system

Abstract

The system according to this embodiment aims to provide a foreign language learning plan optimized for individual learners. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a learning unit, an analysis unit, and an adjustment unit. The reception unit receives input of the foreign language to be learned and the purpose or level of learning. The generation unit generates an appropriate learning plan based on the information received by the reception unit. The learning unit proceeds with learning based on the learning plan generated by the generation unit. The analysis unit accumulates the learning data carried out by the learning unit and identifies areas for improvement to enhance learning effectiveness. The adjustment unit adjusts the learning plan based on the areas for improvement identified by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, foreign language learning plans are not optimized for individual learners, and there is room for improvement in maximizing learning effects.

[0005] The system according to the embodiment aims to provide a foreign language learning plan optimized for individual learners.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a learning unit, an analysis unit, and an adjustment unit. The reception unit receives input of the foreign language to be learned and the purpose or level of learning. The generation unit generates an appropriate learning plan based on the information received by the reception unit. The learning unit proceeds with learning based on the learning plan generated by the generation unit. The analysis unit accumulates the learning data carried out by the learning unit and identifies areas for improvement to enhance learning effectiveness. The adjustment unit adjusts the learning plan based on the areas for improvement identified by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide a foreign language learning plan optimized for individual learners. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The foreign language learning system according to an embodiment of the present invention is a system that uses generative AI to provide a place where users can learn foreign languages ​​more easily and conveniently. This foreign language learning system allows users to select the foreign language they want to learn and input their learning objectives and level, thereby generating an optimal learning plan and enabling them to proceed with their learning. For example, it provides learning plans tailored to the user's objectives, such as learning basic conversation for travel or increasing vocabulary to a level usable in business. This information is input into the generative AI, which analyzes the user's input information and generates an optimal learning plan. The generative AI selects appropriate teaching materials and practice problems according to the user's objectives and level, and customizes the learning plan. For example, for a user who wants to learn basic conversation for travel, it provides practice problems based on everyday conversation phrases and situations. Based on the generated learning plan, the user proceeds with their learning. The generative AI monitors the user's learning progress in real time and provides feedback as needed. For example, when practicing pronunciation, the generative AI analyzes the user's pronunciation and provides advice to help them get closer to correct pronunciation. Furthermore, the generative AI accumulates the user's learning data and identifies areas for improvement to maximize learning effectiveness. For example, if a user has difficulty with a particular grammatical item or vocabulary, the generating AI will adjust the learning plan to focus on that area. This system allows users to learn a foreign language at their own pace and maximize learning effectiveness. Furthermore, the feedback and advice provided by the generating AI enable efficient learning. For instance, when practicing pronunciation, the generating AI analyzes the user's pronunciation and provides advice to help them improve. This system can be accessed through devices such as smartphones and tablets, allowing users to learn a foreign language anytime, anywhere. For example, users can utilize spare time during commutes or breaks to continue learning. In this way, the foreign language learning system allows users to learn a foreign language at their own pace and maximize learning effectiveness.

[0029] The foreign language learning system according to this embodiment comprises a reception unit, a generation unit, a learning unit, an analysis unit, and an adjustment unit. The reception unit receives input from the user regarding the foreign language they wish to learn and their learning objectives and level. For example, if the user wants to learn English, the reception unit can select English and input the objective of learning basic conversation for travel. The generation unit generates an optimal learning plan based on the information received by the reception unit. For example, the generation unit selects appropriate teaching materials and practice problems according to the user's objectives and level, and customizes the learning plan. The generation unit uses a generation AI to analyze the user's input information and generate an optimal learning plan. The learning unit proceeds with learning based on the learning plan generated by the generation unit. For example, the learning unit enables the user to solve practice problems based on everyday conversation phrases and situations. The learning unit uses a generation AI to monitor the user's learning progress in real time and provide feedback as needed. The analysis unit accumulates learning data from the learning unit and identifies areas for improvement to maximize learning effectiveness. For example, if the analysis unit has difficulty with a particular grammatical item or vocabulary, it adjusts the plan to focus on learning those areas. The analysis unit uses generative AI to analyze the user's learning data and identify areas for improvement to maximize learning effectiveness. The adjustment unit adjusts the learning plan based on the areas for improvement identified by the analysis unit. For example, the adjustment unit adjusts the plan to focus on learning grammatical items or vocabulary that the user finds difficult. The adjustment unit uses generative AI to adjust the learning plan. As a result, the foreign language learning system according to this embodiment allows the user to learn a foreign language at their own pace and maximize learning effectiveness.

[0030] The foreign language learning system includes a service area for use via smartphones or tablets, allowing users to progress with their learning anytime, anywhere. The service area enables users to learn through devices such as smartphones and tablets. For example, the service area allows users to learn during spare time, such as during commutes or breaks. The service area provides learning plans and practice exercises via smartphones and tablets. This allows users to learn anytime, anywhere. Some or all of the above-described processes in the service area may be performed using generative AI, or not. For example, the service area can develop an application using generative AI to provide learning plans and practice exercises via smartphones and tablets. This allows users to learn through various devices.

[0031] The reception unit can analyze the user's past learning history and automatically suggest appropriate learning objectives or levels. For example, the reception unit can suggest the next learning step based on the user's level in languages ​​learned in the past. It can also suggest new relevant learning objectives based on what the user has learned in the past. Furthermore, the reception unit can automatically set the optimal learning level based on the user's past learning history. This allows the system to provide an optimal learning plan based on the user's past learning history. Some or all of the above processing in the reception unit may be performed using or without a generative AI. For example, the reception unit can input the user's past learning data into a generative AI and have the generative AI suggest optimal learning objectives and levels.

[0032] The reception desk can filter learning objectives or levels based on the user's current life circumstances or areas of interest. For example, if the user is planning a trip, the reception desk will prioritize presenting learning objectives related to travel. It can also suggest learning objectives related to business terminology and conversation if the user is interested in business. Furthermore, if the user wants to learn something as a hobby, the reception desk can present learning objectives related to that hobby. This allows for the provision of a learning plan tailored to the user's life circumstances and areas of interest. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input data on the user's life circumstances and areas of interest into a generative AI and have the generative AI perform the filtering.

[0033] The reception desk can prioritize presenting relevant languages ​​based on the user's geographical location when the user selects a foreign language to learn. For example, if the user is in Europe, the reception desk will prioritize European languages. It can also prioritize Asian languages ​​if the user is in Asia. Furthermore, if the user is in a specific country, the reception desk can prioritize the official language of that country. This allows the reception desk to provide relevant languages ​​based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input the user's geographical location into a generative AI and have the generative AI perform the task of presenting relevant languages.

[0034] The reception desk can suggest relevant learning objectives based on the user's social media activity when the user inputs their learning objectives and level. For example, if the reception desk posts about travel on social media, it can suggest learning objectives related to travel. It can also suggest objectives for learning business terminology and conversation if the user posts about business. Furthermore, if the reception desk posts about hobbies, it can suggest learning objectives related to those hobbies. This allows the system to provide relevant learning objectives based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input the user's social media data into a generative AI and have the generative AI suggest relevant learning objectives.

[0035] The generation unit can select the most suitable learning materials and practice problems by referring to the user's past learning data when generating a learning plan. For example, the generation unit can select relevant learning materials based on what the user has learned in the past. The generation unit can also select learning materials that focus on areas where the user struggles, based on the user's past learning data. Furthermore, the generation unit can analyze the user's past learning history and select the most effective practice problems. This allows the system to provide the most suitable learning materials and practice problems based on the user's past learning data. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's past learning data into a generation AI and have the generation AI select the most suitable learning materials and practice problems.

[0036] The generation unit can apply different learning algorithms depending on the user's learning style when generating a learning plan. For example, if the user is a visual learner, the generation unit can generate a learning plan that makes extensive use of visual materials. Similarly, if the user is an auditory learner, the generation unit can generate a learning plan that makes extensive use of audio materials. Furthermore, if the user is an experiential learner, the generation unit can generate a learning plan that makes extensive use of practical practice problems. This allows the generation unit to provide learning plans tailored to the user's learning style. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's learning style data into a generation AI and have the generation AI apply different learning algorithms.

[0037] The generation unit can determine the priority of learning plans based on the user's submission timing when generating learning plans. For example, if a user needs to complete their learning by a specific deadline, the generation unit will prioritize generating a learning plan that aligns with that deadline. The generation unit can also generate a step-by-step learning plan if the user has long-term learning goals. Furthermore, if the user has short-term learning goals, the generation unit can generate a plan that allows for focused learning. This allows the generation unit to provide a priority of learning plans based on the user's submission timing. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input user submission timing data into a generation AI and have the generation AI determine the priority of learning plans.

[0038] The generation unit can adjust the order of learning plans based on user relevance when generating them. For example, if a user is interested in a particular topic, the generation unit can generate a plan that starts with that topic. It can also generate a plan that starts with content related to a specific skill if the user wants to prioritize learning that skill. Furthermore, if a user has a specific goal, the generation unit can generate a plan that starts with content related to that goal. This allows the generation unit to provide a learning plan order based on user relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input user interest data into a generation AI and have the generation AI adjust the order of the learning plans.

[0039] The learning unit can provide optimal feedback by referring to the user's past learning history when monitoring learning progress. For example, the learning unit can provide focused feedback on items the user has struggled with in the past. It can also provide positive feedback on items the user has excelled at in the past. Furthermore, the learning unit can provide feedback on the next learning step based on the user's past learning history. This allows the learning unit to provide optimal feedback based on the user's past learning history. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the learning unit can input the user's past learning data into a generative AI and have the generative AI perform the task of providing optimal feedback.

[0040] The learning unit can apply different feedback methods depending on the user's learning style when monitoring learning progress. For example, if the user is a visual learner, the learning unit can provide visual feedback. It can also provide audio feedback if the user is an auditory learner. Furthermore, if the user is an experiential learner, it can provide hands-on feedback. This allows for the provision of feedback tailored to the user's learning style. Some or all of the above processing in the learning unit may be performed using or without a generative AI. For example, the learning unit can input user learning style data into a generative AI and have the generative AI apply different feedback methods.

[0041] The learning unit can provide optimal feedback while monitoring learning progress, taking into account the user's geographical location. For example, if the user is in a specific country, the learning unit can provide feedback related to the culture and customs of that country. It can also provide travel-related feedback if the user is traveling. Furthermore, if the user is in a specific region, the learning unit can provide feedback related to the language and dialect of that region. This allows for the provision of optimal feedback based on the user's geographical location. Some or all of the above processing in the learning unit may be performed using or without a generative AI. For example, the learning unit can input the user's geographical location information into a generative AI and have the generative AI provide optimal feedback.

[0042] The learning unit can analyze the user's social media activity and provide relevant feedback when monitoring learning progress. For example, if the user posts about travel on social media, the learning unit can provide travel-related feedback. It can also provide feedback related to business terminology and conversations if the user posts about business. Furthermore, if the user posts about their hobbies, the learning unit can provide feedback related to those hobbies. This allows the learning unit to provide relevant feedback based on the user's social media activity. Some or all of the above processing in the learning unit may be performed using or without a generative AI. For example, the learning unit can input the user's social media data into a generative AI and have the generative AI provide relevant feedback.

[0043] The analysis unit can identify optimal areas for improvement by referring to the user's past learning data when analyzing training data. For example, the analysis unit can focus on analyzing items that the user has struggled with in the past and identify areas for improvement. The analysis unit can also identify effective learning methods from the user's past learning data. Furthermore, the analysis unit can identify areas for improvement for the next learning step based on the user's past learning history. This allows the analysis unit to provide optimal areas for improvement based on the user's past learning data. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's past learning data into a generative AI and have the generative AI identify optimal areas for improvement.

[0044] The analysis unit can apply different analysis algorithms to the user's learning style when analyzing training data. For example, if the user is a visual learner, the analysis unit can apply an analysis algorithm that emphasizes visual data. If the user is an auditory learner, the analysis unit can also apply an analysis algorithm that emphasizes audio data. Furthermore, if the user is an experiential learner, the analysis unit can also apply an analysis algorithm that emphasizes practical data. This allows the analysis unit to provide an analysis algorithm tailored to the user's learning style. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's learning style data into a generative AI and have the generative AI execute the application of different analysis algorithms.

[0045] The analysis unit can identify optimal areas for improvement by considering the user's geographical location information when analyzing training data. For example, if the user is in a specific country, the analysis unit can identify areas for improvement related to the culture and customs of that country. Furthermore, if the user is traveling, the analysis unit can identify areas for improvement related to travel. Additionally, if the user is in a specific region, the analysis unit can identify areas for improvement related to the language and dialect of that region. This allows the analysis unit to provide optimal areas for improvement based on the user's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the user's geographical location information into a generative AI and have the generative AI identify optimal areas for improvement.

[0046] The analysis unit can analyze a user's social media activity and identify relevant areas for improvement when analyzing training data. For example, if a user posts about travel on social media, the analysis unit can identify areas for improvement related to travel. It can also identify areas for improvement related to business terminology and conversation if a user posts about business. Furthermore, if a user posts about their hobbies, the analysis unit can identify areas for improvement related to those hobbies. This allows the system to provide relevant improvements based on the user's social media activity. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's social media data into a generative AI and have the generative AI identify relevant areas for improvement.

[0047] The adjustment unit can select the optimal adjustment method by referring to the user's past learning data when adjusting the learning plan. For example, the adjustment unit can focus on adjusting items that the user has struggled with in the past. The adjustment unit can also select an effective adjustment method from the user's past learning data. Furthermore, the adjustment unit can select an adjustment method for the next learning step based on the user's past learning history. This allows the system to provide the optimal adjustment method based on the user's past learning data. Some or all of the above processing in the adjustment unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the adjustment unit can input the user's past learning data into a generative AI and have the generative AI select the optimal adjustment method.

[0048] The adjustment unit can apply different adjustment algorithms to the user's learning style when adjusting the learning plan. For example, if the user is a visual learner, the adjustment unit can apply an adjustment algorithm that emphasizes visual data. If the user is an auditory learner, the adjustment unit can also apply an adjustment algorithm that emphasizes audio data. Furthermore, if the user is an experiential learner, the adjustment unit can also apply an adjustment algorithm that emphasizes practical data. This allows the adjustment unit to provide an adjustment algorithm that is tailored to the user's learning style. Some or all of the above processing in the adjustment unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the adjustment unit can input the user's learning style data into a generative AI and have the generative AI execute the application of different adjustment algorithms.

[0049] The adjustment unit can select the optimal adjustment method when adjusting the learning plan, taking into account the user's geographical location information. For example, if the user is in a specific country, the adjustment unit can make adjustments related to the culture and customs of that country. It can also make travel-related adjustments if the user is traveling. Furthermore, if the user is in a specific region, the adjustment unit can make adjustments related to the language and dialect of that region. This allows the system to provide the optimal adjustment method based on the user's geographical location information. Some or all of the above processing in the adjustment unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the adjustment unit can input the user's geographical location information into a generative AI and have the generative AI select the optimal adjustment method.

[0050] The adjustment unit can analyze the user's social media activity and suggest relevant adjustments when adjusting the learning plan. For example, if the user posts about travel on social media, the adjustment unit can make travel-related adjustments. It can also make adjustments related to business terminology and conversation if the user posts about business. Furthermore, if the user posts about their hobbies, the adjustment unit can make adjustments related to those hobbies. This allows the system to provide relevant adjustments based on the user's social media activity. Some or all of the above processing in the adjustment unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the adjustment unit can input the user's social media data into a generative AI and have the generative AI suggest relevant adjustments.

[0051] The content delivery unit can select the optimal delivery method by referring to the user's past learning history when providing learning content. For example, the delivery unit can provide relevant learning content based on what the user has learned in the past. The delivery unit can also provide content that focuses on areas where the user struggles, based on the user's past learning data. Furthermore, the delivery unit can analyze the user's past learning history and provide the most effective learning content. This allows the delivery unit to provide the optimal method of providing learning content based on the user's past learning history. Some or all of the above processing in the delivery unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the delivery unit can input the user's past learning data into a generative AI and have the generative AI select the optimal delivery method.

[0052] The content delivery unit can apply different delivery algorithms depending on the user's learning style when delivering learning content. For example, if the user is a visual learner, the delivery unit can apply a delivery method that heavily utilizes visual content. Similarly, if the user is an auditory learner, the delivery unit can apply a delivery method that heavily utilizes audio content. Furthermore, if the user is an experiential learner, the delivery unit can apply a delivery method that heavily utilizes practical content. This allows for the delivery of learning content tailored to the user's learning style. Some or all of the above processing in the delivery unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the delivery unit can input user learning style data into a generative AI and have the generative AI apply different delivery algorithms.

[0053] The content delivery unit can select the optimal delivery method when providing learning content, taking into account the user's geographical location information. For example, if the user is in a specific country, the delivery unit can provide learning content related to the culture and customs of that country. Furthermore, if the user is traveling, the delivery unit can provide learning content related to travel. Additionally, if the user is in a specific region, the delivery unit can provide learning content related to the language and dialect of that region. This allows the delivery unit to provide the optimal method of delivering learning content based on the user's geographical location information. Some or all of the above processing in the delivery unit may be performed using a generative AI, or without one. For example, the delivery unit can input the user's geographical location information into a generative AI and have the generative AI select the optimal delivery method.

[0054] The content delivery unit can analyze the user's social media activity when providing learning content and suggest relevant delivery methods. For example, if the user posts about travel on social media, the delivery unit can provide travel-related learning content. It can also provide business-related learning content if the user posts about business. Furthermore, if the user posts about their hobbies, the delivery unit can provide learning content related to those hobbies. This allows the delivery unit to provide relevant learning content based on the user's social media activity. Some or all of the above processing in the delivery unit may be performed using a generative AI, or not. For example, the delivery unit can input the user's social media data into a generative AI and have the generative AI suggest relevant delivery methods.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception desk can predict learning progress and suggest what to learn next based on the user's learning history. For example, it can suggest grammar points and vocabulary to learn next based on what the user has learned in the past. The reception desk can also analyze the user's learning speed and suggest an appropriate learning pace. Furthermore, the reception desk can suggest learning materials suited to a specific learning style based on the user's learning history. In this way, it can provide an optimal learning plan based on the user's learning history.

[0057] The system can automatically adjust the difficulty level of learning content according to the user's learning progress. For example, if a user completes a particular task, the difficulty level of the next task will be increased. Conversely, for items the user finds difficult, the difficulty level can be lowered for further attempts. Furthermore, the system can provide review questions at appropriate times based on the user's learning progress. This allows for the provision of optimal learning content tailored to the user's learning progress.

[0058] The reception desk can analyze a user's past learning history and provide appropriate rewards according to their learning progress. For example, it can award badges or points when a user achieves a specific goal. It can also offer special rewards for users who continue learning consistently. Furthermore, it can offer additional rewards when users overcome areas they struggle with. In this way, by providing rewards according to the user's learning progress, it is possible to increase their motivation to learn.

[0059] The reception desk can suggest learning priorities based on the user's current lifestyle and areas of interest when they input their learning objectives or level. For example, if a user is busy with work, it can prioritize suggesting content that can be learned effectively in a short amount of time. If a user wants to learn as a hobby, it can prioritize suggesting content related to that hobby. Furthermore, if a user is planning a trip, it can prioritize suggesting travel-related content. This allows the system to provide a learning plan tailored to the user's lifestyle and areas of interest.

[0060] The reception desk can provide information about local culture and customs based on the user's geographical location when they select a foreign language they wish to learn. For example, if the user is in Europe, it can provide information about European culture and customs. Similarly, if the user is in Asia, it can provide information about Asian culture and customs. Furthermore, if the user is in a specific country, it can provide information about that country's tourist attractions and specialties. This allows the system to stimulate learning interests based on the user's geographical location.

[0061] The reception desk can provide a function to share learning progress based on the user's social media activity when they input their learning objectives and level. For example, if a user achieves a specific goal, they can share that achievement on social media. Furthermore, if a user continues to learn consistently, they can share their progress on social media. Additionally, if a user overcomes a difficult area, they can share that achievement on social media. This allows for increased motivation to learn by sharing the user's learning progress.

[0062] The generation unit, when generating a learning plan, can refer to the user's past learning data to predict learning progress and suggest review at appropriate times. For example, after a user has learned a specific grammar item, it can suggest reviewing that item after a certain period. It can also suggest reviewing new vocabulary after the user has learned it. Furthermore, it can suggest periodic reviews of items the user has struggled with in the past. This allows the system to provide optimal review timing based on the user's past learning data.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk accepts the user's selection of the foreign language they wish to learn and inputs their learning objectives and level. For example, if the user wants to learn English, the reception desk can select English and input their objective, such as wanting to learn basic conversation for travel. Step 2: The generation unit generates an optimal learning plan based on the information received by the reception unit. For example, the generation unit selects appropriate learning materials and practice problems according to the user's purpose and level, and customizes the learning plan. The generation unit uses a generation AI to analyze the user's input information and generate the optimal learning plan. Step 3: The learning unit proceeds with learning based on the learning plan generated by the generation unit. For example, the learning unit enables the user to solve practice problems based on everyday conversation phrases and situations. The learning unit uses generational AI to monitor the user's learning progress in real time and provide feedback as needed. Step 4: The analysis unit accumulates the learning data generated by the learning unit and identifies areas for improvement to maximize learning effectiveness. For example, if the analysis unit finds that the user has difficulty with a particular grammatical item or vocabulary, it adjusts the plan to focus on learning those areas. The analysis unit uses generative AI to analyze the user's learning data and identify areas for improvement to maximize learning effectiveness. Step 5: The adjustment unit adjusts the learning plan based on the improvements identified by the analysis unit. For example, the adjustment unit adjusts the plan to focus on grammar items or vocabulary that the user struggles with. The adjustment unit uses generative AI to adjust the learning plan.

[0065] (Example of form 2) The foreign language learning system according to an embodiment of the present invention is a system that uses generative AI to provide a place where users can learn foreign languages ​​more easily and conveniently. This foreign language learning system allows users to select the foreign language they want to learn and input their learning objectives and level, thereby generating an optimal learning plan and enabling them to proceed with their learning. For example, it provides learning plans tailored to the user's objectives, such as learning basic conversation for travel or increasing vocabulary to a level usable in business. This information is input into the generative AI, which analyzes the user's input information and generates an optimal learning plan. The generative AI selects appropriate teaching materials and practice problems according to the user's objectives and level, and customizes the learning plan. For example, for a user who wants to learn basic conversation for travel, it provides practice problems based on everyday conversation phrases and situations. Based on the generated learning plan, the user proceeds with their learning. The generative AI monitors the user's learning progress in real time and provides feedback as needed. For example, when practicing pronunciation, the generative AI analyzes the user's pronunciation and provides advice to help them get closer to correct pronunciation. Furthermore, the generative AI accumulates the user's learning data and identifies areas for improvement to maximize learning effectiveness. For example, if a user has difficulty with a particular grammatical item or vocabulary, the generating AI will adjust the learning plan to focus on that area. This system allows users to learn a foreign language at their own pace and maximize learning effectiveness. Furthermore, the feedback and advice provided by the generating AI enable efficient learning. For instance, when practicing pronunciation, the generating AI analyzes the user's pronunciation and provides advice to help them improve. This system can be accessed through devices such as smartphones and tablets, allowing users to learn a foreign language anytime, anywhere. For example, users can utilize spare time during commutes or breaks to continue learning. In this way, the foreign language learning system allows users to learn a foreign language at their own pace and maximize learning effectiveness.

[0066] The foreign language learning system according to this embodiment comprises a reception unit, a generation unit, a learning unit, an analysis unit, and an adjustment unit. The reception unit receives input from the user regarding the foreign language they wish to learn and their learning objectives and level. For example, if the user wants to learn English, the reception unit can select English and input the objective of learning basic conversation for travel. The generation unit generates an optimal learning plan based on the information received by the reception unit. For example, the generation unit selects appropriate teaching materials and practice problems according to the user's objectives and level, and customizes the learning plan. The generation unit uses a generation AI to analyze the user's input information and generate an optimal learning plan. The learning unit proceeds with learning based on the learning plan generated by the generation unit. For example, the learning unit enables the user to solve practice problems based on everyday conversation phrases and situations. The learning unit uses a generation AI to monitor the user's learning progress in real time and provide feedback as needed. The analysis unit accumulates learning data from the learning unit and identifies areas for improvement to maximize learning effectiveness. For example, if the analysis unit has difficulty with a particular grammatical item or vocabulary, it adjusts the plan to focus on learning those areas. The analysis unit uses generative AI to analyze the user's learning data and identify areas for improvement to maximize learning effectiveness. The adjustment unit adjusts the learning plan based on the areas for improvement identified by the analysis unit. For example, the adjustment unit adjusts the plan to focus on learning grammatical items or vocabulary that the user finds difficult. The adjustment unit uses generative AI to adjust the learning plan. As a result, the foreign language learning system according to this embodiment allows the user to learn a foreign language at their own pace and maximize learning effectiveness.

[0067] The foreign language learning system includes a service area for use via smartphones or tablets, allowing users to progress with their learning anytime, anywhere. The service area enables users to learn through devices such as smartphones and tablets. For example, the service area allows users to learn during spare time, such as during commutes or breaks. The service area provides learning plans and practice exercises via smartphones and tablets. This allows users to learn anytime, anywhere. Some or all of the above-described processes in the service area may be performed using generative AI, or not. For example, the service area can develop an application using generative AI to provide learning plans and practice exercises via smartphones and tablets. This allows users to learn through various devices.

[0068] The reception desk can estimate the user's emotions and, based on those emotions, present options for the foreign language the user may want to learn. For example, if the user is excited, the reception desk may prioritize presenting languages ​​from countries with exciting cultures. Similarly, if the user is relaxed, it may prioritize presenting languages ​​from countries with a relaxed atmosphere. Furthermore, if the user is stressed, it may prioritize presenting languages ​​that are easier to learn. This allows for increased motivation to learn by providing foreign language options tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using or without generative AI. For example, the reception desk can input user facial expression data into a generative AI and have the generative AI perform the user's emotion estimation.

[0069] The reception unit can analyze the user's past learning history and automatically suggest appropriate learning objectives or levels. For example, the reception unit can suggest the next learning step based on the user's level in languages ​​learned in the past. It can also suggest new relevant learning objectives based on what the user has learned in the past. Furthermore, the reception unit can automatically set the optimal learning level based on the user's past learning history. This allows the system to provide an optimal learning plan based on the user's past learning history. Some or all of the above processing in the reception unit may be performed using or without a generative AI. For example, the reception unit can input the user's past learning data into a generative AI and have the generative AI suggest optimal learning objectives and levels.

[0070] The reception desk can filter learning objectives or levels based on the user's current life circumstances or areas of interest. For example, if the user is planning a trip, the reception desk will prioritize presenting learning objectives related to travel. It can also suggest learning objectives related to business terminology and conversation if the user is interested in business. Furthermore, if the user wants to learn something as a hobby, the reception desk can present learning objectives related to that hobby. This allows for the provision of a learning plan tailored to the user's life circumstances and areas of interest. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input data on the user's life circumstances and areas of interest into a generative AI and have the generative AI perform the filtering.

[0071] The reception unit can estimate the user's emotions and adjust the input order of the learning objectives and levels based on the estimated emotions. For example, if the user is in a hurry, the reception unit can prompt the user to input the most important information first. If the user is relaxed, the reception unit can prompt the user to input detailed information sequentially. Furthermore, if the user is stressed, the reception unit can start with simple questions and gradually prompt for more detailed information. This improves learning efficiency by providing an input order that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using or without a generative AI. For example, the reception unit can input the user's facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0072] The reception desk can prioritize presenting relevant languages ​​based on the user's geographical location when the user selects a foreign language to learn. For example, if the user is in Europe, the reception desk will prioritize European languages. It can also prioritize Asian languages ​​if the user is in Asia. Furthermore, if the user is in a specific country, the reception desk can prioritize the official language of that country. This allows the reception desk to provide relevant languages ​​based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input the user's geographical location into a generative AI and have the generative AI perform the task of presenting relevant languages.

[0073] The reception desk can suggest relevant learning objectives based on the user's social media activity when the user inputs their learning objectives and level. For example, if the reception desk posts about travel on social media, it can suggest learning objectives related to travel. It can also suggest objectives for learning business terminology and conversation if the user posts about business. Furthermore, if the reception desk posts about hobbies, it can suggest learning objectives related to those hobbies. This allows the system to provide relevant learning objectives based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input the user's social media data into a generative AI and have the generative AI suggest relevant learning objectives.

[0074] The generation unit can estimate the user's emotions and adjust the presentation of the learning plan based on the estimated emotions. For example, if the user is relaxed, the generation unit may present the learning plan in a soft tone. If the user is in a hurry, the generation unit may present a concise and to-the-point learning plan. Furthermore, if the user is excited, the generation unit may present the learning plan with a visually stimulating design. This allows for the presentation of the learning plan to be tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using or without a generative AI. For example, the generation unit can input user facial expression data into a generative AI and have the generative AI adjust the presentation of the learning plan.

[0075] The generation unit can select the most suitable learning materials and practice problems by referring to the user's past learning data when generating a learning plan. For example, the generation unit can select relevant learning materials based on what the user has learned in the past. The generation unit can also select learning materials that focus on areas where the user struggles, based on the user's past learning data. Furthermore, the generation unit can analyze the user's past learning history and select the most effective practice problems. This allows the system to provide the most suitable learning materials and practice problems based on the user's past learning data. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's past learning data into a generation AI and have the generation AI select the most suitable learning materials and practice problems.

[0076] The generation unit can apply different learning algorithms depending on the user's learning style when generating a learning plan. For example, if the user is a visual learner, the generation unit can generate a learning plan that makes extensive use of visual materials. Similarly, if the user is an auditory learner, the generation unit can generate a learning plan that makes extensive use of audio materials. Furthermore, if the user is an experiential learner, the generation unit can generate a learning plan that makes extensive use of practical practice problems. This allows the generation unit to provide learning plans tailored to the user's learning style. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's learning style data into a generation AI and have the generation AI apply different learning algorithms.

[0077] The generation unit can estimate the user's emotions and adjust the length of the learning plan based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a learning plan that can be completed in a short time. If the user is relaxed, the generation unit can also generate a longer learning plan with more detailed content. Furthermore, if the user is stressed, the generation unit can generate a shorter, less burdensome learning plan. This allows for the provision of learning plan lengths that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using or without a generative AI. For example, the generation unit can input user facial expression data into a generative AI and have the generative AI adjust the length of the learning plan.

[0078] The generation unit can determine the priority of learning plans based on the user's submission timing when generating learning plans. For example, if a user needs to complete their learning by a specific deadline, the generation unit will prioritize generating a learning plan that aligns with that deadline. The generation unit can also generate a step-by-step learning plan if the user has long-term learning goals. Furthermore, if the user has short-term learning goals, the generation unit can generate a plan that allows for focused learning. This allows the generation unit to provide a priority of learning plans based on the user's submission timing. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input user submission timing data into a generation AI and have the generation AI determine the priority of learning plans.

[0079] The generation unit can adjust the order of learning plans based on user relevance when generating them. For example, if a user is interested in a particular topic, the generation unit can generate a plan that starts with that topic. It can also generate a plan that starts with content related to a specific skill if the user wants to prioritize learning that skill. Furthermore, if a user has a specific goal, the generation unit can generate a plan that starts with content related to that goal. This allows the generation unit to provide a learning plan order based on user relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input user interest data into a generation AI and have the generation AI adjust the order of the learning plans.

[0080] The learning unit can estimate the user's emotions and adjust the display method of learning progress based on the estimated user emotions. For example, if the user is nervous, the learning unit can provide a simple and highly visible display method. If the user is relaxed, the learning unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the learning unit can provide a concise display method. This allows for a display method of learning progress that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using the generative AI or not. For example, the learning unit can input user facial expression data into the generative AI and have the generative AI adjust the display method of learning progress.

[0081] The learning unit can provide optimal feedback by referring to the user's past learning history when monitoring learning progress. For example, the learning unit can provide focused feedback on items the user has struggled with in the past. It can also provide positive feedback on items the user has excelled at in the past. Furthermore, the learning unit can provide feedback on the next learning step based on the user's past learning history. This allows the learning unit to provide optimal feedback based on the user's past learning history. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the learning unit can input the user's past learning data into a generative AI and have the generative AI perform the task of providing optimal feedback.

[0082] The learning unit can apply different feedback methods depending on the user's learning style when monitoring learning progress. For example, if the user is a visual learner, the learning unit can provide visual feedback. It can also provide audio feedback if the user is an auditory learner. Furthermore, if the user is an experiential learner, it can provide hands-on feedback. This allows for the provision of feedback tailored to the user's learning style. Some or all of the above processing in the learning unit may be performed using or without a generative AI. For example, the learning unit can input user learning style data into a generative AI and have the generative AI apply different feedback methods.

[0083] The learning unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is in a hurry, the learning unit will prioritize providing the most important feedback. If the user is relaxed, the learning unit can also provide detailed feedback. Furthermore, if the user is stressed, the learning unit can provide concise and to-the-point feedback. This allows for the provision of feedback prioritization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using or without a generative AI. For example, the learning unit can input user facial expression data into a generative AI and have the generative AI determine the priority of feedback.

[0084] The learning unit can provide optimal feedback while monitoring learning progress, taking into account the user's geographical location. For example, if the user is in a specific country, the learning unit can provide feedback related to the culture and customs of that country. It can also provide travel-related feedback if the user is traveling. Furthermore, if the user is in a specific region, the learning unit can provide feedback related to the language and dialect of that region. This allows for the provision of optimal feedback based on the user's geographical location. Some or all of the above processing in the learning unit may be performed using or without a generative AI. For example, the learning unit can input the user's geographical location information into a generative AI and have the generative AI provide optimal feedback.

[0085] The learning unit can analyze the user's social media activity and provide relevant feedback when monitoring learning progress. For example, if the user posts about travel on social media, the learning unit can provide travel-related feedback. It can also provide feedback related to business terminology and conversations if the user posts about business. Furthermore, if the user posts about their hobbies, the learning unit can provide feedback related to those hobbies. This allows the learning unit to provide relevant feedback based on the user's social media activity. Some or all of the above processing in the learning unit may be performed using or without a generative AI. For example, the learning unit can input the user's social media data into a generative AI and have the generative AI provide relevant feedback.

[0086] The analysis unit can estimate the user's emotions and adjust the analysis method of the training data based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise and to-the-point analysis results. Furthermore, if the user is stressed, the analysis unit can provide visually easy-to-understand analysis results. This allows for the provision of a training data analysis method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI adjust the training data analysis method.

[0087] The analysis unit can identify optimal areas for improvement by referring to the user's past learning data when analyzing training data. For example, the analysis unit can focus on analyzing items that the user has struggled with in the past and identify areas for improvement. The analysis unit can also identify effective learning methods from the user's past learning data. Furthermore, the analysis unit can identify areas for improvement for the next learning step based on the user's past learning history. This allows the analysis unit to provide optimal areas for improvement based on the user's past learning data. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's past learning data into a generative AI and have the generative AI identify optimal areas for improvement.

[0088] The analysis unit can apply different analysis algorithms to the user's learning style when analyzing training data. For example, if the user is a visual learner, the analysis unit can apply an analysis algorithm that emphasizes visual data. If the user is an auditory learner, the analysis unit can also apply an analysis algorithm that emphasizes audio data. Furthermore, if the user is an experiential learner, the analysis unit can also apply an analysis algorithm that emphasizes practical data. This allows the analysis unit to provide an analysis algorithm tailored to the user's learning style. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's learning style data into a generative AI and have the generative AI execute the application of different analysis algorithms.

[0089] The analysis unit can estimate the user's emotions and determine the priority of improvements based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will prioritize presenting the most important improvements. If the user is relaxed, the analysis unit can also present detailed improvements. Furthermore, if the user is stressed, the analysis unit can present concise and to-the-point improvements. This allows for the provision of improvement priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI determine the priority of improvements.

[0090] The analysis unit can identify optimal areas for improvement by considering the user's geographical location information when analyzing training data. For example, if the user is in a specific country, the analysis unit can identify areas for improvement related to the culture and customs of that country. Furthermore, if the user is traveling, the analysis unit can identify areas for improvement related to travel. Additionally, if the user is in a specific region, the analysis unit can identify areas for improvement related to the language and dialect of that region. This allows the analysis unit to provide optimal areas for improvement based on the user's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the user's geographical location information into a generative AI and have the generative AI identify optimal areas for improvement.

[0091] The analysis unit can analyze a user's social media activity and identify relevant areas for improvement when analyzing training data. For example, if a user posts about travel on social media, the analysis unit can identify areas for improvement related to travel. It can also identify areas for improvement related to business terminology and conversation if a user posts about business. Furthermore, if a user posts about their hobbies, the analysis unit can identify areas for improvement related to those hobbies. This allows the system to provide relevant improvements based on the user's social media activity. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's social media data into a generative AI and have the generative AI identify relevant areas for improvement.

[0092] The adjustment unit can estimate the user's emotions and adjust the learning plan adjustment method based on the estimated user emotions. For example, if the user is relaxed, the adjustment unit can provide a detailed adjustment method. If the user is in a hurry, the adjustment unit can also provide a concise and to-the-point adjustment method. Furthermore, if the user is stressed, the adjustment unit can provide a visually easy-to-understand adjustment method. This allows the system to provide a learning plan adjustment method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the adjustment unit may be performed using the generative AI or not. For example, the adjustment unit can input user facial expression data into the generative AI and have the generative AI perform the adjustment of the learning plan adjustment method.

[0093] The adjustment unit can select the optimal adjustment method by referring to the user's past learning data when adjusting the learning plan. For example, the adjustment unit can focus on adjusting items that the user has struggled with in the past. The adjustment unit can also select an effective adjustment method from the user's past learning data. Furthermore, the adjustment unit can select an adjustment method for the next learning step based on the user's past learning history. This allows the system to provide the optimal adjustment method based on the user's past learning data. Some or all of the above processing in the adjustment unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the adjustment unit can input the user's past learning data into a generative AI and have the generative AI select the optimal adjustment method.

[0094] The adjustment unit can apply different adjustment algorithms to the user's learning style when adjusting the learning plan. For example, if the user is a visual learner, the adjustment unit can apply an adjustment algorithm that emphasizes visual data. If the user is an auditory learner, the adjustment unit can also apply an adjustment algorithm that emphasizes audio data. Furthermore, if the user is an experiential learner, the adjustment unit can also apply an adjustment algorithm that emphasizes practical data. This allows the adjustment unit to provide an adjustment algorithm that is tailored to the user's learning style. Some or all of the above processing in the adjustment unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the adjustment unit can input the user's learning style data into a generative AI and have the generative AI execute the application of different adjustment algorithms.

[0095] The adjustment unit can estimate the user's emotions and determine the priority of adjustments to the learning plan based on the estimated emotions. For example, if the user is in a hurry, the adjustment unit will prioritize the most important adjustments. If the user is relaxed, the adjustment unit can also make detailed adjustments. Furthermore, if the user is stressed, the adjustment unit can make concise and to-the-point adjustments. This allows the system to provide learning plan adjustment priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using or without a generative AI. For example, the adjustment unit can input user facial expression data into a generative AI and have the generative AI determine the priority of adjustments to the learning plan.

[0096] The adjustment unit can select the optimal adjustment method when adjusting the learning plan, taking into account the user's geographical location information. For example, if the user is in a specific country, the adjustment unit can make adjustments related to the culture and customs of that country. It can also make travel-related adjustments if the user is traveling. Furthermore, if the user is in a specific region, the adjustment unit can make adjustments related to the language and dialect of that region. This allows the system to provide the optimal adjustment method based on the user's geographical location information. Some or all of the above processing in the adjustment unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the adjustment unit can input the user's geographical location information into a generative AI and have the generative AI select the optimal adjustment method.

[0097] The adjustment unit can analyze the user's social media activity and suggest relevant adjustments when adjusting the learning plan. For example, if the user posts about travel on social media, the adjustment unit can make travel-related adjustments. It can also make adjustments related to business terminology and conversation if the user posts about business. Furthermore, if the user posts about their hobbies, the adjustment unit can make adjustments related to those hobbies. This allows the system to provide relevant adjustments based on the user's social media activity. Some or all of the above processing in the adjustment unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the adjustment unit can input the user's social media data into a generative AI and have the generative AI suggest relevant adjustments.

[0098] The delivery unit can estimate the user's emotions and adjust the way learning content is delivered based on the estimated emotions. For example, if the user is relaxed, the delivery unit can deliver learning content in a soft tone. If the user is in a hurry, the delivery unit can also deliver concise and to-the-point learning content. Furthermore, if the user is excited, the delivery unit can deliver learning content with a visually stimulating design. This allows for a delivery method of learning content that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using or without a generative AI. For example, the delivery unit can input user facial expression data into a generative AI and have the generative AI adjust the way learning content is delivered.

[0099] The content delivery unit can select the optimal delivery method by referring to the user's past learning history when providing learning content. For example, the delivery unit can provide relevant learning content based on what the user has learned in the past. The delivery unit can also provide content that focuses on areas where the user struggles, based on the user's past learning data. Furthermore, the delivery unit can analyze the user's past learning history and provide the most effective learning content. This allows the delivery unit to provide the optimal method of providing learning content based on the user's past learning history. Some or all of the above processing in the delivery unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the delivery unit can input the user's past learning data into a generative AI and have the generative AI select the optimal delivery method.

[0100] The content delivery unit can apply different delivery algorithms depending on the user's learning style when delivering learning content. For example, if the user is a visual learner, the delivery unit can apply a delivery method that heavily utilizes visual content. Similarly, if the user is an auditory learner, the delivery unit can apply a delivery method that heavily utilizes audio content. Furthermore, if the user is an experiential learner, the delivery unit can apply a delivery method that heavily utilizes practical content. This allows for the delivery of learning content tailored to the user's learning style. Some or all of the above processing in the delivery unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the delivery unit can input user learning style data into a generative AI and have the generative AI apply different delivery algorithms.

[0101] The service provider can estimate the user's emotions and determine the priority of providing learning content based on the estimated emotions. For example, if the user is in a hurry, the service provider will prioritize providing the most important learning content. If the user is relaxed, the service provider can also provide detailed learning content. Furthermore, if the user is stressed, the service provider can provide concise and to-the-point learning content. This allows for the provision of learning content priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using or without a generative AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI determine the priority of providing learning content.

[0102] The content delivery unit can select the optimal delivery method when providing learning content, taking into account the user's geographical location information. For example, if the user is in a specific country, the delivery unit can provide learning content related to the culture and customs of that country. Furthermore, if the user is traveling, the delivery unit can provide learning content related to travel. Additionally, if the user is in a specific region, the delivery unit can provide learning content related to the language and dialect of that region. This allows the delivery unit to provide the optimal method of delivering learning content based on the user's geographical location information. Some or all of the above processing in the delivery unit may be performed using a generative AI, or without one. For example, the delivery unit can input the user's geographical location information into a generative AI and have the generative AI select the optimal delivery method.

[0103] The content delivery unit can analyze the user's social media activity when providing learning content and suggest relevant delivery methods. For example, if the user posts about travel on social media, the delivery unit can provide travel-related learning content. It can also provide business-related learning content if the user posts about business. Furthermore, if the user posts about their hobbies, the delivery unit can provide learning content related to those hobbies. This allows the delivery unit to provide relevant learning content based on the user's social media activity. Some or all of the above processing in the delivery unit may be performed using a generative AI, or not. For example, the delivery unit can input the user's social media data into a generative AI and have the generative AI suggest relevant delivery methods.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The reception desk can predict learning progress and suggest what to learn next based on the user's learning history. For example, it can suggest grammar points and vocabulary to learn next based on what the user has learned in the past. The reception desk can also analyze the user's learning speed and suggest an appropriate learning pace. Furthermore, the reception desk can suggest learning materials suited to a specific learning style based on the user's learning history. In this way, it can provide an optimal learning plan based on the user's learning history.

[0106] The system can automatically adjust the difficulty level of learning content according to the user's learning progress. For example, if a user completes a particular task, the difficulty level of the next task will be increased. Conversely, for items the user finds difficult, the difficulty level can be lowered for further attempts. Furthermore, the system can provide review questions at appropriate times based on the user's learning progress. This allows for the provision of optimal learning content tailored to the user's learning progress.

[0107] The reception desk can estimate the user's emotions and provide messages to boost their learning motivation based on those estimates. For example, if the user is tired, it can display an encouraging message. If the user is excited, it can display a message that increases their anticipation for the next learning step. Furthermore, if the user is relaxed, it can display a message that emphasizes the enjoyment of learning. In this way, by providing messages that match the user's emotions, it is possible to increase their motivation to learn.

[0108] The reception desk can analyze a user's past learning history and provide appropriate rewards according to their learning progress. For example, it can award badges or points when a user achieves a specific goal. It can also offer special rewards for users who continue learning consistently. Furthermore, it can offer additional rewards when users overcome areas they struggle with. In this way, by providing rewards according to the user's learning progress, it is possible to increase their motivation to learn.

[0109] The reception desk can suggest learning priorities based on the user's current lifestyle and areas of interest when they input their learning objectives or level. For example, if a user is busy with work, it can prioritize suggesting content that can be learned effectively in a short amount of time. If a user wants to learn as a hobby, it can prioritize suggesting content related to that hobby. Furthermore, if a user is planning a trip, it can prioritize suggesting travel-related content. This allows the system to provide a learning plan tailored to the user's lifestyle and areas of interest.

[0110] The reception system can estimate the user's emotions and adjust the criteria for evaluating learning progress based on those emotions. For example, if the user is stressed, the evaluation criteria can be relaxed. Conversely, if the user is relaxed, the criteria can be made stricter. Furthermore, if the user is excited, challenging evaluation criteria can be set. This allows for improved learning efficiency by providing evaluation criteria that are tailored to the user's emotions.

[0111] The reception desk can provide information about local culture and customs based on the user's geographical location when they select a foreign language they wish to learn. For example, if the user is in Europe, it can provide information about European culture and customs. Similarly, if the user is in Asia, it can provide information about Asian culture and customs. Furthermore, if the user is in a specific country, it can provide information about that country's tourist attractions and specialties. This allows the system to stimulate learning interests based on the user's geographical location.

[0112] The reception desk can provide a function to share learning progress based on the user's social media activity when they input their learning objectives and level. For example, if a user achieves a specific goal, they can share that achievement on social media. Furthermore, if a user continues to learn consistently, they can share their progress on social media. Additionally, if a user overcomes a difficult area, they can share that achievement on social media. This allows for increased motivation to learn by sharing the user's learning progress.

[0113] The generation unit can estimate the user's emotions and adjust how the learning plan's progress is visualized based on those emotions. For example, if the user is relaxed, a detailed progress graph can be displayed. If the user is in a hurry, a concise progress bar can be displayed. Furthermore, if the user is excited, a visually stimulating progress animation can be displayed. This provides a method of visualizing progress that responds to the user's emotions, thereby increasing their motivation to learn.

[0114] The generation unit, when generating a learning plan, can refer to the user's past learning data to predict learning progress and suggest review at appropriate times. For example, after a user has learned a specific grammar item, it can suggest reviewing that item after a certain period. It can also suggest reviewing new vocabulary after the user has learned it. Furthermore, it can suggest periodic reviews of items the user has struggled with in the past. This allows the system to provide optimal review timing based on the user's past learning data.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The reception desk accepts the user's selection of the foreign language they wish to learn and inputs their learning objectives and level. For example, if the user wants to learn English, the reception desk can select English and input their objective, such as wanting to learn basic conversation for travel. Step 2: The generation unit generates an optimal learning plan based on the information received by the reception unit. For example, the generation unit selects appropriate learning materials and practice problems according to the user's purpose and level, and customizes the learning plan. The generation unit uses a generation AI to analyze the user's input information and generate the optimal learning plan. Step 3: The learning unit proceeds with learning based on the learning plan generated by the generation unit. For example, the learning unit enables the user to solve practice problems based on everyday conversation phrases and situations. The learning unit uses generational AI to monitor the user's learning progress in real time and provide feedback as needed. Step 4: The analysis unit accumulates the learning data generated by the learning unit and identifies areas for improvement to maximize learning effectiveness. For example, if the analysis unit finds that the user has difficulty with a particular grammatical item or vocabulary, it adjusts the plan to focus on learning those areas. The analysis unit uses generative AI to analyze the user's learning data and identify areas for improvement to maximize learning effectiveness. Step 5: The adjustment unit adjusts the learning plan based on the improvements identified by the analysis unit. For example, the adjustment unit adjusts the plan to focus on grammar items or vocabulary that the user struggles with. The adjustment unit uses generative AI to adjust the learning plan.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0119] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the reception unit, generation unit, learning unit, analysis unit, adjustment unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and accepts the user's selection of the foreign language they wish to learn and input of their learning objectives and level. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's input information to generate an optimal learning plan. The learning unit is implemented by the control unit 46A of the smart device 14 and proceeds with learning based on the generated learning plan. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and stores learning data and identifies areas for improvement to maximize learning effectiveness. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the learning plan based on the areas for improvement identified by the analysis unit. The provision unit is implemented by the control unit 46A of the smart device 14 and enables the user to proceed with learning through a device such as a smartphone or tablet. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the reception unit, generation unit, learning unit, analysis unit, adjustment unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and accepts the user's selection of the foreign language they wish to learn and inputs their learning objectives and level. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's input information to generate an optimal learning plan. The learning unit is implemented by the control unit 46A of the smart glasses 214 and proceeds with learning based on the generated learning plan. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and stores learning data and identifies areas for improvement to maximize learning effectiveness. The adjustment unit is implemented by the identification processing unit 290 of the data processing unit 12 and adjusts the learning plan based on the areas for improvement identified by the analysis unit. The provision unit is implemented by the control unit 46A of the smart glasses 214 and enables the user to proceed with learning through a device such as a smartphone or tablet. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the reception unit, generation unit, learning unit, analysis unit, adjustment unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and accepts the user's selection of the foreign language they wish to learn and inputs their learning objectives and level. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's input information to generate an optimal learning plan. The learning unit is implemented by the control unit 46A of the headset terminal 314 and proceeds with learning based on the generated learning plan. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and stores learning data and identifies areas for improvement to maximize learning effectiveness. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the learning plan based on the areas for improvement identified by the analysis unit. The provision unit is implemented by the control unit 46A of the headset terminal 314 and enables the user to proceed with learning through a device such as a smartphone or tablet. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the reception unit, generation unit, learning unit, analysis unit, adjustment unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and accepts the user's selection of the foreign language they wish to learn and inputs their learning objectives and level. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's input information to generate an optimal learning plan. The learning unit is implemented by the control unit 46A of the robot 414 and proceeds with learning based on the generated learning plan. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and stores learning data and identifies areas for improvement to maximize learning effectiveness. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the learning plan based on the areas for improvement identified by the analysis unit. The provision unit is implemented by the control unit 46A of the robot 414 and enables the user to proceed with learning through a device such as a smartphone or tablet. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0170] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0172] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0173] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0174] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0178] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0179] 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.

[0180] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0181] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0182] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0183] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0185] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0186] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0187] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0188] (Note 1) A reception desk where you can select the foreign language you want to learn and input your learning objectives or level, A generation unit that generates an appropriate learning plan based on the information received by the reception unit, A learning unit that proceeds with learning based on the learning plan generated by the generation unit, An analysis unit accumulates the learning data generated by the aforementioned learning unit and identifies areas for improvement to enhance the learning effect, An adjustment unit adjusts the learning plan based on the areas for improvement identified by the analysis unit, Equipped with A system characterized by the following features. (Note 2) It includes a section for accessing the system via smartphones or tablets, allowing users to continue their learning anytime, anywhere. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is It estimates the user's emotions and, based on those emotions, presents options for foreign languages ​​the user might want to learn. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is It analyzes the user's past learning history and automatically suggests appropriate learning objectives or levels. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is When users enter their learning objectives or level, the system filters the results based on their current life circumstances or areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the input order of the learning objectives and levels based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When selecting a foreign language to learn, the system prioritizes suggesting relevant languages ​​based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When users input their learning objectives and level, the system suggests relevant learning objectives based on their social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is It estimates the user's emotions and adjusts how the learning plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is When generating a learning plan, the system selects the most suitable learning materials and practice problems by referring to the user's past learning data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When generating a learning plan, different learning algorithms are applied depending on the user's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts the length of the learning plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating a learning plan, the priority of the learning plan is determined based on when the user submits it. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a learning plan, the order of the learning plan is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned learning unit, It estimates the user's emotions and adjusts how learning progress is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned learning unit, When monitoring learning progress, the system provides optimal feedback by referencing the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned learning unit, When monitoring learning progress, apply different feedback methods depending on the user's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned learning unit, It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned learning unit, When monitoring learning progress, provide optimal feedback while considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned learning unit, When monitoring learning progress, analyze the user's social media activity and provide relevant feedback. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method of the training data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, When analyzing training data, we refer to the user's past training data to identify the optimal areas for improvement. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, When analyzing training data, different analysis algorithms are applied depending on the user's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, We estimate user emotions and prioritize areas for improvement based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit, When analyzing training data, we identify the optimal areas for improvement by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit, When analyzing training data, we analyze users' social media activity and identify relevant areas for improvement. The system described in Appendix 1, characterized by the features described herein. (Note 27) The adjustment unit is, It estimates the user's emotions and adjusts the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The adjustment unit is, When adjusting the learning plan, the system selects the optimal adjustment method by referring to the user's past learning data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The adjustment unit is, When adjusting the learning plan, different adjustment algorithms are applied depending on the user's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 30) The adjustment unit is, It estimates the user's emotions and determines the priority of adjusting the learning plan based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The adjustment unit is, When adjusting the learning plan, the optimal adjustment method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The adjustment unit is, When adjusting learning plans, we analyze users' social media activity and suggest relevant adjustments. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, It estimates the user's emotions and adjusts how learning content is delivered based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned supply unit is, When providing learning content, the system selects the optimal delivery method by referring to the user's past learning history. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing learning content, different delivery algorithms are applied depending on the user's learning style. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of providing learning content based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned supply unit is, When providing learning content, the optimal delivery method is selected considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned supply unit is, When providing learning content, we analyze users' social media activity and suggest relevant delivery methods. The system described in Appendix 2, characterized by the features described herein. [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where you can select the foreign language you want to learn and input your learning objectives or level, A generation unit that generates an appropriate learning plan based on the information received by the reception unit, A learning unit that proceeds with learning based on the learning plan generated by the generation unit, An analysis unit accumulates the learning data generated by the aforementioned learning unit and identifies areas for improvement to enhance the learning effect, An adjustment unit adjusts the learning plan based on the areas for improvement identified by the analysis unit, Equipped with A system characterized by the following features.

2. It includes a section for accessing the system via smartphones or tablets, allowing users to continue their learning anytime, anywhere. The system according to feature 1.

3. The aforementioned reception unit is It estimates the user's emotions and, based on those emotions, presents options for foreign languages ​​the user might want to learn. The system according to feature 1.

4. The aforementioned reception unit is It analyzes the user's past learning history and automatically suggests appropriate learning objectives or levels. The system according to feature 1.

5. The aforementioned reception unit is When users enter their learning objectives or level, the system filters the results based on their current life circumstances or areas of interest. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the input order of the learning objectives and levels based on the estimated user emotions. The system according to feature 1.

7. The aforementioned reception unit is When selecting a foreign language to learn, the system prioritizes suggesting relevant languages ​​based on the user's geographical location. The system according to feature 1.

8. The aforementioned reception unit is When users input their learning objectives and level, the system suggests relevant learning objectives based on their social media activity. The system according to feature 1.

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