System

Abstract

An object of the system according to the embodiment is to create a personalized learning plan based on a user's selection and provide feedback in real time.SOLUTION: A system includes a selection unit, a creation unit, a feedback unit, and an execution unit. The selection unit selects a specialized field that the user wants to learn. The creation unit creates a learning plan based on the information selected by the selection unit. The feedback unit provides feedback in real time based on the learning plan created by the creation unit. The practice portion provides an opportunity to practice what is learned based on the feedback provided by the feedback portion.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies lack the ability to create learning plans tailored to individual needs and provide real-time feedback when learning specialized fields, leaving room for improvement.

[0005] The system according to the embodiment aims to create a personalized learning plan based on the user's selections and provide feedback in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a selection unit, a creation unit, a feedback unit, and a practice unit. The selection unit selects a specialty field that a user wants to learn. The creation unit creates a study plan based on the information selected by the selection unit. The feedback unit provides real-time feedback based on the study plan created by the creation unit. The practice unit provides an opportunity to practice what has been learned based on the feedback provided by the feedback unit. [Effects of the Invention]

[0007] An embodiment of the system can create a personalized learning plan based on user selections and provide real-time feedback. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An educational support system according to an embodiment of the present invention utilizes AI to create opportunities for people around the world to receive a university education. The educational support system allows users to select a field of study they wish to study, and AI creates a study plan, provides real-time feedback, and provides opportunities to practice what they have learned. For example, the educational support system allows users to select a field of study they wish to study. The user can choose from a variety of fields, such as physics, chemistry, and economics. This information is input into the AI. The educational support system then creates an appropriate study plan based on the selected field of study. The AI ​​provides an optimal study plan, taking into account the user's learning progress and level of understanding. For example, the AI ​​can provide materials and assignments appropriate for beginner, intermediate, and advanced levels. Furthermore, the educational support system provides real-time feedback based on the user's learning progress. For example, when a user submits an assignment, the AI ​​can evaluate the content and suggest improvements or additional learning resources. This allows users to effectively deepen their knowledge while learning at their own pace. The educational support system also provides opportunities for users to practice what they have learned. For example, the AI ​​can introduce research projects and internship opportunities, allowing users to apply their knowledge in real-world situations. This allows users to acquire not only theoretical knowledge but also practical skills. This allows the educational support system to allow people to learn and grow throughout their lives by paying an hourly fee to the AI, instead of the high costs of studying abroad. This allows the educational support system to provide people around the world with the opportunity to receive a university education and contribute to the dramatic development of humanity. For example, by paying an hourly fee, users can receive expert instruction from the AI. This allows users to receive a high-quality education while reducing their financial burden.

[0029] An educational support system according to an embodiment includes a selection unit, a creation unit, a feedback unit, and a practice unit. The selection unit selects a specialized field that a user wants to study. The specialized field that the user wants to study includes, but is not limited to, science, technology, business, art, etc. The selection unit allows the user to select from fields such as physics, chemistry, and economics. The selection unit can also select a suitable field based on the user's interests and career. For example, the selection unit can recommend an appropriate field based on the user's past studies and career goals. The creation unit uses AI to create a study plan based on the information selected by the selection unit. The creation unit provides an optimal study plan, taking into account, for example, the user's learning progress and level of understanding. For example, the creation unit provides learning materials and assignments according to beginner, intermediate, and advanced levels. The creation unit can also adjust the level of detail of the plan based on the user's learning goals. For example, the creation unit can provide a specific and detailed plan for a user with short-term goals and a plan covering a wide range of content for a user with long-term goals. The feedback unit uses AI to provide real-time feedback based on the study plan created by the creation unit. For example, when a user submits an assignment, the feedback unit evaluates the content and provides suggestions for improvement or additional learning resources. For example, if a user struggles with a particular assignment, the feedback unit may suggest specific suggestions for improvement for that assignment. The feedback unit may also provide advice for adjusting the user's learning pace according to the user's learning progress. The practice unit uses AI to provide opportunities to practice what the user has learned based on the feedback provided by the feedback unit. For example, the practice unit may introduce research projects and internship opportunities, providing the user with opportunities to apply the knowledge they have learned in real-world situations. For example, the practice unit may introduce collaborations with companies or projects in university laboratories. The practice unit may also provide flexible practice opportunities according to the user's living situation. As a result, the education support system according to the embodiment enables effective learning by allowing users to select a specialized field they want to study, create a study plan, receive feedback in real time, and provide opportunities to practice what they have learned.

[0030] The selection unit can select a suitable field based on the user's interests and career. For example, the selection unit selects a suitable field based on the user's interests and career. For example, the selection unit recommends a suitable field based on the user's past studies and career goals. The selection unit can also conduct a survey to identify the user's interests. For example, the selection unit provides a survey including questions about topics and fields in which the user is interested, and selects a suitable field based on the results. Furthermore, the selection unit can analyze the user's past history and recommend a suitable field. For example, the selection unit recommends related fields based on the subjects the user has taken in the past and the qualifications he or she has obtained. This enables more effective learning by selecting a suitable field based on the user's interests and career.

[0031] The creation unit can provide an appropriate study plan while taking into consideration the user's learning progress and level of understanding. The creation unit provides an appropriate study plan while taking into consideration, for example, the user's learning progress and level of understanding. For example, the creation unit creates an optimal study plan based on the content the user has previously learned and test results. The creation unit can also adjust the study plan by referring to the user's self-assessment and the teacher's assessment. For example, the creation unit provides a plan that focuses on areas that the user feels are weak in their self-assessment. The creation unit can also adjust the pace of the study plan according to the user's learning progress. For example, the creation unit provides additional assignments if the user is progressing quickly, and provides supplementary materials if the user is falling behind. In this way, an optimal study plan can be provided by taking into consideration the user's learning progress and level of understanding.

[0032] The feedback unit can evaluate the content of an assignment submitted by a user and provide suggestions for improvement or additional learning resources. For example, when a user submits an assignment, the feedback unit evaluates the content and provides suggestions for improvement or additional learning resources. For example, the feedback unit uses AI to analyze the assignment submitted by the user and suggest specific suggestions for improvement. Furthermore, if a user is struggling with a particular assignment, the feedback unit can provide additional learning resources for that assignment. For example, the feedback unit can provide reference materials or additional assignments to support the user in deepening their understanding. Furthermore, the feedback unit can provide advice for adjusting the learning pace according to the user's learning progress. For example, if the user's progress is slow, the feedback unit can provide specific advice for increasing the learning pace, and if progress is going well, the feedback unit can provide instructions for moving on to the next step. In this way, the quality of learning is improved by providing appropriate feedback when the user submits an assignment.

[0033] The practical application section can introduce research projects and internship opportunities, providing users with opportunities to apply the knowledge they have learned in real-world situations. For example, the practical application section can introduce research projects and internship opportunities, providing users with opportunities to apply the knowledge they have learned in real-world situations. For example, the practical application section can introduce collaborations with companies and projects in university laboratories. The practical application section can also provide opportunities for online projects and remote internships. For example, the practical application section can introduce online projects that users can participate in from home, providing opportunities to acquire practical skills. The practical application section can also provide flexible practical opportunities according to users' living situations. For example, the practical application section can provide users who are raising children with projects that can be completed in a short amount of time or internships with flexible schedules. This allows users to acquire practical skills by providing them with opportunities to apply the knowledge they have learned in real-world situations.

[0034] The creation unit can provide teaching materials and assignments according to beginner, intermediate, and advanced levels. The creation unit provides, for example, teaching materials and assignments according to beginner, intermediate, and advanced levels. For example, the creation unit provides beginner-level users with teaching materials for learning basic knowledge, and intermediate-level users with teaching materials for learning applied knowledge. The creation unit can also provide advanced-level users with teaching materials for deepening specialized knowledge. For example, the creation unit can provide beginner-level users with teaching materials for learning basic concepts and terminology, and intermediate-level users with practical assignments and case studies. The creation unit can also provide advanced-level users with advanced theories and research papers. This enables effective learning by providing teaching materials and assignments according to the user's level.

[0035] The selection unit can analyze the user's past learning history and recommend an appropriate field. The selection unit, for example, analyzes the user's past learning history and recommends an appropriate field. For example, the selection unit recommends related new fields based on fields the user has studied in the past. The selection unit can also preferentially recommend fields in which the user has received high marks in the past. The selection unit can also recommend fields that the user has struggled with in the past, avoiding such fields. For example, the selection unit analyzes data such as the user's courses, grades, and study time to recommend the most appropriate field. In this way, the selection unit can recommend the most appropriate field by analyzing the user's past learning history.

[0036] The selection unit can filter suitable fields based on the user's current occupation and living situation. The selection unit filters suitable fields based on, for example, the user's current occupation and living situation. For example, the selection unit preferentially recommends fields related to the user's current occupation. The selection unit can also recommend fields that allow for flexible learning depending on the user's living situation (e.g., childcare). The selection unit can also recommend fields that are useful for career advancement based on the user's career goals. For example, the selection unit analyzes data such as the user's type of occupation, working hours, and home environment to filter out optimal fields. This enables more effective learning by filtering suitable fields based on the user's current occupation and living situation.

[0037] The selection unit can provide an appropriate field selection means according to the user's input method. The selection unit provides an appropriate field selection means according to, for example, the user's input method. For example, when the user uses voice input, the selection unit selects a field using voice recognition technology. Furthermore, when the user uses text input, the selection unit can also select a field using keyword search. Furthermore, when the user uses image input, the selection unit can also select a related field using image analysis technology. For example, the selection unit provides an optimal field selection means according to a specific input method such as voice input, text input, or image input. This enables more effective learning by providing an optimal field selection means according to the user's input method.

[0038] The selection unit can prioritize selecting highly relevant fields by taking into account the user's geographical location information. The selection unit, for example, prioritizes selecting highly relevant fields by taking into account the user's geographical location information. For example, if the user lives in a specific area, the selection unit can recommend fields related to that area. Furthermore, if the user is traveling, the selection unit can recommend fields related to the travel destination. Furthermore, if the user is interested in a specific country or area, the selection unit can recommend fields related to that area. For example, the selection unit analyzes the user's geographical location information, such as GPS data or address information, and recommends the most appropriate field. In this way, highly relevant fields can be prioritized by taking into account the user's geographical location information.

[0039] The selection unit can analyze the user's social media activity and recommend related fields. The selection unit, for example, analyzes the user's social media activity and recommends related fields. For example, the selection unit recommends fields related to accounts the user follows on social media. The selection unit can also analyze the content of the user's posts and recommend fields that the user is likely to be interested in. The selection unit can also recommend related fields based on the activities of the user's friends. For example, the selection unit analyzes data such as the content of the user's posts, the number of likes, and the number of followers, and recommends the most appropriate field. In this way, related fields can be recommended by analyzing the user's social media activity.

[0040] The selection unit can customize the category selection method by reflecting the user's past feedback. The selection unit customizes the category selection method by reflecting the user's past feedback, for example. For example, the selection unit preferentially recommends categories that the user has previously given high ratings to. The selection unit can also recommend categories that the user has previously given low ratings to, avoiding them. The selection unit can also adjust the category selection algorithm based on the user's feedback. For example, the selection unit analyzes data such as the user's evaluation comments, scores, and improvement suggestions to recommend the most appropriate category. In this way, the category selection method can be customized by reflecting the user's past feedback.

[0041] The creation unit can provide learning materials and assignments that match the user's learning style when creating a study plan. For example, the creation unit provides learning materials and assignments that match the user's learning style when creating a study plan. For example, if the user is a visual learner, the creation unit can provide visual learning materials. Also, if the user is an auditory learner, the creation unit can provide audio learning materials. Also, if the user prefers hands-on learning, the creation unit can provide experiment- or project-based assignments. For example, the creation unit provides optimal learning materials and assignments depending on the user's learning style, such as visual, auditory, or experiential. This enables more effective learning by providing learning materials and assignments that match the user's learning style.

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

[0043] The education support system may further include a history analysis unit that analyzes the user's learning history. The history analysis unit collects and analyzes data such as the content the user has learned in the past, grades, and study time. For example, the history analysis unit may identify areas in which the user has previously received high marks and create a new learning plan related to those areas. The history analysis unit may also provide feedback to help the user avoid areas in which the user struggled in the past. Furthermore, the history analysis unit may analyze the user's learning patterns and suggest an optimal learning schedule. This makes it possible to utilize the user's past learning history to provide a more effective learning plan.

[0044] The education support system may further include a social analysis unit that analyzes the user's social media activity. The social analysis unit analyzes the accounts and posts the user follows on social media and recommends related learning topics. For example, the social analysis unit may suggest areas of interest based on topics the user frequently likes or comments on. The social analysis unit may also provide related learning resources based on the activities of the user's friends and followers. Furthermore, the social analysis unit may analyze the content of the user's posts and provide feedback to increase motivation to study. This makes it possible to utilize the user's social media activity to provide a more personalized learning experience.

[0045] The education support system may further include a location information analysis unit that takes into account the user's geographic location information. The location information analysis unit recommends relevant learning fields and resources based on the user's current geographic location. For example, if the user lives in a particular area, the location information analysis unit may suggest learning resources and events related to that area. If the user is traveling, the location information analysis unit may also provide learning opportunities related to the user's travel destination. Furthermore, if the user is interested in a particular country or region, the location information analysis unit may also provide learning resources related to the culture and history of that region. In this way, the user's geographic location information can be utilized to provide a more relevant learning experience.

[0046] The educational support system may further include a style adaptation unit that provides learning materials and assignments according to the user's learning style. The style adaptation unit takes into account that users have different learning styles, such as visual learners, auditory learners, and experiential learners, and provides learning materials and assignments according to the learning styles. For example, the style adaptation unit may provide visual learning materials to visual learners and audio learning materials to auditory learners. It may also provide experiments or project-based assignments to experiential learners. Furthermore, the style adaptation unit may adjust the pace and content of the learning plan based on the user's learning style. This allows for more effective learning by providing learning materials and assignments according to the user's learning style.

[0047] The education support system can further include a goal adaptation unit that customizes the study plan according to the user's learning goals. The goal adaptation unit provides a specific and detailed plan if the user has a short-term goal, and a plan covering a wide range of content if the user has a long-term goal. For example, if the user's goal is to pass a qualification exam, the goal adaptation unit can provide a study plan specialized for that exam. In addition, if the user is aiming to advance their career, the goal adaptation unit can also provide a plan to deepen skills and knowledge related to that field. Furthermore, the goal adaptation unit can flexibly adjust the study plan according to the user's progress. This enables more effective learning by providing a study plan customized according to the user's learning goals.

[0048] The education support system may further include a progress display unit that visually displays the user's learning progress. The progress display unit visually displays the user's current progress in learning, thereby increasing motivation to learn. For example, the progress display unit may display the tasks the user has completed and the goals they have achieved in the form of a graph or chart. The progress display unit may also visually indicate the next tasks or goals the user should tackle. Furthermore, the progress display unit may analyze the user's learning pace and suggest an appropriate pace of progress. In this way, visually displaying the user's learning progress can increase motivation to learn and support effective learning.

[0049] The education support system can further include an environment optimization unit that optimizes the user's learning environment. The environment optimization unit analyzes the user's learning environment and provides advice to provide an optimal learning environment. For example, the environment optimization unit analyzes the user's learning location, time of day, ambient noise level, etc., and proposes an optimal learning environment. The environment optimization unit can also provide specific advice to create an environment that makes it easier for the user to concentrate. Furthermore, the environment optimization unit can adjust the learning plan according to the user's learning environment. This allows for more effective learning by optimizing the user's learning environment.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The selector selects the field of study the user wants to study. Fields of study that the user wants to study include science, technology, business, arts, etc. The selector allows the user to choose from fields such as physics, chemistry, and economics, and can also select suitable fields based on the user's interests and career. For example, the selector recommends suitable fields based on the user's past studies and career goals. Step 2: The creation unit uses AI to create a study plan based on the information selected by the selection unit. The creation unit provides the optimal study plan, taking into account the user's learning progress and level of understanding. For example, it provides learning materials and assignments according to beginner, intermediate, and advanced levels, and adjusts the level of detail of the plan based on the user's learning goals. It provides specific and detailed plans for users with short-term goals, and plans covering a wide range of content for users with long-term goals. Step 3: The feedback unit uses AI to provide real-time feedback based on the learning plan created by the creation unit. When the user submits an assignment, the feedback unit evaluates the content and provides suggestions for improvement and additional learning resources. For example, if the user is struggling with a particular assignment, the feedback unit will suggest specific improvements for that assignment and provide advice on adjusting the learning pace according to the user's progress. Step 4: The Practice Department uses AI to provide opportunities for users to practice what they have learned based on the feedback provided by the Feedback Department. The Practice Department introduces research projects and internship opportunities, providing users with opportunities to apply the knowledge they have learned in real-world situations. For example, it introduces collaborations with companies and projects in university laboratories, providing flexible practice opportunities that suit the user's living situation.

[0052] (Example 2) An educational support system according to an embodiment of the present invention utilizes AI to create opportunities for people around the world to receive a university education. The educational support system allows users to select a field of study they wish to study, and AI creates a study plan, provides real-time feedback, and provides opportunities to practice what they have learned. For example, the educational support system allows users to select a field of study they wish to study. The user can choose from a variety of fields, such as physics, chemistry, and economics. This information is input into the AI. The educational support system then creates an appropriate study plan based on the selected field of study. The AI ​​provides an optimal study plan, taking into account the user's learning progress and level of understanding. For example, the AI ​​can provide materials and assignments appropriate for beginner, intermediate, and advanced levels. Furthermore, the educational support system provides real-time feedback based on the user's learning progress. For example, when a user submits an assignment, the AI ​​can evaluate the content and suggest improvements or additional learning resources. This allows users to effectively deepen their knowledge while learning at their own pace. The educational support system also provides opportunities for users to practice what they have learned. For example, the AI ​​can introduce research projects and internship opportunities, allowing users to apply their knowledge in real-world situations. This allows users to acquire not only theoretical knowledge but also practical skills. This allows the educational support system to allow people to learn and grow throughout their lives by paying an hourly fee to the AI, instead of the high costs of studying abroad. This allows the educational support system to provide people around the world with the opportunity to receive a university education and contribute to the dramatic development of humanity. For example, by paying an hourly fee, users can receive expert instruction from the AI. This allows users to receive a high-quality education while reducing their financial burden.

[0053] An educational support system according to an embodiment includes a selection unit, a creation unit, a feedback unit, and a practice unit. The selection unit selects a specialized field that a user wants to study. The specialized field that the user wants to study includes, but is not limited to, science, technology, business, art, etc. The selection unit allows the user to select from fields such as physics, chemistry, and economics. The selection unit can also select a suitable field based on the user's interests and career. For example, the selection unit can recommend an appropriate field based on the user's past studies and career goals. The creation unit uses AI to create a study plan based on the information selected by the selection unit. The creation unit provides an optimal study plan, taking into account, for example, the user's learning progress and level of understanding. For example, the creation unit provides learning materials and assignments according to beginner, intermediate, and advanced levels. The creation unit can also adjust the level of detail of the plan based on the user's learning goals. For example, the creation unit can provide a specific and detailed plan for a user with short-term goals and a plan covering a wide range of content for a user with long-term goals. The feedback unit uses AI to provide real-time feedback based on the study plan created by the creation unit. For example, when a user submits an assignment, the feedback unit evaluates the content and provides suggestions for improvement or additional learning resources. For example, if a user struggles with a particular assignment, the feedback unit may suggest specific suggestions for improvement for that assignment. The feedback unit may also provide advice for adjusting the user's learning pace according to the user's learning progress. The practice unit uses AI to provide opportunities to practice what the user has learned based on the feedback provided by the feedback unit. For example, the practice unit may introduce research projects and internship opportunities, providing the user with opportunities to apply the knowledge they have learned in real-world situations. For example, the practice unit may introduce collaborations with companies or projects in university laboratories. The practice unit may also provide flexible practice opportunities according to the user's living situation. As a result, the education support system according to the embodiment enables effective learning by allowing users to select a specialized field they want to study, create a study plan, receive feedback in real time, and provide opportunities to practice what they have learned.

[0054] The selection unit can select a suitable field based on the user's interests and career. For example, the selection unit selects a suitable field based on the user's interests and career. For example, the selection unit recommends a suitable field based on the user's past studies and career goals. The selection unit can also conduct a survey to identify the user's interests. For example, the selection unit provides a survey including questions about topics and fields in which the user is interested, and selects a suitable field based on the results. Furthermore, the selection unit can analyze the user's past history and recommend a suitable field. For example, the selection unit recommends related fields based on the subjects the user has taken in the past and the qualifications he or she has obtained. This enables more effective learning by selecting a suitable field based on the user's interests and career.

[0055] The creation unit can provide an appropriate study plan while taking into consideration the user's learning progress and level of understanding. The creation unit provides an appropriate study plan while taking into consideration, for example, the user's learning progress and level of understanding. For example, the creation unit creates an optimal study plan based on the content the user has previously learned and test results. The creation unit can also adjust the study plan by referring to the user's self-assessment and the teacher's assessment. For example, the creation unit provides a plan that focuses on areas that the user feels are weak in their self-assessment. The creation unit can also adjust the pace of the study plan according to the user's learning progress. For example, the creation unit provides additional assignments if the user is progressing quickly, and provides supplementary materials if the user is falling behind. In this way, an optimal study plan can be provided by taking into consideration the user's learning progress and level of understanding.

[0056] The feedback unit can evaluate the content of an assignment submitted by a user and provide suggestions for improvement or additional learning resources. For example, when a user submits an assignment, the feedback unit evaluates the content and provides suggestions for improvement or additional learning resources. For example, the feedback unit uses AI to analyze the assignment submitted by the user and suggest specific suggestions for improvement. Furthermore, if a user is struggling with a particular assignment, the feedback unit can provide additional learning resources for that assignment. For example, the feedback unit can provide reference materials or additional assignments to support the user in deepening their understanding. Furthermore, the feedback unit can provide advice for adjusting the learning pace according to the user's learning progress. For example, if the user's progress is slow, the feedback unit can provide specific advice for increasing the learning pace, and if progress is going well, the feedback unit can provide instructions for moving on to the next step. In this way, the quality of learning is improved by providing appropriate feedback when the user submits an assignment.

[0057] The practical application section can introduce research projects and internship opportunities, providing users with opportunities to apply the knowledge they have learned in real-world situations. For example, the practical application section can introduce research projects and internship opportunities, providing users with opportunities to apply the knowledge they have learned in real-world situations. For example, the practical application section can introduce collaborations with companies and projects in university laboratories. The practical application section can also provide opportunities for online projects and remote internships. For example, the practical application section can introduce online projects that users can participate in from home, providing opportunities to acquire practical skills. The practical application section can also provide flexible practical opportunities according to users' living situations. For example, the practical application section can provide users who are raising children with projects that can be completed in a short amount of time or internships with flexible schedules. This allows users to acquire practical skills by providing them with opportunities to apply the knowledge they have learned in real-world situations.

[0058] The creation unit can provide teaching materials and assignments according to beginner, intermediate, and advanced levels. The creation unit provides, for example, teaching materials and assignments according to beginner, intermediate, and advanced levels. For example, the creation unit provides beginner-level users with teaching materials for learning basic knowledge, and intermediate-level users with teaching materials for learning applied knowledge. The creation unit can also provide advanced-level users with teaching materials for deepening specialized knowledge. For example, the creation unit can provide beginner-level users with teaching materials for learning basic concepts and terminology, and intermediate-level users with practical assignments and case studies. The creation unit can also provide advanced-level users with advanced theories and research papers. This enables effective learning by providing teaching materials and assignments according to the user's level.

[0059] The selection unit can estimate the user's emotions and assist in selecting an appropriate field based on the estimated user emotions. For example, the selection unit can estimate the user's emotions and assist in selecting an appropriate field based on the estimated user emotions. For example, if the user is excited, the selection unit can suggest a challenging field. Furthermore, if the user is calm, the selection unit can suggest a field requiring deep thought. Furthermore, if the user is anxious, the selection unit can suggest a field that allows for relaxation. For example, the selection unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the selection unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the selection unit can estimate the emotion based on the user's self-reporting. This enables more effective learning by selecting an appropriate field based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0060] The selection unit can analyze the user's past learning history and recommend an appropriate field. The selection unit, for example, analyzes the user's past learning history and recommends an appropriate field. For example, the selection unit recommends related new fields based on fields the user has studied in the past. The selection unit can also preferentially recommend fields in which the user has received high marks in the past. The selection unit can also recommend fields that the user has struggled with in the past, avoiding such fields. For example, the selection unit analyzes data such as the user's courses, grades, and study time to recommend the most appropriate field. In this way, the selection unit can recommend the most appropriate field by analyzing the user's past learning history.

[0061] The selection unit can filter suitable fields based on the user's current occupation and living situation. The selection unit filters suitable fields based on, for example, the user's current occupation and living situation. For example, the selection unit preferentially recommends fields related to the user's current occupation. The selection unit can also recommend fields that allow for flexible learning depending on the user's living situation (e.g., childcare). The selection unit can also recommend fields that are useful for career advancement based on the user's career goals. For example, the selection unit analyzes data such as the user's type of occupation, working hours, and home environment to filter out optimal fields. This enables more effective learning by filtering suitable fields based on the user's current occupation and living situation.

[0062] The selection unit can provide an appropriate field selection means according to the user's input method. The selection unit provides an appropriate field selection means according to, for example, the user's input method. For example, when the user uses voice input, the selection unit selects a field using voice recognition technology. Furthermore, when the user uses text input, the selection unit can also select a field using keyword search. Furthermore, when the user uses image input, the selection unit can also select a related field using image analysis technology. For example, the selection unit provides an optimal field selection means according to a specific input method such as voice input, text input, or image input. This enables more effective learning by providing an optimal field selection means according to the user's input method.

[0063] The selection unit can estimate the user's emotions and determine the priority of field selection based on the estimated user emotions. The selection unit, for example, estimates the user's emotions and determines the priority of field selection based on the estimated user emotions. For example, if the user is excited, the selection unit can prioritize challenging fields. Furthermore, if the user is calm, the selection unit can prioritize fields that require deep contemplation. Furthermore, if the user is anxious, the selection unit can prioritize relaxing fields. For example, the selection unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the selection unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the selection unit can estimate the emotion based on the user's self-reporting. This enables more effective learning by determining the priority of field selection based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0064] The selection unit can prioritize selecting highly relevant fields by taking into account the user's geographical location information. The selection unit, for example, prioritizes selecting highly relevant fields by taking into account the user's geographical location information. For example, if the user lives in a specific area, the selection unit can recommend fields related to that area. Furthermore, if the user is traveling, the selection unit can recommend fields related to the travel destination. Furthermore, if the user is interested in a specific country or area, the selection unit can recommend fields related to that area. For example, the selection unit analyzes the user's geographical location information, such as GPS data or address information, and recommends the most appropriate field. In this way, highly relevant fields can be prioritized by taking into account the user's geographical location information.

[0065] The selection unit can analyze the user's social media activity and recommend related fields. The selection unit, for example, analyzes the user's social media activity and recommends related fields. For example, the selection unit recommends fields related to accounts the user follows on social media. The selection unit can also analyze the content of the user's posts and recommend fields that the user is likely to be interested in. The selection unit can also recommend related fields based on the activities of the user's friends. For example, the selection unit analyzes data such as the content of the user's posts, the number of likes, and the number of followers, and recommends the most appropriate field. In this way, related fields can be recommended by analyzing the user's social media activity.

[0066] The selection unit can customize the category selection method by reflecting the user's past feedback. The selection unit customizes the category selection method by reflecting the user's past feedback, for example. For example, the selection unit preferentially recommends categories that the user has previously given high ratings to. The selection unit can also recommend categories that the user has previously given low ratings to, avoiding them. The selection unit can also adjust the category selection algorithm based on the user's feedback. For example, the selection unit analyzes data such as the user's evaluation comments, scores, and improvement suggestions to recommend the most appropriate category. In this way, the category selection method can be customized by reflecting the user's past feedback.

[0067] The creation unit can estimate the user's emotions and adjust the presentation of the study plan based on the estimated user emotions. For example, the creation unit can estimate the user's emotions and adjust the presentation of the study plan based on the estimated user emotions. For example, if the user is relaxed, the creation unit can provide a study plan that progresses at a leisurely pace. If the user is in a hurry, the creation unit can provide a study plan that can be completed in a short period of time. If the user is excited, the creation unit can provide a study plan that includes visually stimulating materials. For example, the creation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The creation unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the creation unit can estimate the emotion based on the user's self-reporting. This enables more effective learning by adjusting the presentation of the study plan based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0068] The creation unit can provide learning materials and assignments that match the user's learning style when creating a study plan. For example, the creation unit provides learning materials and assignments that match the user's learning style when creating a study plan. For example, if the user is a visual learner, the creation unit can provide visual learning materials. Also, if the user is an auditory learner, the creation unit can provide audio learning materials. Also, if the user prefers hands-on learning, the creation unit can provide experiment- or project-based assignments. For example, the creation unit provides optimal learning materials and assignments depending on the user's learning style, such as visual, auditory, or experiential. This enables more effective learning by providing learning materials and assignments that match the user's learning style. === Hard Collateral 1-1 === Each of the multiple elements, including the selection unit, creation unit, feedback unit, and practice unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the selection unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the creation unit is implemented by the specific processing unit 290 of the data processing device 12 and creates a study plan taking into account the user's learning progress and level of understanding. The feedback unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and provides feedback in real time. The practice unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and provides opportunities for research projects and internships. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned selection unit, creation unit, feedback unit, and practice unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a study plan taking into account the user's learning progress and level of understanding. The feedback unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and provides feedback in real time. The practice unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and provides opportunities for research projects and internships. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned selection unit, creation unit, feedback unit, and practice unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a study plan taking into account the user's learning progress and level of understanding. The feedback unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12 and provides feedback in real time. The practice unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12 and provides opportunities for research projects and internships. === Hard Collateral 1-4 === Each of the multiple elements including the selection unit, creation unit, feedback unit, and practice unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a study plan taking into account the user's learning progress and level of understanding. The feedback unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and provides feedback in real time. The practice unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and provides opportunities for research projects and internships.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The education support system may further include a history analysis unit that analyzes the user's learning history. The history analysis unit collects and analyzes data such as the content the user has learned in the past, grades, and study time. For example, the history analysis unit may identify areas in which the user has previously received high marks and create a new learning plan related to those areas. The history analysis unit may also provide feedback to help the user avoid areas in which the user struggled in the past. Furthermore, the history analysis unit may analyze the user's learning patterns and suggest an optimal learning schedule. This makes it possible to utilize the user's past learning history to provide a more effective learning plan.

[0071] The education support system may further include a social analysis unit that analyzes the user's social media activity. The social analysis unit analyzes the accounts and posts the user follows on social media and recommends related learning topics. For example, the social analysis unit may suggest areas of interest based on topics the user frequently likes or comments on. The social analysis unit may also provide related learning resources based on the activities of the user's friends and followers. Furthermore, the social analysis unit may analyze the content of the user's posts and provide feedback to increase motivation to study. This makes it possible to utilize the user's social media activity to provide a more personalized learning experience.

[0072] The education support system may further include a location information analysis unit that takes into account the user's geographic location information. The location information analysis unit recommends relevant learning fields and resources based on the user's current geographic location. For example, if the user lives in a particular area, the location information analysis unit may suggest learning resources and events related to that area. If the user is traveling, the location information analysis unit may also provide learning opportunities related to the user's travel destination. Furthermore, if the user is interested in a particular country or region, the location information analysis unit may also provide learning resources related to the culture and history of that region. In this way, the user's geographic location information can be utilized to provide a more relevant learning experience.

[0073] The education support system may further include an emotion adjustment unit that estimates the user's emotions and adjusts the study plan based on the estimated emotions. The emotion adjustment unit estimates the user's emotions based on the user's facial expressions, voice, self-reporting, etc., and adjusts the study plan based on the results. For example, if the user is feeling stressed, the emotion adjustment unit may provide a relaxing study plan. If the user is excited, the emotion adjustment unit may also provide a study plan that includes challenging tasks. Furthermore, if the user is tired, the emotion adjustment unit may also provide a study plan that can be completed in a short time. This allows for more effective learning by adjusting the study plan according to the user's emotions.

[0074] The educational support system may further include a style adaptation unit that provides learning materials and assignments according to the user's learning style. The style adaptation unit takes into account that users have different learning styles, such as visual learners, auditory learners, and experiential learners, and provides learning materials and assignments according to the learning styles. For example, the style adaptation unit may provide visual learning materials to visual learners and audio learning materials to auditory learners. It may also provide experiments or project-based assignments to experiential learners. Furthermore, the style adaptation unit may adjust the pace and content of the learning plan based on the user's learning style. This allows for more effective learning by providing learning materials and assignments according to the user's learning style.

[0075] The education support system can further include a goal adaptation unit that customizes the study plan according to the user's learning goals. The goal adaptation unit provides a specific and detailed plan if the user has a short-term goal, and a plan covering a wide range of content if the user has a long-term goal. For example, if the user's goal is to pass a qualification exam, the goal adaptation unit can provide a study plan specialized for that exam. In addition, if the user is aiming to advance their career, the goal adaptation unit can also provide a plan to deepen skills and knowledge related to that field. Furthermore, the goal adaptation unit can flexibly adjust the study plan according to the user's progress. This enables more effective learning by providing a study plan customized according to the user's learning goals.

[0076] The education support system may further include an emotion feedback unit that estimates the user's emotion and provides feedback based on the estimated emotion. The emotion feedback unit estimates the user's emotion based on the user's facial expressions, voice, self-reporting, etc., and provides feedback based on the result. For example, if the user is feeling anxious, the emotion feedback unit may provide an encouraging message or relaxation resources. If the user is excited, the emotion feedback unit may also provide challenging tasks or additional learning resources. Furthermore, if the user is tired, the emotion feedback unit may provide a message encouraging the user to take a break or a task that can be completed in a short time. In this way, the quality of learning can be improved by providing appropriate feedback according to the user's emotion.

[0077] The education support system may further include a progress display unit that visually displays the user's learning progress. The progress display unit visually displays the user's current progress in learning, thereby increasing motivation to learn. For example, the progress display unit may display the tasks the user has completed and the goals they have achieved in the form of a graph or chart. The progress display unit may also visually indicate the next tasks or goals the user should tackle. Furthermore, the progress display unit may analyze the user's learning pace and suggest an appropriate pace of progress. In this way, visually displaying the user's learning progress can increase motivation to learn and support effective learning.

[0078] The education support system can further include an environment optimization unit that optimizes the user's learning environment. The environment optimization unit analyzes the user's learning environment and provides advice to provide an optimal learning environment. For example, the environment optimization unit analyzes the user's learning location, time of day, ambient noise level, etc., and proposes an optimal learning environment. The environment optimization unit can also provide specific advice to create an environment that makes it easier for the user to concentrate. Furthermore, the environment optimization unit can adjust the learning plan according to the user's learning environment. This allows for more effective learning by optimizing the user's learning environment.

[0079] The education support system can further include an emotional environment adjustment unit that estimates the user's emotions and adjusts the learning environment based on the estimated emotions. The emotional environment adjustment unit estimates the user's emotions based on the user's facial expressions, voice, self-reporting, etc., and adjusts the learning environment based on the results. For example, if the user is feeling stressed, the emotional environment adjustment unit can suggest relaxing music or lighting. Also, if the user is excited, the emotional environment adjustment unit can suggest environmental settings to improve concentration. Furthermore, if the user is tired, the emotional environment adjustment unit can suggest environmental settings to encourage the user to take a break. In this way, adjusting the learning environment according to the user's emotions enables more effective learning.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The selector selects the field of study the user wants to study. Fields of study that the user wants to study include science, technology, business, arts, etc. The selector allows the user to choose from fields such as physics, chemistry, and economics, and can also select suitable fields based on the user's interests and career. For example, the selector recommends suitable fields based on the user's past studies and career goals. Step 2: The creation unit uses AI to create a study plan based on the information selected by the selection unit. The creation unit provides the optimal study plan, taking into account the user's learning progress and level of understanding. For example, it provides learning materials and assignments according to beginner, intermediate, and advanced levels, and adjusts the level of detail of the plan based on the user's learning goals. It provides specific and detailed plans for users with short-term goals, and plans covering a wide range of content for users with long-term goals. Step 3: The feedback unit uses AI to provide real-time feedback based on the learning plan created by the creation unit. When the user submits an assignment, the feedback unit evaluates the content and provides suggestions for improvement and additional learning resources. For example, if the user is struggling with a particular assignment, the feedback unit will suggest specific improvements for that assignment and provide advice on adjusting the learning pace according to the user's progress. Step 4: The Practice Department uses AI to provide opportunities for users to practice what they have learned based on the feedback provided by the Feedback Department. The Practice Department introduces research projects and internship opportunities, providing users with opportunities to apply the knowledge they have learned in real-world situations. For example, it introduces collaborations with companies and projects in university laboratories, providing flexible practice opportunities that suit the user's living situation.

[0082] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0085] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0126] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0136] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0137] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0138] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0139] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0140] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0141] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0142] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0143] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0144] 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.

[0145] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0146] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0147] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0148] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0149] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0150] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0151] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0152] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0153] [Explanation of symbols]

[0154] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A selection section for selecting a specialization that the user wants to learn; a creation unit that creates a study plan based on the information selected by the selection unit; a feedback unit that provides real-time feedback based on the learning plan created by the creation unit; a practice section that provides an opportunity to practice what has been learned based on the feedback provided by the feedback section; Equipped with A system characterized by:

2. The selection unit Select the right field based on your interests and career 2. The system of claim 1.

3. The creation unit Providing an appropriate learning plan that takes into account the user's learning progress and level of understanding 2. The system of claim 1.

4. The feedback unit When users submit their assignments, we evaluate their work and provide suggestions for improvement or additional learning resources.

2. The system of claim 1.

5. The practice department: Introducing research projects and internship opportunities to give users the opportunity to apply their knowledge in the field 2. The system of claim 1.

6. The creation unit Providing materials and assignments for beginner, intermediate and advanced levels 2. The system of claim 1.

7. The selection unit Estimates user emotions and helps users select appropriate fields based on the estimated user emotions.

2. The system of claim 1.

8. The selection unit Analyze the user's past learning history and recommend appropriate subjects 2. The system of claim 1.

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