System

Abstract

To provide a system according to an embodiment that provides an optimal language learning content corresponding to a scene of a user.SOLUTION: A system according to an embodiment comprises: a reception unit; a generation unit; and a providing unit. The reception unit is configured to receive an input of a scene using a language. The generation unit is configured to generate a conversation content of a language level corresponding to the scene received by the reception unit. The providing unit is configured to provide learning of the language for the user on the basis of the conversation content generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it is difficult to provide optimal language learning content for individual users, and there is room for improvement.

[0005] The system according to the embodiment aims to provide optimal language learning content according to the user's situation. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives an input of a situation in which a language is used. The generation unit generates a conversation content at a language level corresponding to the situation received by the reception unit. The provision unit provides a user with language learning based on the conversation content generated by the generation unit. Effect of the Invention

[0007] The system according to the embodiment can provide optimal language learning content according to the user's situation. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) A language learning system according to an embodiment of the present invention is a system that allows learning natural English conversation (including languages ​​other than English) using character input and voice recognition. In this system, the AI ​​automatically generates conversation content that matches the user's level by specifying the level of daily conversation, conversation at a travel destination, business English, etc. For example, in the case of travel, conversation content incorporating local information is provided, providing a service tailored to each individual that was not available in previous personal English learning. For example, the user inputs a situation in which the language is used. For example, a situation such as "conversation at a travel destination" or "business meeting" is specified. This information is input to a reception unit (which may include AI processing). Next, a generation unit (which may include AI processing) generates conversation content at a language level corresponding to the situation accepted by the reception unit. For example, when "conversation at a travel destination" is specified, the generation unit generates conversation content related to local tourist information, restaurant reservations, etc. The generated conversation content is provided to the user by a provision unit (which may include AI processing). The user learns the language based on the generated conversation content. For example, the generated conversation content is read aloud, or pronunciation practice is performed using voice recognition. Furthermore, there is a tracking unit (which may include AI processing) that tracks the progress of the user, and automatically adjusts the level according to the progress of learning. For example, when the user reaches a certain level, the tracking unit instructs the generation unit on the conversation content of the next level. In addition, there is also a matching unit (which may include AI processing) that provides a matching function for the user to practice conversation with other users. This allows the user to practice actual conversation with other learners. Furthermore, the reception unit estimates the user's emotions and adjusts the input method of the language and level based on the estimated emotions. For example, if the user is tired, it can suggest simple conversation content. In addition, the reception unit analyzes the user's past learning history and suggests the optimal input method. For example, it adjusts to avoid content that the user was not good at in the past. Finally, when inputting the language and level, the reception unit filters based on the user's current learning situation and goals. This makes it possible to provide the user with the optimal learning content. This makes it possible for the language learning system to provide language learning according to the situation specified by the user.

[0029] The language learning system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit inputs a situation in which a user uses a language. For example, the user specifies a situation such as "conversation at a travel destination" or "business meeting". This information is input to the reception unit. The reception unit, for example, analyzes the information input by the user and determines an appropriate language level. The generation unit generates a conversation content of a language level corresponding to the situation accepted by the reception unit. For example, when "conversation at a travel destination" is specified, the generation unit generates a conversation content related to local tourist information, restaurant reservations, and the like. The generation unit generates a conversation content according to the situation specified by the user, for example, using a text generation AI (for example, LLM). The generation unit can also generate a conversation content according to the situation specified by the user, using a multimodal generation AI. The generation unit, for example, extracts keywords according to the situation specified by the user, and generates a conversation content based on the keywords. The provision unit provides the user with language learning based on the conversation content generated by the generation unit. The provision unit, for example, displays the generated conversation content to the user so that the user can read it out loud. The providing unit can also provide the generated conversation content to the user using a voice recognition technology. For example, when the user reads out the generated conversation content, the providing unit evaluates the accuracy of the pronunciation using the voice recognition technology. In this way, the language learning system according to the embodiment can provide language learning according to a scene specified by the user.

[0030] The reception unit inputs a situation where the user uses language. For example, the user specifies a situation such as "conversation at a travel destination" or "business meeting". This information is input to the reception unit. The reception unit, for example, analyzes the information input by the user and determines an appropriate language level. Specifically, the reception unit analyzes the situation information input by the user using natural language processing technology and extracts keywords and phrases related to the situation. For example, if "conversation at a travel destination" is specified, the reception unit extracts keywords such as "tourist destination", "hotel", and "restaurant", and determines an appropriate language level based on this. Furthermore, the reception unit can suggest optimal learning content in consideration of the user's past learning history and current language skills. For example, if the user is a beginner-level learner, the reception unit suggests conversation content focusing on basic greetings and simple questions, and if the user is an intermediate or higher level learner, the reception unit suggests conversation content including more complex expressions and grammar. The reception unit can also send the information input by the user to a cloud server and share data in cooperation with other system components. This allows the reception unit to flexibly respond to the needs of the user, improving the efficiency and effectiveness of the entire system.

[0031] The generation unit generates a language-level conversation content corresponding to the scene accepted by the reception unit. For example, when "conversation at a travel destination" is specified, the generation unit generates a conversation content related to local tourist information and restaurant reservations. The generation unit generates a conversation content according to the scene specified by the user, for example, using a text generation AI (for example, LLM). Specifically, the generation unit utilizes a large-scale language model (LLM) to generate natural conversation sentences related to the scene specified by the user. For example, when "conversation at a travel destination" is specified, the generation unit generates specific conversation sentences such as "Hello, do you have any recommendations for tourist spots?" or "I would like to make a reservation at this restaurant, is it open?". In addition, the generation unit can generate a conversation content that combines not only text but also other media formats such as images and audio using a multimodal generation AI. For example, a more realistic learning experience is provided by generating a conversation related to the place while displaying photos and maps of the tourist spot. The generation unit extracts keywords according to the scene specified by the user and generates the conversation content based on them. For example, if "business meeting" is specified, the generator generates business-related conversational sentences such as "Are you ready for the presentation?" and "What's on the agenda for the next meeting?" In this way, the generator can provide a variety of conversational content according to the user's needs, supporting effective language learning.

[0032] The providing unit provides the user with language learning based on the conversation content generated by the generating unit. The providing unit, for example, displays the generated conversation content to the user, allowing the user to read it out loud. Specifically, the providing unit displays the generated conversation content in text format on the user's device, and the user can practice pronunciation by reading the content out loud. The providing unit can also provide the generated conversation content to the user using voice recognition technology. For example, when the user reads out the generated conversation content, the providing unit uses voice recognition technology to evaluate the accuracy of the pronunciation. The voice recognition technology analyzes the user's pronunciation and compares it with the correct pronunciation to provide feedback. For example, if the pronunciation of a specific word or phrase is inaccurate, the providing unit highlights that part and plays back the correct pronunciation by voice to prompt the user to correct it. Furthermore, the providing unit can record the user's learning progress and propose an individual learning plan. For example, by having the user repeatedly practice the conversation content in a specific situation, content with gradually increasing difficulty is provided, supporting effective learning. The providing unit can also provide a platform for the user to share the generated conversation content with other users and practice actual conversation. This allows the providing unit to provide the user with a variety of learning methods, maximizing the effectiveness of language learning.

[0033] The tracking unit can track the progress of the user and automatically adjust the level according to the progress of the learning. The tracking unit, for example, records the user's learning time and test results and evaluates the progress based on them. For example, when the user achieves a certain learning time, the tracking unit instructs the generation unit to provide the next level of conversation content. The tracking unit can also provide an appropriate level of conversation content based on the user's test results. For example, when the user obtains a high score in a test, the tracking unit provides the next level of conversation content. This provides an appropriate level of learning according to the user's learning progress. Some or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI or may be performed without using AI. For example, the tracking unit can input the user's learning data to the generation AI and cause the generation AI to perform an evaluation of the progress.

[0034] The matching unit can provide a matching function for a user to practice conversation with other users. The matching unit matches an appropriate partner based on, for example, the user's learning level and interests. For example, the matching unit matches other users of the same level according to the learning level specified by the user. The matching unit can also match other users who have a common interest based on the user's interests. For example, if a user wants to learn "conversation at a travel destination", the matching unit matches other users who are also interested in travel. This makes it possible to practice actual conversation with other learners. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the user's learning data into the generation AI and cause the generation AI to execute matching of an appropriate partner.

[0035] The generation unit can generate conversation content incorporating information about the travel destination. For example, when the user specifies "conversation at the travel destination," the generation unit generates conversation content related to local tourist information and restaurant reservations. For example, the generation unit generates conversation content incorporating information about local tourist spots and culture. The generation unit can also generate conversation content related to reservations at local restaurants and hotels. For example, the generation unit generates conversation content when the user makes a reservation at a local restaurant. This provides conversation content including information about the travel destination. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input local information to the generation AI and cause the generation AI to generate the conversation content.

[0036] The reception unit can analyze the user's past learning history and suggest the optimal input method. For example, the reception unit automatically displays the language and level that the user frequently input in the past as candidates. The reception unit can also preferentially suggest the input method (voice, text, etc.) that the user used in the past. Furthermore, the reception unit predicts and suggests the language and level to be used in a specific time period from the user's past learning history. This provides the optimal input method based on the past learning history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's learning history data to the generation AI and cause the generation AI to execute the suggestion of the optimal input method.

[0037] The reception unit can perform filtering based on the user's current learning situation and goals when inputting the language and level. For example, the reception unit automatically suggests a language of an appropriate level based on the user's current learning situation. The reception unit can also preferentially display related topics and scenarios based on the user's learning goals. Furthermore, the reception unit suggests the optimal language and level for proceeding to the next step in consideration of the user's progress. This provides optimal learning content based on the user's learning situation and goals. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's learning data to the generation AI and cause the generation AI to perform filtering.

[0038] The reception unit can preferentially display a related language and level based on the geographical location information of the user. For example, when the user is in a specific country, the reception unit preferentially displays a level related to the language of that country. In addition, when the user is traveling, the reception unit can automatically suggest a language and level of the travel destination. Furthermore, the reception unit displays a language and level specific to the region based on the user's current location. This provides an optimal language and level based on the geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to execute the display of the related language and level.

[0039] The reception unit can analyze the user's social media activities and suggest related learning content. The reception unit can suggest learning content based on, for example, a language that the user frequently uses on social media. The reception unit can also extract topics of interest from the user's social media activities and suggest related learning content. Furthermore, the reception unit can analyze the user's social media interaction history and suggest the optimal language and level. This provides learning content based on social media activities. A part or all of the above-mentioned processing in the reception unit can be performed, for example, using AI or can be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to execute the suggestion of related learning content.

[0040] The generation unit can generate conversation content specialized for a business scenario. For example, when a user specifies a business meeting, the generation unit generates conversation content related to the progress of the meeting. Furthermore, when a user specifies a presentation, the generation unit can also generate conversation content related to the contents of the presentation. Furthermore, when a user specifies a negotiation, the generation unit generates conversation content related to the progress of the negotiation. This provides conversation content specialized for a business scenario. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input information of the business scenario to the generation AI and cause the generation AI to generate the conversation content.

[0041] The generation unit can generate conversation content based on the local culture and customs in consideration of the geographical location information of the user. For example, when the user is in a specific country, the generation unit generates conversation content based on the culture and customs of that country. In addition, when the user is traveling, the generation unit can also generate conversation content based on the culture and customs of the travel destination. Furthermore, the generation unit generates conversation content based on the culture and customs specific to the region based on the user's current location. This provides conversation content based on the geographical location information. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the geographical location information of the user to the generation AI and cause the generation AI to generate the conversation content.

[0042] The providing unit can adjust the difficulty of the conversation content to be provided according to the user's learning progress. For example, when the user is at a beginner's level, the providing unit provides easy conversation content. Also, when the user is at an intermediate level, the providing unit can provide slightly difficult conversation content. Furthermore, when the user is at an advanced level, the providing unit provides difficult conversation content. In this way, conversation content according to the learning progress is provided. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's learning progress data to the generating AI and cause the generating AI to adjust the difficulty level of the conversation content.

[0043] The providing unit can select the optimal providing method according to the learning style of the user. For example, when the user is a visual learner, the providing unit provides conversation content including a visual aid. Also, when the user is an auditory learner, the providing unit can provide conversation content including an audio guide. Furthermore, when the user is a tactile learner, the providing unit provides interactive conversation content. This provides the optimal providing method according to the learning style. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the learning style data of the user to the generating AI and cause the generating AI to select the optimal providing method.

[0044] The tracking unit can analyze the user's learning history and select the optimal tracking method. The tracking unit can, for example, suggest the optimal tracking method based on the tracking method used by the user in the past. The tracking unit can also preferentially track the progress of a specific topic or scenario from the user's learning history. Furthermore, the tracking unit can analyze the user's learning history and suggest the most efficient tracking method. This provides the optimal tracking method based on the learning history. A part or all of the above-mentioned processing in the tracking unit can be performed, for example, using AI or can be performed without using AI. For example, the tracking unit can input the user's learning history data to the generation AI and cause the generation AI to select the optimal tracking method.

[0045] The tracking unit can provide an optimal tracking method according to the learning style of the user. For example, if the user is a visual learner, the tracking unit provides a progress display including a visual aid. Also, if the user is an auditory learner, the tracking unit can provide a progress display including an audio guide. Furthermore, if the user is a tactile learner, the tracking unit provides an interactive progress display. This provides an optimal tracking method according to the learning style. A part or all of the above-mentioned processing in the tracking unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the tracking unit can input the learning style data of the user to the generation AI and cause the generation AI to provide the optimal tracking method.

[0046] The matching unit can analyze the user's learning history and select the optimal matching method. The matching unit can, for example, suggest the optimal matching method based on the matching method used by the user in the past. The matching unit can also prioritize matching for a specific topic or scenario based on the user's learning history. Furthermore, the matching unit can analyze the user's learning history and suggest the most efficient matching method. This provides the optimal matching method based on the learning history. Some or all of the above-mentioned processing in the matching unit can be performed, for example, using AI or can be performed without using AI. For example, the matching unit can input the user's learning history data to the generation AI and cause the generation AI to select the optimal matching method.

[0047] The matching unit can provide an optimal matching method according to the learning style of the user. For example, if the user is a visual learner, the matching unit provides a matching method including a visual aid. Also, if the user is an auditory learner, the matching unit can provide a matching method including an audio guide. Furthermore, if the user is a tactile learner, the matching unit provides an interactive matching method. This provides an optimal matching method according to the learning style. A part or all of the above-mentioned processing in the matching unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the matching unit can input the learning style data of the user to the generation AI and cause the generation AI to provide the optimal matching method.

[0048] The matching unit can display related matching partners preferentially in consideration of the geographical location information of the user. For example, when the user is in a specific country, the matching unit displays matching partners based on the culture and customs of that country. In addition, when the user is traveling, the matching unit can also display matching partners based on the culture and customs of the travel destination. Furthermore, the matching unit displays matching partners based on the culture and customs specific to the region based on the user's current location. This provides optimal matching partners based on the geographical location information. Part or all of the above-mentioned processing in the matching unit may be performed, for example, using AI, or may be performed without using AI. For example, the matching unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the display of related matching partners.

[0049] The matching unit can analyze the user's social media activity and display related matching partners. The matching unit can display matching partners based on, for example, topics frequently used by the user on social media. The matching unit can also extract topics of interest from the user's social media activity and display matching partners. Furthermore, the matching unit can analyze the user's social media exchange history and display related matching partners. This provides optimal matching partners based on social media activity. Some or all of the above-mentioned processing in the matching unit can be performed, for example, using AI or can be performed without using AI. For example, the matching unit can input the user's social media data to the generation AI and cause the generation AI to display related matching partners.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The reception unit can suggest the most suitable learning method based on the user's learning style. For example, for a visual learner, learning materials that make extensive use of images and videos can be suggested. For an auditory learner, learning materials in the form of audio guides or podcasts can be provided. Furthermore, for a tactile learner, learning materials in the form of interactive quizzes or games can be suggested. In this way, the most suitable learning method according to the user's learning style can be provided.

[0052] The generation unit can analyze the user's learning history and generate conversation content that focuses on reviewing topics that the user struggled with in the past. For example, if the user struggled with a particular part of grammar in the past, the generation unit can generate conversation content that includes that part. Also, if the user has difficulty remembering certain words or phrases, the generation unit can generate conversation content that repeatedly uses those words or phrases. Furthermore, the generation unit can generate conversation content for the user to correct pronunciation mistakes that the user made in the past. This provides effective review based on the user's learning history.

[0053] The providing unit can adjust the difficulty of the learning content in real time based on the user's learning progress. For example, if the user obtains a high score on a specific topic, the difficulty of the next topic can be increased. Also, if the user obtains a low score, the difficulty of the same topic can be lowered and provided again. Furthermore, if the user obtains a medium score, the difficulty level can be fine-tuned and provided. In this way, learning content of an optimal difficulty level according to the user's learning progress is provided.

[0054] The tracking unit can propose an optimal study schedule based on the user's study time period. For example, if the user mostly studies in the morning, the tracking unit can propose study content optimal for the morning time period. Also, if the user mostly studies at night, the tracking unit can provide study content suitable for the evening time period. Furthermore, if the user concentrates on studying on weekends, the tracking unit can propose a study plan specialized for weekends. In this way, an optimal study schedule according to the user's study time period is provided.

[0055] The matching unit can suggest optimal conversation partners based on the user's learning goals. For example, if the user wants to learn business English, it can match other users who also want to learn business English. Also, if the user wants to learn travel English, it can suggest other users who are interested in travel English. Furthermore, if the user wants to prepare for a specific exam, it can match other users who are studying for that exam. In this way, optimal conversation partners according to the user's learning goals are provided.

[0056] The process flow of the first embodiment will be briefly described below.

[0057] Step 1: The reception unit inputs situations in which the user will use language. For example, the user specifies situations such as "conversations while traveling" or "business meetings." This information is input to the reception unit. The reception unit analyzes the information input by the user and determines the appropriate language level. Step 2: The generation unit generates language-level conversation content corresponding to the scene accepted by the acceptance unit. For example, if "conversation at a travel destination" is specified, the generation unit generates conversation content related to local tourist information, restaurant reservations, etc. The generation unit uses a text generation AI (e.g., LLM) or a multimodal generation AI to generate conversation content according to the scene specified by the user. The generation unit also extracts keywords corresponding to the scene specified by the user and generates conversation content based on them. Step 3: The providing unit provides the user with language learning based on the conversation content generated by the generating unit. The providing unit displays the generated conversation content to the user so that the user can read it out loud. The providing unit can also provide the generated conversation content to the user using voice recognition technology. For example, the providing unit uses voice recognition technology to evaluate the accuracy of pronunciation when the user reads out the generated conversation content.

[0058] (Example 2) A language learning system according to an embodiment of the present invention is a system that allows learning natural English conversation (including languages ​​other than English) using character input and voice recognition. In this system, the AI ​​automatically generates conversation content that matches the user's level by specifying the level of daily conversation, conversation at a travel destination, business English, etc. For example, in the case of travel, conversation content incorporating local information is provided, providing a service tailored to each individual that was not available in previous personal English learning. For example, the user inputs a situation in which the language is used. For example, a situation such as "conversation at a travel destination" or "business meeting" is specified. This information is input to a reception unit (which may include AI processing). Next, a generation unit (which may include AI processing) generates conversation content at a language level corresponding to the situation accepted by the reception unit. For example, when "conversation at a travel destination" is specified, the generation unit generates conversation content related to local tourist information, restaurant reservations, etc. The generated conversation content is provided to the user by a provision unit (which may include AI processing). The user learns the language based on the generated conversation content. For example, the generated conversation content is read aloud, or pronunciation practice is performed using voice recognition. Furthermore, there is a tracking unit (which may include AI processing) that tracks the progress of the user, and automatically adjusts the level according to the progress of learning. For example, when the user reaches a certain level, the tracking unit instructs the generation unit on the conversation content of the next level. In addition, there is also a matching unit (which may include AI processing) that provides a matching function for the user to practice conversation with other users. This allows the user to practice actual conversation with other learners. Furthermore, the reception unit estimates the user's emotions and adjusts the input method of the language and level based on the estimated emotions. For example, if the user is tired, it can suggest simple conversation content. In addition, the reception unit analyzes the user's past learning history and suggests the optimal input method. For example, it adjusts to avoid content that the user was not good at in the past. Finally, when inputting the language and level, the reception unit filters based on the user's current learning situation and goals. This makes it possible to provide the user with the optimal learning content. This makes it possible for the language learning system to provide language learning according to the situation specified by the user.

[0059] The language learning system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit inputs a situation in which a user uses a language. For example, the user specifies a situation such as "conversation at a travel destination" or "business meeting". This information is input to the reception unit. The reception unit, for example, analyzes the information input by the user and determines an appropriate language level. The generation unit generates a conversation content of a language level corresponding to the situation accepted by the reception unit. For example, when "conversation at a travel destination" is specified, the generation unit generates a conversation content related to local tourist information, restaurant reservations, and the like. The generation unit generates a conversation content according to the situation specified by the user, for example, using a text generation AI (for example, LLM). The generation unit can also generate a conversation content according to the situation specified by the user, using a multimodal generation AI. The generation unit, for example, extracts keywords according to the situation specified by the user, and generates a conversation content based on the keywords. The provision unit provides the user with language learning based on the conversation content generated by the generation unit. The provision unit, for example, displays the generated conversation content to the user so that the user can read it out loud. The providing unit can also provide the generated conversation content to the user using a voice recognition technology. For example, when the user reads out the generated conversation content, the providing unit evaluates the accuracy of the pronunciation using the voice recognition technology. In this way, the language learning system according to the embodiment can provide language learning according to a scene specified by the user.

[0060] The reception unit inputs a situation where the user uses language. For example, the user specifies a situation such as "conversation at a travel destination" or "business meeting". This information is input to the reception unit. The reception unit, for example, analyzes the information input by the user and determines an appropriate language level. Specifically, the reception unit analyzes the situation information input by the user using natural language processing technology and extracts keywords and phrases related to the situation. For example, if "conversation at a travel destination" is specified, the reception unit extracts keywords such as "tourist destination", "hotel", and "restaurant", and determines an appropriate language level based on this. Furthermore, the reception unit can suggest optimal learning content in consideration of the user's past learning history and current language skills. For example, if the user is a beginner-level learner, the reception unit suggests conversation content focusing on basic greetings and simple questions, and if the user is an intermediate or higher level learner, the reception unit suggests conversation content including more complex expressions and grammar. The reception unit can also send the information input by the user to a cloud server and share data in cooperation with other system components. This allows the reception unit to flexibly respond to the needs of the user, improving the efficiency and effectiveness of the entire system.

[0061] The generation unit generates a language-level conversation content corresponding to the scene accepted by the reception unit. For example, when "conversation at a travel destination" is specified, the generation unit generates a conversation content related to local tourist information and restaurant reservations. The generation unit generates a conversation content according to the scene specified by the user, for example, using a text generation AI (for example, LLM). Specifically, the generation unit utilizes a large-scale language model (LLM) to generate natural conversation sentences related to the scene specified by the user. For example, when "conversation at a travel destination" is specified, the generation unit generates specific conversation sentences such as "Hello, do you have any recommendations for tourist spots?" or "I would like to make a reservation at this restaurant, is it open?". In addition, the generation unit can generate a conversation content that combines not only text but also other media formats such as images and audio using a multimodal generation AI. For example, a more realistic learning experience is provided by generating a conversation related to the place while displaying photos and maps of the tourist spot. The generation unit extracts keywords according to the scene specified by the user and generates the conversation content based on them. For example, if "business meeting" is specified, the generator generates business-related conversational sentences such as "Are you ready for the presentation?" and "What's on the agenda for the next meeting?" In this way, the generator can provide a variety of conversational content according to the user's needs, supporting effective language learning.

[0062] The providing unit provides the user with language learning based on the conversation content generated by the generating unit. The providing unit, for example, displays the generated conversation content to the user, allowing the user to read it out loud. Specifically, the providing unit displays the generated conversation content in text format on the user's device, and the user can practice pronunciation by reading the content out loud. The providing unit can also provide the generated conversation content to the user using voice recognition technology. For example, when the user reads out the generated conversation content, the providing unit uses voice recognition technology to evaluate the accuracy of the pronunciation. The voice recognition technology analyzes the user's pronunciation and compares it with the correct pronunciation to provide feedback. For example, if the pronunciation of a specific word or phrase is inaccurate, the providing unit highlights that part and plays back the correct pronunciation by voice to prompt the user to correct it. Furthermore, the providing unit can record the user's learning progress and propose an individual learning plan. For example, by having the user repeatedly practice the conversation content in a specific situation, content with gradually increasing difficulty is provided, supporting effective learning. The providing unit can also provide a platform for the user to share the generated conversation content with other users and practice actual conversation. This allows the providing unit to provide the user with a variety of learning methods, maximizing the effectiveness of language learning.

[0063] The tracking unit can track the progress of the user and automatically adjust the level according to the progress of the learning. The tracking unit, for example, records the user's learning time and test results and evaluates the progress based on them. For example, when the user achieves a certain learning time, the tracking unit instructs the generation unit to provide the next level of conversation content. The tracking unit can also provide an appropriate level of conversation content based on the user's test results. For example, when the user obtains a high score in a test, the tracking unit provides the next level of conversation content. This provides an appropriate level of learning according to the user's learning progress. Some or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI or may be performed without using AI. For example, the tracking unit can input the user's learning data to the generation AI and cause the generation AI to perform an evaluation of the progress.

[0064] The matching unit can provide a matching function for a user to practice conversation with other users. The matching unit matches an appropriate partner based on, for example, the user's learning level and interests. For example, the matching unit matches other users of the same level according to the learning level specified by the user. The matching unit can also match other users who have a common interest based on the user's interests. For example, if a user wants to learn "conversation at a travel destination", the matching unit matches other users who are also interested in travel. This makes it possible to practice actual conversation with other learners. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the user's learning data into the generation AI and cause the generation AI to execute matching of an appropriate partner.

[0065] The generation unit can generate conversation content incorporating information about the travel destination. For example, when the user specifies "conversation at the travel destination," the generation unit generates conversation content related to local tourist information and restaurant reservations. For example, the generation unit generates conversation content incorporating information about local tourist spots and culture. The generation unit can also generate conversation content related to reservations at local restaurants and hotels. For example, the generation unit generates conversation content when the user makes a reservation at a local restaurant. This provides conversation content including information about the travel destination. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input local information to the generation AI and cause the generation AI to generate the conversation content.

[0066] The reception unit can estimate the user's emotion and adjust the language and level input method based on the estimated user's emotion. For example, when the user is stressed, the reception unit provides a simple interface and minimizes input procedures. In addition, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit prioritizes voice input and enables the language and level to be input quickly. This provides an input method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0067] The reception unit can analyze the user's past learning history and suggest the optimal input method. For example, the reception unit automatically displays the language and level that the user frequently input in the past as candidates. The reception unit can also preferentially suggest the input method (voice, text, etc.) that the user used in the past. Furthermore, the reception unit predicts and suggests the language and level to be used in a specific time period from the user's past learning history. This provides the optimal input method based on the past learning history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's learning history data to the generation AI and cause the generation AI to execute the suggestion of the optimal input method.

[0068] The reception unit can perform filtering based on the user's current learning situation and goals when inputting the language and level. For example, the reception unit automatically suggests a language of an appropriate level based on the user's current learning situation. The reception unit can also preferentially display related topics and scenarios based on the user's learning goals. Furthermore, the reception unit suggests the optimal language and level for proceeding to the next step in consideration of the user's progress. This provides optimal learning content based on the user's learning situation and goals. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's learning data to the generation AI and cause the generation AI to perform filtering.

[0069] The reception unit can estimate the user's emotion and change the design of the input interface based on the estimated user's emotion. For example, when the user is nervous, the reception unit provides an interface with a calm color scheme to reduce visual stress. Also, when the user is having fun, the reception unit can provide an interface with a bright color scheme to make the input work enjoyable. Furthermore, when the user is tired, the reception unit provides a simple and highly visible interface to facilitate the input work. This provides an interface according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute the design change of the interface.

[0070] The reception unit can preferentially display a related language and level based on the geographical location information of the user. For example, when the user is in a specific country, the reception unit preferentially displays a level related to the language of that country. In addition, when the user is traveling, the reception unit can automatically suggest a language and level of the travel destination. Furthermore, the reception unit displays a language and level specific to the region based on the user's current location. This provides an optimal language and level based on the geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to execute the display of the related language and level.

[0071] The reception unit can analyze the user's social media activities and suggest related learning content. The reception unit can suggest learning content based on, for example, a language that the user frequently uses on social media. The reception unit can also extract topics of interest from the user's social media activities and suggest related learning content. Furthermore, the reception unit can analyze the user's social media interaction history and suggest the optimal language and level. This provides learning content based on social media activities. A part or all of the above-mentioned processing in the reception unit can be performed, for example, using AI or can be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to execute the suggestion of related learning content.

[0072] The generation unit can estimate the user's emotion and adjust the expression method of the conversation content based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates a conversation content that proceeds at a leisurely pace. Also, when the user is in a hurry, the generation unit can generate a short conversation content that focuses on the main points. Furthermore, when the user is excited, the generation unit generates a conversation content with a visually stimulating effect. In this way, the conversation content according to the user's emotion is provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the conversation content.

[0073] The generation unit can generate conversation content specialized for a business scenario. For example, when a user specifies a business meeting, the generation unit generates conversation content related to the progress of the meeting. Furthermore, when a user specifies a presentation, the generation unit can also generate conversation content related to the contents of the presentation. Furthermore, when a user specifies a negotiation, the generation unit generates conversation content related to the progress of the negotiation. This provides conversation content specialized for a business scenario. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input information of the business scenario to the generation AI and cause the generation AI to generate the conversation content.

[0074] The generation unit can generate conversation content based on the local culture and customs in consideration of the geographical location information of the user. For example, when the user is in a specific country, the generation unit generates conversation content based on the culture and customs of that country. In addition, when the user is traveling, the generation unit can also generate conversation content based on the culture and customs of the travel destination. Furthermore, the generation unit generates conversation content based on the culture and customs specific to the region based on the user's current location. This provides conversation content based on the geographical location information. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the geographical location information of the user to the generation AI and cause the generation AI to generate the conversation content.

[0075] The providing unit can estimate the user's emotion and adjust the providing method based on the estimated user's emotion. For example, when the user is nervous, the providing unit provides a simple and highly visible display method. In addition, when the user is relaxed, the providing unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the providing unit provides a display method that focuses on the main points. This provides a providing method according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to execute the adjustment of the providing method.

[0076] The providing unit can adjust the difficulty of the conversation content to be provided according to the user's learning progress. For example, when the user is at a beginner's level, the providing unit provides easy conversation content. Also, when the user is at an intermediate level, the providing unit can provide slightly difficult conversation content. Furthermore, when the user is at an advanced level, the providing unit provides difficult conversation content. In this way, conversation content according to the learning progress is provided. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's learning progress data to the generating AI and cause the generating AI to adjust the difficulty level of the conversation content.

[0077] The providing unit can select the optimal providing method according to the learning style of the user. For example, when the user is a visual learner, the providing unit provides conversation content including a visual aid. Also, when the user is an auditory learner, the providing unit can provide conversation content including an audio guide. Furthermore, when the user is a tactile learner, the providing unit provides interactive conversation content. This provides the optimal providing method according to the learning style. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the learning style data of the user to the generating AI and cause the generating AI to select the optimal providing method.

[0078] The tracking unit can estimate the user's emotions and adjust the method of tracking the learning progress based on the estimated user's emotions. For example, when the user is stressed, the tracking unit provides a simple progress display to reduce visual burden. In addition, when the user is relaxed, the tracking unit can provide detailed progress information to enhance the sense of accomplishment of learning. Furthermore, when the user is in a hurry, the tracking unit provides a progress display with the main points, allowing the user to quickly grasp the learning situation. This provides a method of tracking the learning progress according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the tracking unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the tracking unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the method of tracking the learning progress.

[0079] The tracking unit can analyze the user's learning history and select the optimal tracking method. The tracking unit can, for example, suggest the optimal tracking method based on the tracking method used by the user in the past. The tracking unit can also preferentially track the progress of a specific topic or scenario from the user's learning history. Furthermore, the tracking unit can analyze the user's learning history and suggest the most efficient tracking method. This provides the optimal tracking method based on the learning history. A part or all of the above-mentioned processing in the tracking unit can be performed, for example, using AI or can be performed without using AI. For example, the tracking unit can input the user's learning history data to the generation AI and cause the generation AI to select the optimal tracking method.

[0080] The tracking unit can provide an optimal tracking method according to the learning style of the user. For example, if the user is a visual learner, the tracking unit provides a progress display including a visual aid. Also, if the user is an auditory learner, the tracking unit can provide a progress display including an audio guide. Furthermore, if the user is a tactile learner, the tracking unit provides an interactive progress display. This provides an optimal tracking method according to the learning style. A part or all of the above-mentioned processing in the tracking unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the tracking unit can input the learning style data of the user to the generation AI and cause the generation AI to provide the optimal tracking method.

[0081] The matching unit can estimate the user's emotion and adjust the matching method based on the estimated user's emotion. For example, when the user is nervous, the matching unit preferentially matches a partner who has a relaxed atmosphere. Also, when the user is relaxed, the matching unit can also match a partner who has a similarly relaxed atmosphere. Furthermore, when the user is in a hurry, the matching unit preferentially proposes a partner who can be quickly matched. This provides a matching method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the matching unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the matching unit can input the user's emotion data to the generation AI and cause the generation AI to execute the adjustment of the matching method.

[0082] The matching unit can analyze the user's learning history and select the optimal matching method. The matching unit can, for example, suggest the optimal matching method based on the matching method used by the user in the past. The matching unit can also prioritize matching for a specific topic or scenario based on the user's learning history. Furthermore, the matching unit can analyze the user's learning history and suggest the most efficient matching method. This provides the optimal matching method based on the learning history. Some or all of the above-mentioned processing in the matching unit can be performed, for example, using AI or can be performed without using AI. For example, the matching unit can input the user's learning history data to the generation AI and cause the generation AI to select the optimal matching method.

[0083] The matching unit can provide an optimal matching method according to the learning style of the user. For example, if the user is a visual learner, the matching unit provides a matching method including a visual aid. Also, if the user is an auditory learner, the matching unit can provide a matching method including an audio guide. Furthermore, if the user is a tactile learner, the matching unit provides an interactive matching method. This provides an optimal matching method according to the learning style. A part or all of the above-mentioned processing in the matching unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the matching unit can input the learning style data of the user to the generation AI and cause the generation AI to provide the optimal matching method.

[0084] The matching unit can estimate the user's emotion and adjust the display method of the matching partner based on the estimated user's emotion. For example, when the user is nervous, the matching unit preferentially displays a partner with a relaxed atmosphere. Also, when the user is relaxed, the matching unit can also display a partner with a similarly relaxed atmosphere. Furthermore, when the user is in a hurry, the matching unit preferentially displays a partner who can be quickly matched. This provides a display method of the matching partner according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the matching unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the matching unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the matching partner.

[0085] The matching unit can display related matching partners preferentially in consideration of the geographical location information of the user. For example, when the user is in a specific country, the matching unit displays matching partners based on the culture and customs of that country. In addition, when the user is traveling, the matching unit can also display matching partners based on the culture and customs of the travel destination. Furthermore, the matching unit displays matching partners based on the culture and customs specific to the region based on the user's current location. This provides optimal matching partners based on the geographical location information. Part or all of the above-mentioned processing in the matching unit may be performed, for example, using AI, or may be performed without using AI. For example, the matching unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the display of related matching partners.

[0086] The matching unit can analyze the user's social media activity and display related matching partners. The matching unit can display matching partners based on, for example, topics frequently used by the user on social media. The matching unit can also extract topics of interest from the user's social media activity and display matching partners. Furthermore, the matching unit can analyze the user's social media exchange history and display related matching partners. This provides optimal matching partners based on social media activity. Some or all of the above-mentioned processing in the matching unit can be performed, for example, using AI or can be performed without using AI. For example, the matching unit can input the user's social media data to the generation AI and cause the generation AI to display related matching partners.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The reception unit can suggest the most suitable learning method based on the user's learning style. For example, for a visual learner, learning materials that make extensive use of images and videos can be suggested. For an auditory learner, learning materials in the form of audio guides or podcasts can be provided. Furthermore, for a tactile learner, learning materials in the form of interactive quizzes or games can be suggested. In this way, the most suitable learning method according to the user's learning style can be provided.

[0089] The generation unit can analyze the user's learning history and generate conversation content that focuses on reviewing topics that the user struggled with in the past. For example, if the user struggled with a particular part of grammar in the past, the generation unit can generate conversation content that includes that part. Also, if the user has difficulty remembering certain words or phrases, the generation unit can generate conversation content that repeatedly uses those words or phrases. Furthermore, the generation unit can generate conversation content for the user to correct pronunciation mistakes that the user made in the past. This provides effective review based on the user's learning history.

[0090] The providing unit can adjust the difficulty of the learning content in real time based on the user's learning progress. For example, if the user obtains a high score on a specific topic, the difficulty of the next topic can be increased. Also, if the user obtains a low score, the difficulty of the same topic can be lowered and provided again. Furthermore, if the user obtains a medium score, the difficulty level can be fine-tuned and provided. In this way, learning content of an optimal difficulty level according to the user's learning progress is provided.

[0091] The tracking unit can propose an optimal study schedule based on the user's study time period. For example, if the user mostly studies in the morning, the tracking unit can propose study content optimal for the morning time period. Also, if the user mostly studies at night, the tracking unit can provide study content suitable for the evening time period. Furthermore, if the user concentrates on studying on weekends, the tracking unit can propose a study plan specialized for weekends. In this way, an optimal study schedule according to the user's study time period is provided.

[0092] The matching unit can suggest optimal conversation partners based on the user's learning goals. For example, if the user wants to learn business English, it can match other users who also want to learn business English. Also, if the user wants to learn travel English, it can suggest other users who are interested in travel English. Furthermore, if the user wants to prepare for a specific exam, it can match other users who are studying for that exam. In this way, optimal conversation partners according to the user's learning goals are provided.

[0093] The reception unit can estimate the user's emotions and adjust the topic of the learning content based on the estimated emotions. For example, if the user is feeling stressed, a relaxing topic can be provided. If the user is excited, a challenging topic can be provided. Furthermore, if the user is tired, an easy and fun topic can be provided. In this way, the optimal learning topic according to the user's emotions is provided.

[0094] The generation unit can estimate the user's emotion and adjust the tone of the conversation content based on the estimated emotion. For example, when the user is relaxed, the generation unit can generate a conversation content with a casual tone. When the user is nervous, the generation unit can generate a conversation content with a formal and calm tone. Furthermore, when the user is having fun, the generation unit can generate a conversation content with a humorous tone. In this way, an optimal conversation tone according to the user's emotion is provided.

[0095] The providing unit can estimate the user's emotion and adjust the learning feedback method based on the estimated emotion. For example, if the user is depressed, feedback including an encouraging message can be provided. If the user is confident, feedback pointing out specific areas for improvement can be provided. Furthermore, if the user is impatient, quick and concise feedback can be provided. In this way, optimal feedback according to the user's emotion can be provided.

[0096] The tracking unit can estimate the user's emotions and adjust the way in which the learning progress is displayed based on the estimated emotions. For example, if the user is stressed, a simple and visually less burdensome progress display can be provided. If the user is relaxed, detailed progress information can be provided. If the user is in a hurry, a progress display that focuses on the main points can be provided. In this way, an optimal progress display according to the user's emotions can be provided.

[0097] The matching unit can estimate the user's emotions and adjust the method of suggesting match-up partners based on the estimated emotions. For example, if the user is nervous, a partner with a relaxed mood can be preferentially suggested. Also, if the user is relaxed, a partner with a similarly relaxed mood can be suggested. Furthermore, if the user is in a hurry, a partner who can be quickly matched can be preferentially suggested. In this way, the optimal match-up partner according to the user's emotions is provided.

[0098] The process flow of the second embodiment will be briefly described below.

[0099] Step 1: The reception unit inputs situations in which the user will use language. For example, the user specifies situations such as "conversations while traveling" or "business meetings." This information is input to the reception unit. The reception unit analyzes the information input by the user and determines the appropriate language level. Step 2: The generation unit generates language-level conversation content corresponding to the scene accepted by the acceptance unit. For example, if "conversation at a travel destination" is specified, the generation unit generates conversation content related to local tourist information, restaurant reservations, etc. The generation unit uses a text generation AI (e.g., LLM) or a multimodal generation AI to generate conversation content according to the scene specified by the user. The generation unit also extracts keywords corresponding to the scene specified by the user and generates conversation content based on them. Step 3: The providing unit provides the user with language learning based on the conversation content generated by the generating unit. The providing unit displays the generated conversation content to the user so that the user can read it out loud. The providing unit can also provide the generated conversation content to the user using voice recognition technology. For example, the providing unit uses voice recognition technology to evaluate the accuracy of pronunciation when the user reads out the generated conversation content.

[0100] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0102] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0103] For example, the reception unit is realized by the reception device 38 of the smart device 14. For example, information specifying a scene such as "conversation at a travel destination" or "business meeting" by the user is input through a touch panel 38A or a microphone 38B. The generation unit is realized by, for example, a specific processing unit 290 of the data processing device 12, and generates conversation content according to the scene specified by the user. The provision unit is realized by, for example, an output device 40 of the smart device 14, and provides the generated conversation content to the user through a display 40A or a speaker 40B. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0111] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0114] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0116] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0119] For example, the reception unit is realized by the microphone 238 of the smart glasses 214. For example, information specifying a scene such as "conversation at a travel destination" or "business meeting" by the user is input through the microphone 238. The generation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and generates conversation content according to the scene specified by the user. The provision unit is realized by, for example, the speaker 240 of the smart glasses 214, and provides the generated conversation content to the user by voice. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

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

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

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

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

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

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

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

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

[0130] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

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

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

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

[0135] For example, the reception unit is realized by the microphone 238 of the headset terminal 314. For example, information specifying a scene such as "conversation at a travel destination" or "business meeting" by the user is input through the microphone 238. The generation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and generates conversation content according to the scene specified by the user. The provision unit is realized by, for example, the display 343 and the speaker 240 of the headset terminal 314, and provides the generated conversation content to the user visually and audibly. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0147] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] For example, the reception unit is realized by the microphone 238 of the robot 414. For example, information specifying a scene such as "conversation at a travel destination" or "business meeting" by the user is input through the microphone 238. The generation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and generates conversation content according to the scene specified by the user. The provision unit is realized by, for example, the speaker 240 and display device of the robot 414, and provides the generated conversation content to the user in audio and visual form. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0153] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0154] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0157] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0158] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] In addition, 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 upon request from the data processing device 12.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0165] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration in which a processor is used that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0169] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0171] (Appendix 1) A reception unit that receives an input of a situation in which a language is used; a generation unit that generates a conversation content of a language level corresponding to the scene accepted by the acceptance unit; A provision unit that provides a user with language learning based on the conversation content generated by the generation unit. A system characterized by: (Appendix 2) Track user progress, Equipped with a tracking section that automatically adjusts the level according to the learning progress 2. The system of claim 1, (Appendix 3) A matching unit is provided that provides a matching function for users to practice conversation with other users. 2. The system of claim 1, (Appendix 4) The generation unit is Generate conversation content incorporating travel destination information 2. The system of claim 1, (Appendix 5) The reception unit is Infer user emotion and adjust language and level input method based on the estimated user emotion 2. The system of claim 1, (Appendix 6) The reception unit is Analyze the user's past learning history and suggest appropriate input methods 2. The system of claim 1, (Appendix 7) The reception unit is When entering language and level, filters are performed based on the user's current learning situation and goals 2. The system of claim 1, (Appendix 8) The reception unit is Estimating user emotions and changing the design of the input interface based on the estimated user emotions 2. The system of claim 1, (Appendix 9) The reception unit is Prioritize relevant languages ​​and levels based on the user's geographic location 2. The system of claim 1, (Appendix 10) The reception unit is Analyze users' social media activity and suggest relevant learning content 2. The system of claim 1, (Appendix 11) The generation unit is Estimate the user's emotions and adjust the way the conversation is expressed based on the estimated user's emotions 2. The system of claim 1, (Appendix 12) The generation unit is Generate conversation content specific to your business scenario 2. The system of claim 1, (Appendix 13) The generation unit is Generate conversations based on local culture and customs, taking into account the user's geographic location 2. The system of claim 1, (Appendix 14) The providing unit is To estimate a user's emotion and adjust a method of providing information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 15) The providing unit is Adjust the difficulty of the conversation content provided according to the user's learning progress 2. The system of claim 1, (Appendix 16) The providing unit is Select the appropriate delivery method based on the user's learning style 2. The system of claim 1, (Appendix 17) The tracking unit is Inferring a user's emotions and adjusting a method for tracking learning progress based on the inferred user emotions 3. The system of claim 2, (Appendix 18) The tracking unit is Analyze user learning history and choose the appropriate tracking method 3. The system of claim 2, (Appendix 19) The tracking unit is Provide optimal tracking methods based on users' learning styles 3. The system of claim 2, (Appendix 20) The matching unit is To estimate a user's emotion and adjust a matching method based on the estimated user's emotion. 4. The system of claim 3. (Appendix 21) The matching unit is Analyze the user's learning history and select the appropriate matching method 4. The system of claim 3. (Appendix 22) The matching unit is Providing optimal matching methods according to the user's learning style 4. The system of claim 3. (Appendix 23) The matching unit is Estimate the user's emotions and adjust the way matches are displayed based on the estimated user's emotions 4. The system of claim 3. (Appendix 24) The matching unit is Taking into account the user's geographic location, it prioritizes relevant matches 4. The system of claim 3. (Appendix 25) The matching unit is Analyze your social media activity to show relevant matches 4. The system of claim 3. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives an input of a situation in which a language is used; a generation unit that generates a conversation content of a language level corresponding to the scene accepted by the acceptance unit; A provision unit that provides a user with language learning based on the conversation content generated by the generation unit. A system characterized by:

2. Track user progress, Equipped with a tracking section that automatically adjusts the level according to the learning progress 2. The system of claim 1 .

3. A matching unit is provided that provides a matching function for users to practice conversation with other users.

2. The system of claim 1 .

4. The generation unit is Generate conversation content incorporating travel destination information 2. The system of claim 1 .

5. The reception unit is Infer user emotion and adjust language and level input method based on the estimated user emotion 2. The system of claim 1 .

6. The reception unit is Analyze the user's past learning history and suggest appropriate input methods 2. The system of claim 1 .

7. The reception unit is When entering language and level, filters are performed based on the user's current learning situation and goals 2. The system of claim 1 .

8. The reception unit is Estimating user emotions and changing the design of the input interface based on the estimated user emotions 2. The system of claim 1 .

9. The reception unit is Prioritize relevant languages ​​and levels based on the user's geographic location 2. The system of claim 1 .

10. The reception unit is Analyze users' social media activity and suggest relevant learning content 2. The system of claim 1 .

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