unit can also set goals considering local events and exam schedules in the user's area. In this way, the goal-setting unit can set highly relevant goals based on the user's geographical location information.

[0043] The goal-setting unit can analyze the user's social media activity and set relevant goals when setting goals. For example, the goal-setting unit can set goals based on topics the user is interested in on social media. The goal-setting unit can also set goals by referring to goals achieved by the user's followers and friends. The goal-setting unit can also analyze the content of the user's social media posts and set goals that will attract their attention. In this way, the goal-setting unit can set relevant goals based on the user's social media activity.

[0044] The input unit can analyze the user's past test results and select the optimal input method. For example, it might prioritize suggesting input methods the user has used in the past (such as photos or text). It can also suggest the most efficient input method based on the user's past test results. Furthermore, it can analyze the user's past input history and suggest the optimal input timing. This allows the input unit to select the optimal input method based on the user's past test results.

[0045] The input unit can filter test results based on the user's current learning status and areas of interest. For example, it can analyze the user's current learning status and input only relevant test results. It can also prioritize inputting important test results based on the user's areas of interest. Furthermore, it can input only the necessary test results according to the user's learning progress. This allows the input unit to filter test results based on the user's current learning status and areas of interest.

[0046] The input unit can prioritize inputting highly relevant data when entering test results, taking into account the user's geographical location. For example, the input unit can input highly relevant test results based on information about educational institutions in the user's area. The input unit can also input test results that make it easier to allocate study time based on the user's commute route to school or work. The input unit can also input test results considering local events and exam schedules. In this way, the input unit can prioritize inputting highly relevant data based on the user's geographical location.

[0047] The input unit can analyze the user's social media activity and input relevant data when entering test results. For example, the input unit can input test results based on topics the user is interested in on social media. The input unit can also input test results based on goals achieved by the user's followers and friends. The input unit can also analyze the content of the user's social media posts and input interesting test results. In this way, the input unit can input relevant data based on the user's social media activity.

[0048] The analysis department can improve the accuracy of its analysis by considering the interrelationships of test results. For example, the analysis department can analyze the interrelationships of test results and prioritize the analysis of highly relevant data. The analysis department can also improve the accuracy of its analysis by considering the interrelationships of test results. The analysis department can also select the optimal analysis method based on the interrelationships of test results. As a result, the analysis department improves the accuracy of its analysis by considering the interrelationships of test results.

[0049] The analysis department can perform analyses while considering user attribute information. For example, the analysis department can select the optimal analysis method by considering the user's age and gender. The analysis department can also select the optimal analysis method based on the user's learning history. The analysis department can also improve the accuracy of the analysis by considering user attribute information. In this way, the analysis department can improve the accuracy of the analysis by considering user attribute information.

[0050] The analysis department can perform analyses while considering the geographical distribution of test results. For example, the analysis department can analyze the geographical distribution of test results to understand regional trends. The analysis department can also select the optimal analysis method while considering the geographical distribution of test results. Based on the geographical distribution of test results, the analysis department can also propose countermeasures for each region. In this way, the analysis department can understand regional trends by considering the geographical distribution of test results.

[0051] The analysis department can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis department can improve the accuracy of its analysis by referring to relevant literature. The analysis department can also select the optimal analysis method based on relevant literature. Furthermore, the analysis department can improve the reliability of its analysis results by referring to relevant literature. Thus, the analysis department improves the accuracy of its analysis by referring to relevant literature.

[0052] The proposal department can adjust the level of detail in its proposals based on the importance of the learning plans. For example, it can provide detailed proposals for highly important learning plans, and simplified proposals for less important ones. The proposal department can also adjust the level of detail in its proposals in stages according to the importance of the learning plans. This allows the proposal department to adjust the level of detail in its proposals according to the importance of the learning plans.

[0053] The proposal function can apply different proposal algorithms depending on the category of the learning plan during the proposal process. For example, the proposal function can apply a mathematical proposal algorithm to learning plans for science subjects. It can also apply a linguistic proposal algorithm to learning plans for humanities subjects. It can also apply a practical proposal algorithm to learning plans for practical skills subjects. This allows the proposal function to apply the most suitable proposal algorithm according to the category of the learning plan.

[0054] The proposal department can determine the priority of proposals based on the submission timing of the learning plans. For example, the proposal department will prioritize proposals for learning plans with approaching deadlines. The proposal department can also postpone proposals for learning plans with later deadlines. The proposal department can also adjust the priority of proposals in stages according to the submission timing. This allows the proposal department to determine the priority of proposals according to the submission timing of the learning plans.

[0055] The proposal department can adjust the order of proposals based on the relevance of the learning plans. For example, the proposal department can prioritize proposals for highly relevant learning plans. The proposal department can also postpone proposals for less relevant learning plans. The proposal department can also adjust the order of proposals in stages according to the relevance of the learning plans. This allows the proposal department to adjust the order of proposals according to the relevance of the learning plans.

[0056] The management department can select the optimal management method when managing learning progress by referring to the user's past learning history. For example, the management department can select the optimal progress management method based on the user's past learning history. The management department can also propose an efficient progress management method based on the user's past learning history. The management department can also analyze the user's past learning history and select the optimal progress management method. In this way, the management department can select the optimal management method based on the user's past learning history.

[0057] The management unit can customize the management methods based on the user's current learning status when managing learning progress. For example, the management unit can analyze the user's current learning status and select the optimal progress management method. The management unit can also propose an efficient progress management method based on the user's current learning status. The management unit can also customize the progress management method considering the user's current learning status. In this way, the management unit can customize the management methods based on the user's current learning status.

[0058] The management department can select the optimal management method when managing learning progress, taking into account the user's geographical location. For example, the management department can select the optimal progress management method based on information about educational institutions in the user's area. The management department can also propose a progress management method that makes it easier to secure study time based on the user's commute route to school or work. The management department can also select a progress management method considering local events and exam schedules in the user's area. In this way, the management department can select the optimal management method based on the user's geographical location.

[0059] The management department can analyze users' social media activity and propose management methods when managing learning progress. For example, the management department can propose progress management methods based on topics that users are interested in on social media. The management department can also propose progress management methods by referring to goals achieved by the user's followers and friends. The management department can also analyze the content of users' social media posts and propose progress management methods that are engaging. In this way, the management department can propose management methods based on users' social media activity.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The suggestion function can customize learning plans based on the user's learning style. For example, it can suggest learning plans that make extensive use of diagrams and graphs to visual learners, plans that incorporate audio and podcasts to auditory learners, and plans that include practical exercises and fieldwork to experiential learners. This allows the suggestion function to provide the optimal learning plan tailored to the user's learning style.

[0062] The management department can compare a user's learning progress with other users and provide feedback on their relative progress. For example, if a user's progress is behind that of other users with the same goal, the management department can suggest additional study time. Conversely, if a user is progressing quickly, the management department can offer advice on how to move on to the next step. Furthermore, by comparing users in the same region or school, the management department can understand learning trends specific to each region or school and provide appropriate feedback. This allows the management department to relatively evaluate users' learning progress and provide appropriate feedback.

[0063] The proposal department can predict the effectiveness of learning plans based on the user's learning history and propose the optimal plan. For example, it can analyze the effectiveness of past learning plans and prioritize proposing plans that were highly effective. Furthermore, if a particular learning method proved effective based on the user's learning history, it can propose a plan incorporating that method. It can also propose a plan that focuses on strengthening areas where the user struggles, based on their learning history. In this way, the proposal department can provide the optimal learning plan based on the user's learning history.

[0064] The management department can visualize users' learning progress and provide interactive elements to maintain motivation. For example, learning progress can be displayed in graphs and charts, allowing users to visually see how far they are progressing towards their goals. Furthermore, badges or points can be awarded upon achieving learning goals to provide a sense of accomplishment. In addition, encouraging messages and advice can be provided according to learning progress to maintain user motivation. In this way, the management department can visualize users' learning progress and provide interactive elements to maintain motivation.

[0065] The suggestion department can customize learning plans based on the user's learning environment. For example, for a user who prefers to study in a quiet environment, it can suggest studying in a library or study room. Conversely, for a user who finds studying effective while listening to music, it can suggest a learning plan that incorporates music. Furthermore, for a user who excels at online learning, it can suggest a learning plan that includes online courses or webinars. This allows the suggestion department to provide the optimal learning plan tailored to the user's learning environment.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The goal setting unit sets the user's goals. The goal setting unit provides an interface for the user to set specific learning goals, for example. The user can set goals such as passing the University of Tokyo entrance exam or obtaining the G-Test certification. Step 2: The input section is where users enter the results of mock exams or school tests. For example, users can take photos of their mock exam or school test results and upload them. Alternatively, test results can be entered manually. Step 3: The analysis unit analyzes the user's learning progress based on the results entered by the input unit. For example, the analysis unit uses AI to analyze the user's test results and evaluate the user's academic ability and strengths and weaknesses. Step 4: The proposal unit proposes a learning plan based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes a plan that takes into account the user's classes, learning progress, and areas of difficulty. The proposal unit uses AI to analyze the user's learning situation and generate the optimal learning plan. Step 5: The management department manages learning progress based on the learning plan proposed by the proposal department. The management department provides feedback, for example, based on the user's learning status and learning results. The management department uses AI to monitor the user's learning progress in real time and revise the learning plan as needed.

[0068] (Example of form 2) The autonomous learning support system according to an embodiment of the present invention is an AI agent that provides autonomous learning support for elementary, junior high, and high school students, university entrance exam candidates, their parents, and working adults. This autonomous learning support system allows users to set specific goals and input photos of mock exam or school test results, after which the AI ​​analyzes the distance between the user and their goals. Next, the AI ​​presents milestones to the user's goals and proposes a learning plan that takes into account the user's classes, learning progress, and areas of weakness. Furthermore, it provides feedback based on the user's learning status and results, and automatically suggests the next learning step. This mechanism enables users to reach their goals with a personalized, highly effective learning plan tailored to their academic ability, strengths and weaknesses, and learning progress. For example, it can strengthen specific problem areas based on test results and autonomously adjust the learning plan if delays occur. To address challenges such as maintaining motivation, lack of effective learning methods, difficulty in progress management, and the burden of guidance on parents, this AI agent has a function to autonomously modify the learning plan according to learning progress, visualizes progress, and provides interactive elements to maintain motivation. Furthermore, it provides specific feedback based on learning outcomes and automatically suggests the next learning steps. This enables the autonomous learning support system to reach its goals with the most effective learning plan tailored to the user's academic ability, strengths and weaknesses, and learning progress.

[0069] The autonomous learning support system according to this embodiment comprises a goal setting unit, an input unit, an analysis unit, a proposal unit, and a management unit. The goal setting unit sets the user's goals. The goal setting unit provides, for example, an interface for the user to set specific learning goals. The user can set goals such as passing the University of Tokyo entrance exam or obtaining the G-Test certification. The input unit inputs the results of mock exams and school tests. The input unit allows the user to, for example, take a picture of the results of mock exams or school tests and upload it. The input unit also allows the user to manually input test results. The analysis unit analyzes the user's learning progress based on the results entered by the input unit. The analysis unit, for example, uses AI to analyze the user's test results and evaluates the user's academic ability and strengths and weaknesses. The proposal unit proposes a learning plan based on the analysis results obtained by the analysis unit. The proposal unit proposes, for example, a plan that takes into account the user's classes, learning progress, and areas of weakness. The proposal unit uses AI to analyze the user's learning situation and generates an optimal learning plan. The management unit manages the learning progress based on the learning plan proposed by the proposal unit. The management department provides feedback, for example, based on the user's learning status and learning outcomes. The management department uses AI to monitor the user's learning progress in real time and revise the learning plan as needed. As a result, the autonomous learning support system according to the embodiment can efficiently set user goals, analyze learning progress, propose learning plans, and manage learning progress.

[0070] The goal-setting section allows users to set their own goals. For example, it provides an interface for users to set specific learning goals. Users can set goals such as passing the University of Tokyo entrance exam or obtaining the G-certification. The goal-setting section allows users to input deadlines and specific achievement criteria when setting goals. For example, if a user sets passing the University of Tokyo entrance exam as their goal, the goal-setting section provides an interface for inputting detailed information such as the subjects, scores, and study time required for passing. Furthermore, the goal-setting section has a function to visualize the progress towards the goals set by the user, allowing them to see at a glance how far they have progressed towards their goals. In addition, the goal-setting section has a function to send periodic reminders and encouraging messages to help users maintain their motivation to achieve their goals. This allows users to stay constantly aware of their goals and increase their motivation to learn.

[0071] The input section allows users to input results from mock exams and school tests. For example, users can upload photos of their mock exam or school test results. The input section also allows for manual input of test results. When users input test results, the input section provides an interface for entering detailed information such as the test type, date, subject, and score. For example, when a user inputs mock exam results, the input section provides a form for entering the mock exam name, date, and scores for each subject. Furthermore, when uploading photos of test results, the input section uses image recognition technology to automatically read the test results and complete the input content. This allows users to input accurate test results without effort. In addition, the input section saves the entered data as a history so that users can refer to past test results at any time as needed. This makes it easier for users to understand their learning progress and helps them plan their next studies.

[0072] The analysis unit analyzes the user's learning progress based on the results entered by the input unit. For example, the analysis unit uses AI to analyze the user's test results and evaluate the user's academic ability and strengths and weaknesses. Specifically, the AI ​​compares the user's test results with past data and analyzes the trend in scores and strengths and weaknesses in each subject. For example, if a user consistently scores high on math tests, the AI ​​will evaluate that subject as a strength, and conversely, if a user consistently scores low on English tests, it will evaluate that subject as a weakness. The AI ​​also analyzes in detail the strengths and weaknesses in specific areas and question formats based on the user's test results. For example, it may provide specific analysis results such as being good at calculation problems in math tests but weak at application problems. Furthermore, the analysis unit monitors the user's learning progress in real time and evaluates the learning progress and goal achievement. This allows the user to accurately understand their learning situation and use this information to create their next learning plan.

[0073] The Proposal Department proposes a learning plan based on the analysis results obtained by the Analysis Department. For example, the Proposal Department proposes a plan that takes into account the user's classes, learning progress, and areas of difficulty. The Proposal Department uses AI to analyze the user's learning situation and generate an optimal learning plan. Specifically, the AI ​​creates an efficient learning schedule based on the user's strengths and weaknesses and learning progress. For example, if a user has difficulty with English listening, the Proposal Department will propose a learning plan that focuses on strengthening listening skills. It also sets learning priorities according to the user's goal achievement deadline and provides a concrete action plan to efficiently advance learning. Furthermore, the Proposal Department proposes the most suitable learning methods and materials according to the user's learning style and preferences. For example, for users who prefer visual learning, it will propose video materials and materials that make extensive use of diagrams, and for users who prefer auditory learning, it will propose audio materials and podcasts. This allows users to efficiently advance their learning using a learning method that suits them.

[0074] The Management Department manages learning progress based on the learning plan proposed by the Proposal Department. The Management Department provides feedback, for example, based on the user's learning status and learning results. The Management Department uses AI to monitor the user's learning progress in real time and revise the learning plan as needed. Specifically, the AI ​​periodically evaluates the user's learning status and monitors their progress toward achieving their goals. For example, it evaluates whether the user is learning according to plan and whether their learning results are improving, and modifies the learning plan as necessary. The Management Department also provides regular feedback to the user, informing them of their learning progress and areas for improvement. For example, it provides specific advice on how the user should approach subjects they find difficult and how to adjust their learning pace. Furthermore, the Management Department has a function to send rewards and encouraging messages according to the level of achievement in order to maintain the user's motivation to learn. This allows users to always be aware of their learning status and to learn efficiently toward achieving their goals.

[0075] The proposal function can suggest study plans that take into account the user's classes, learning progress, and areas of difficulty. For example, the proposal function can suggest a plan that efficiently allocates study time, taking into account the user's class schedule. The proposal function can also evaluate the user's learning progress and suggest a learning plan that matches that progress. The proposal function can identify the user's areas of difficulty and suggest a plan that focuses on those areas. In this way, the proposal function can suggest study plans that are tailored to the user's individual learning situation.

[0076] The management department can provide feedback based on the user's learning progress and achievements. For example, the management department can monitor the user's learning progress in real time and provide appropriate feedback according to the progress. The management department can also evaluate the user's learning achievements and provide feedback based on those achievements. The management department can also suggest improvements to learning methods and content based on the user's learning progress. In this way, the management department can provide feedback tailored to the user's learning progress.

[0077] The management department can suggest the next learning steps and revise the learning plan. For example, the management department can suggest the next learning content based on the user's learning progress. The management department can also periodically review the user's learning plan and adjust it according to the progress. The management department can also evaluate the user's learning status and suggest revisions to the learning plan as needed. This allows the management department to suggest the next steps according to the user's learning progress and revise the plan.

[0078] The suggestion function can propose strengthening specific problem areas based on test results. For example, it can analyze a user's test results and propose a learning plan to strengthen specific problem areas. It can also identify problem areas where the user struggles and propose a plan to focus on learning those areas. It can also propose a learning plan to further strengthen problem areas where the user excels. In this way, the suggestion function can strengthen specific problem areas based on test results.

[0079] The suggestion unit can autonomously adjust the learning plan if delays occur. For example, the suggestion unit can monitor the user's learning progress in real time and automatically adjust the learning plan if delays occur. The suggestion unit can also periodically review the user's learning plan and adjust it according to the progress. The suggestion unit can also evaluate the user's learning status and suggest revisions to the learning plan as needed. In this way, the suggestion unit can autonomously adjust to delays in the learning plan.

[0080] The goal-setting unit can estimate the user's emotions and adjust the difficulty of the goals based on those emotions. For example, if the user is stressed, the goal-setting unit can set the difficulty of the goals lower to make it easier for the user to achieve a sense of accomplishment. If the user is relaxed, the goal-setting unit can also set the difficulty of the goals higher to provide a challenging goal. If the user is anxious, the goal-setting unit can also set short-term goals to enable them to be achieved quickly. In this way, the goal-setting unit can adjust the difficulty of the goals according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The goal-setting unit can analyze the user's past goal achievement history and select the optimal goal-setting method. For example, the goal-setting unit can suggest easily achievable goals based on data of goals the user has achieved in the past. The goal-setting unit can also analyze the reasons for goals the user has failed to achieve in the past and set goals that reflect improvements. The goal-setting unit can also suggest the optimal goal achievement period based on the user's past goal achievement history. In this way, the goal-setting unit can set optimal goals based on the user's past goal achievement history.

[0082] The goal-setting unit can customize goals based on the user's current learning status and areas of interest. For example, the goal-setting unit can analyze the user's current learning status and set achievable goals. The goal-setting unit can also set engaging goals based on the user's areas of interest. The goal-setting unit can also set goals that gradually increase in difficulty according to the user's learning progress. In this way, the goal-setting unit can customize goals based on the user's current learning status and areas of interest.

[0083] The goal-setting unit can estimate the user's emotions and determine the priority of goals based on those estimated emotions. For example, if the user is feeling stressed, the goal-setting unit will prioritize goals that are easier to achieve. If the user is relaxed, the goal-setting unit may also prioritize challenging goals. If the user is anxious, the goal-setting unit may also prioritize short-term goals. In this way, the goal-setting unit can determine the priority of goals according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The goal-setting unit can prioritize setting highly relevant goals by considering the user's geographical location information when setting goals. For example, the goal-setting unit can set highly relevant goals based on information about educational institutions in the user's area. The goal-setting unit can also set goals that make it easier to secure study time based on the user's commute route to school or work. The goal-setting unit can also set goals considering local events and exam schedules in the user's area. In this way, the goal-setting unit can set highly relevant goals based on the user's geographical location information.

[0085] The goal-setting unit can analyze the user's social media activity and set relevant goals when setting goals. For example, the goal-setting unit can set goals based on topics the user is interested in on social media. The goal-setting unit can also set goals by referring to goals achieved by the user's followers and friends. The goal-setting unit can also analyze the content of the user's social media posts and set goals that will attract their attention. In this way, the goal-setting unit can set relevant goals based on the user's social media activity.

[0086] The input unit can estimate the user's emotions and adjust the timing of data acquisition based on the estimated emotions. For example, if the user is relaxed, the input unit may prompt the user to input data periodically. If the user is stressed, the input unit may reduce the frequency of data input. If the user is focused, the input unit may adjust the timing of data input to avoid interfering with learning. In this way, the input unit can adjust the timing of data acquisition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The input unit can analyze the user's past test results and select the optimal input method. For example, it might prioritize suggesting input methods the user has used in the past (such as photos or text). It can also suggest the most efficient input method based on the user's past test results. Furthermore, it can analyze the user's past input history and suggest the optimal input timing. This allows the input unit to select the optimal input method based on the user's past test results.

[0088] The input unit can filter test results based on the user's current learning status and areas of interest. For example, it can analyze the user's current learning status and input only relevant test results. It can also prioritize inputting important test results based on the user's areas of interest. Furthermore, it can input only the necessary test results according to the user's learning progress. This allows the input unit to filter test results based on the user's current learning status and areas of interest.

[0089] The input unit can estimate the user's emotions and prioritize input data based on those emotions. For example, if the user is stressed, the input unit may postpone inputting less important data. If the user is relaxed, the input unit may prioritize inputting more important data. If the user is anxious, the input unit may prioritize data that needs to be entered quickly. In this way, the input unit can prioritize input data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The input unit can prioritize inputting highly relevant data when entering test results, taking into account the user's geographical location. For example, the input unit can input highly relevant test results based on information about educational institutions in the user's area. The input unit can also input test results that make it easier to allocate study time based on the user's commute route to school or work. The input unit can also input test results considering local events and exam schedules. In this way, the input unit can prioritize inputting highly relevant data based on the user's geographical location.

[0091] The input unit can analyze the user's social media activity and input relevant data when entering test results. For example, the input unit can input test results based on topics the user is interested in on social media. The input unit can also input test results based on goals achieved by the user's followers and friends. The input unit can also analyze the content of the user's social media posts and input interesting test results. In this way, the input unit can input relevant data based on the user's social media activity.

[0092] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. If the user is stressed, the analysis unit can perform a simplified analysis. If the user is focused, the analysis unit can perform a focused analysis. This allows the analysis unit to adjust the analysis criteria according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The analysis department can improve the accuracy of its analysis by considering the interrelationships of test results. For example, the analysis department can analyze the interrelationships of test results and prioritize the analysis of highly relevant data. The analysis department can also improve the accuracy of its analysis by considering the interrelationships of test results. The analysis department can also select the optimal analysis method based on the interrelationships of test results. As a result, the analysis department improves the accuracy of its analysis by considering the interrelationships of test results.

[0094] The analysis department can perform analyses while considering user attribute information. For example, the analysis department can select the optimal analysis method by considering the user's age and gender. The analysis department can also select the optimal analysis method based on the user's learning history. The analysis department can also improve the accuracy of the analysis by considering user attribute information. In this way, the analysis department can improve the accuracy of the analysis by considering user attribute information.

[0095] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit will prioritize displaying detailed analysis results. If the user is stressed, the analysis unit can also prioritize displaying simplified analysis results. If the user is focused, the analysis unit can also prioritize displaying key analysis results. In this way, the analysis unit can adjust the display order of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The analysis department can perform analyses while considering the geographical distribution of test results. For example, the analysis department can analyze the geographical distribution of test results to understand regional trends. The analysis department can also select the optimal analysis method while considering the geographical distribution of test results. Based on the geographical distribution of test results, the analysis department can also propose countermeasures for each region. In this way, the analysis department can understand regional trends by considering the geographical distribution of test results.

[0097] The analysis department can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis department can improve the accuracy of its analysis by referring to relevant literature. The analysis department can also select the optimal analysis method based on relevant literature. Furthermore, the analysis department can improve the reliability of its analysis results by referring to relevant literature. Thus, the analysis department improves the accuracy of its analysis by referring to relevant literature.

[0098] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is stressed, the suggestion unit may provide simplified suggestions. If the user is focused, the suggestion unit may provide focused suggestions. This allows the suggestion unit to adjust the way it presents suggestions according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The proposal department can adjust the level of detail in its proposals based on the importance of the learning plans. For example, it can provide detailed proposals for highly important learning plans, and simplified proposals for less important ones. The proposal department can also adjust the level of detail in its proposals in stages according to the importance of the learning plans. This allows the proposal department to adjust the level of detail in its proposals according to the importance of the learning plans.

[0100] The proposal function can apply different proposal algorithms depending on the category of the learning plan during the proposal process. For example, the proposal function can apply a mathematical proposal algorithm to learning plans for science subjects. It can also apply a linguistic proposal algorithm to learning plans for humanities subjects. It can also apply a practical proposal algorithm to learning plans for practical skills subjects. This allows the proposal function to apply the most suitable proposal algorithm according to the category of the learning plan.

[0101] The suggestion unit can estimate the user's emotions and adjust the length of its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is stressed, the suggestion unit can provide simplified suggestions. If the user is focused, the suggestion unit can provide focused suggestions. This allows the suggestion unit to adjust the length of its suggestions according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The proposal department can determine the priority of proposals based on the submission timing of the learning plans. For example, the proposal department will prioritize proposals for learning plans with approaching deadlines. The proposal department can also postpone proposals for learning plans with later deadlines. The proposal department can also adjust the priority of proposals in stages according to the submission timing. This allows the proposal department to determine the priority of proposals according to the submission timing of the learning plans.

[0103] The proposal department can adjust the order of proposals based on the relevance of the learning plans. For example, the proposal department can prioritize proposals for highly relevant learning plans. The proposal department can also postpone proposals for less relevant learning plans. The proposal department can also adjust the order of proposals in stages according to the relevance of the learning plans. This allows the proposal department to adjust the order of proposals according to the relevance of the learning plans.

[0104] The management unit can estimate the user's emotions and adjust the learning progress management method based on the estimated user emotions. For example, if the user is relaxed, the management unit can perform detailed progress management. If the user is stressed, the management unit can perform simplified progress management. If the user is focused, the management unit can perform focused progress management. In this way, the management unit can adjust the learning progress management method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The management department can select the optimal management method when managing learning progress by referring to the user's past learning history. For example, the management department can select the optimal progress management method based on the user's past learning history. The management department can also propose an efficient progress management method based on the user's past learning history. The management department can also analyze the user's past learning history and select the optimal progress management method. In this way, the management department can select the optimal management method based on the user's past learning history.

[0106] The management unit can customize the management methods based on the user's current learning status when managing learning progress. For example, the management unit can analyze the user's current learning status and select the optimal progress management method. The management unit can also propose an efficient progress management method based on the user's current learning status. The management unit can also customize the progress management method considering the user's current learning status. In this way, the management unit can customize the management methods based on the user's current learning status.

[0107] The management unit can estimate the user's emotions and prioritize learning progress based on those emotions. For example, if the user is relaxed, the management unit will prioritize high-priority progress. If the user is stressed, the management unit may postpone lower-priority progress. If the user is focused, the management unit may prioritize key progress. This allows the management unit to prioritize learning progress according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The management department can select the optimal management method when managing learning progress, taking into account the user's geographical location. For example, the management department can select the optimal progress management method based on information about educational institutions in the user's area. The management department can also propose a progress management method that makes it easier to secure study time based on the user's commute route to school or work. The management department can also select a progress management method considering local events and exam schedules in the user's area. In this way, the management department can select the optimal management method based on the user's geographical location.

[0109] The management department can analyze users' social media activity and propose management methods when managing learning progress. For example, the management department can propose progress management methods based on topics that users are interested in on social media. The management department can also propose progress management methods by referring to goals achieved by the user's followers and friends. The management department can also analyze the content of users' social media posts and propose progress management methods that are engaging. In this way, the management department can propose management methods based on users' social media activity.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The suggestion function can customize learning plans based on the user's learning style. For example, it can suggest learning plans that make extensive use of diagrams and graphs to visual learners, plans that incorporate audio and podcasts to auditory learners, and plans that include practical exercises and fieldwork to experiential learners. This allows the suggestion function to provide the optimal learning plan tailored to the user's learning style.

[0112] The management department can compare a user's learning progress with other users and provide feedback on their relative progress. For example, if a user's progress is behind that of other users with the same goal, the management department can suggest additional study time. Conversely, if a user is progressing quickly, the management department can offer advice on how to move on to the next step. Furthermore, by comparing users in the same region or school, the management department can understand learning trends specific to each region or school and provide appropriate feedback. This allows the management department to relatively evaluate users' learning progress and provide appropriate feedback.

[0113] The proposal department can predict the effectiveness of learning plans based on the user's learning history and propose the optimal plan. For example, it can analyze the effectiveness of past learning plans and prioritize proposing plans that were highly effective. Furthermore, if a particular learning method proved effective based on the user's learning history, it can propose a plan incorporating that method. It can also propose a plan that focuses on strengthening areas where the user struggles, based on their learning history. In this way, the proposal department can provide the optimal learning plan based on the user's learning history.

[0114] The management department can visualize users' learning progress and provide interactive elements to maintain motivation. For example, learning progress can be displayed in graphs and charts, allowing users to visually see how far they are progressing towards their goals. Furthermore, badges or points can be awarded upon achieving learning goals to provide a sense of accomplishment. In addition, encouraging messages and advice can be provided according to learning progress to maintain user motivation. In this way, the management department can visualize users' learning progress and provide interactive elements to maintain motivation.

[0115] The suggestion department can customize learning plans based on the user's learning environment. For example, for a user who prefers to study in a quiet environment, it can suggest studying in a library or study room. Conversely, for a user who finds studying effective while listening to music, it can suggest a learning plan that incorporates music. Furthermore, for a user who excels at online learning, it can suggest a learning plan that includes online courses or webinars. This allows the suggestion department to provide the optimal learning plan tailored to the user's learning environment.

[0116] The goal-setting unit can estimate the user's emotions and adjust the goal-achievement method based on those emotions. For example, if the user is stressed, it can break down the steps to achieving the goal into smaller steps to make it easier to feel a sense of accomplishment. If the user is relaxed, it can also broaden the steps to achieving the goal and provide a more challenging goal. If the user is anxious, it can set short-term goals to enable them to be achieved quickly. In this way, the goal-setting unit can adjust the goal-achievement method according to the user's emotions.

[0117] The input unit can estimate the user's emotions and adjust the format of the input data based on those emotions. For example, if the user is relaxed, it may prompt for detailed data entry. If the user is stressed, it may suggest simplified data entry. If the user is focused, it may prompt for focused data entry. In this way, the input unit can adjust the format of the input data according to the user's emotions.

[0118] The analysis unit can estimate the user's emotions and adjust the feedback method of the analysis results based on the estimated emotions. For example, if the user is relaxed, detailed feedback can be provided. If the user is stressed, simplified feedback can be provided. If the user is focused, focused feedback can be provided. In this way, the analysis unit can adjust the feedback method of the analysis results according to the user's emotions.

[0119] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is relaxed, suggestions will be made more frequently. If the user is stressed, the frequency of suggestions may be reduced. If the user is focused, important suggestions may be prioritized. In this way, the suggestion function can adjust the timing of suggestions according to the user's emotions.

[0120] The management department can estimate the user's emotions and adjust the way learning progress is reported based on those estimates. For example, if the user is relaxed, detailed progress reports can be provided. If the user is stressed, simplified progress reports can be provided. If the user is focused, focused progress reports can be provided. This allows the management department to adjust the way learning progress is reported according to the user's emotions.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The goal setting unit sets the user's goals. The goal setting unit provides an interface for the user to set specific learning goals, for example. The user can set goals such as passing the University of Tokyo entrance exam or obtaining the G-Test certification. Step 2: The input section is where users enter the results of mock exams or school tests. For example, users can take photos of their mock exam or school test results and upload them. Alternatively, test results can be entered manually. Step 3: The analysis unit analyzes the user's learning progress based on the results entered by the input unit. For example, the analysis unit uses AI to analyze the user's test results and evaluate the user's academic ability and strengths and weaknesses. Step 4: The proposal unit proposes a learning plan based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes a plan that takes into account the user's classes, learning progress, and areas of difficulty. The proposal unit uses AI to analyze the user's learning situation and generate the optimal learning plan. Step 5: The management department manages learning progress based on the learning plan proposed by the proposal department. The management department provides feedback, for example, based on the user's learning status and learning results. The management department uses AI to monitor the user's learning progress in real time and revise the learning plan as needed.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the goal setting unit, input unit, analysis unit, proposal unit, and management unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the goal setting unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the user to set specific learning goals. The input unit allows the user to take pictures of mock exam or school test results using the camera 42 of the smart device 14 and upload them. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses AI to analyze the user's test results and evaluate their academic ability and strengths and weaknesses. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a plan that takes into account the user's classes, learning progress, and areas of difficulty. The management unit is implemented by the control unit 46A of the smart device 14 and provides feedback according to the user's learning status and learning results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the goal setting unit, input unit, analysis unit, proposal unit, and management unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the goal setting unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for the user to set specific learning goals. The input unit allows the user to take pictures of mock exam or school test results using the camera 42 of the smart glasses 214 and upload them. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses AI to analyze the user's test results and evaluate their academic ability and strengths and weaknesses. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a plan that takes into account the user's classes, learning progress, and areas of difficulty. The management unit is implemented by the control unit 46A of the smart glasses 214 and provides feedback according to the user's learning status and learning results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the goal setting unit, input unit, analysis unit, proposal unit, and management unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the goal setting unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for the user to set specific learning goals. The input unit allows the user to take pictures of mock exam or school test results using the camera 42 of the headset terminal 314 and upload them. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses AI to analyze the user's test results and evaluate their academic ability and strengths and weaknesses. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a plan that takes into account the user's classes, learning progress, and areas of difficulty. The management unit is implemented by the control unit 46A of the headset terminal 314 and provides feedback according to the user's learning status and learning results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the goal setting unit, input unit, analysis unit, proposal unit, and management unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the goal setting unit is implemented by the control unit 46A of the robot 414 and provides an interface for the user to set specific learning goals. The input unit allows the robot 414's camera 42 to take and upload photos of mock exam or school test results. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses AI to analyze the user's test results and evaluate their academic ability and strengths and weaknesses. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a plan that takes into account the user's classes, learning progress, and areas of difficulty. The management unit is implemented by the control unit 46A of the robot 414 and provides feedback according to the user's learning status and learning results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A goal setting unit for setting user goals, An input section for entering the results of mock exams and school tests, An analysis unit analyzes the user's learning progress based on the results input by the aforementioned input unit, A proposal unit proposes a learning plan based on the analysis results obtained by the aforementioned analysis unit, The system includes a management unit that manages learning progress based on the learning plan proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We propose a study plan that takes into account the user's lessons, learning progress, and areas of difficulty. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned management department, Provide feedback based on the user's learning progress and achievements. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned management department, Present the next learning steps and revise the learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We propose strengthening specific problem areas based on test results. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Autonomous adjustments to the learning plan in case of delays. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned target setting unit, It estimates the user's emotions and adjusts the difficulty level of the goal based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned target setting unit, Analyze the user's past goal achievement history to select the optimal goal setting method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned target setting unit, When setting goals, customize them based on the user's current learning progress and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned target setting unit, It estimates user emotions and determines goal priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned target setting unit, When setting goals, prioritize highly relevant goals by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned target setting unit, When setting goals, analyze users' social media activity and set relevant goals. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned input unit is The system estimates the user's emotions and adjusts the timing of input data acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned input unit is Analyze the user's past test results and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned input unit is When entering test results, filtering is performed based on the user's current learning status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned input unit is It estimates the user's emotions and prioritizes input data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned input unit is When entering test results, the system prioritizes inputting highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned input unit is When entering test results, analyze the user's social media activity and input relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is During analysis, consider the interrelationships between test results to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is When performing analysis, user attribute information should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is It estimates the user's emotions and adjusts the display order of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is When conducting the analysis, the geographical distribution of the test results should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is During analysis, refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, apply a different proposal algorithm depending on the category of the learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When submitting a proposal, prioritize the proposals based on when the learning plan will be submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned management department, It estimates the user's emotions and adjusts the learning progress management method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned management department, When managing learning progress, the system selects the optimal management method by referring to the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned management department, When managing learning progress, customize the management method based on the user's current learning status. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned management department, It estimates the user's emotions and prioritizes learning progress based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned management department, When managing learning progress, the optimal management method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned management department, When managing learning progress, we analyze users' social media activity and propose management methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A goal setting unit for setting user goals, An input section for entering the results of mock exams and school tests, An analysis unit analyzes the user's learning progress based on the results input by the aforementioned input unit, A proposal unit proposes a learning plan based on the analysis results obtained by the aforementioned analysis unit, The system includes a management unit that manages learning progress based on the learning plan proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned proposal section is, We propose a study plan that takes into account the user's lessons, learning progress, and areas of difficulty. The system according to feature 1.

3. The aforementioned management department, Provide feedback based on the user's learning progress and achievements. The system according to feature 1.

4. The aforementioned management department, Present the next learning steps and revise the learning plan. The system according to feature 1.

5. The aforementioned proposal section is, We propose strengthening specific problem areas based on test results. The system according to feature 1.

6. The aforementioned proposal section is, Autonomous adjustments to the learning plan in case of delays. The system according to feature 1.

7. The aforementioned target setting unit, It estimates the user's emotions and adjusts the difficulty level of the goal based on those estimated emotions. The system according to feature 1.

8. The aforementioned target setting unit, Analyze the user's past goal achievement history to select the optimal goal setting method. The system according to feature 1.

9. The aforementioned target setting unit, When setting goals, customize them based on the user's current learning progress and areas of interest. The system according to feature 1.

10. The aforementioned target setting unit, It estimates user emotions and determines goal priorities based on those estimated emotions. The system according to feature 1.

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