system

Abstract

The system according to the embodiment aims to help foreigners acquire natural Japanese language in a comprehensive manner. [Solution] The system according to the embodiment comprises a reading unit, an explanation unit, an example unit, a correction unit, and a conversation example creation unit. The reading unit reads the text aloud. The explanation unit explains the meaning based on the text read aloud by the reading unit. The example unit teaches natural usage examples based on the meaning explained by the explanation unit. The correction unit corrects the text entered or spoken by the user into natural Japanese expressions based on the usage examples taught by the example unit. The conversation example creation unit creates natural conversation examples based on the user's app usage history and the content corrected by the correction unit, and presents them as questions.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not provide sufficient comprehensive learning support for foreigners to acquire natural Japanese, and there is room for improvement.

[0005] The system according to the embodiment aims to help foreigners acquire natural Japanese language in a comprehensive manner. [Means for solving the problem]

[0006] The system according to this embodiment comprises a reading unit, an explanation unit, an example unit, a correction unit, and a conversation example creation unit. The reading unit reads the text aloud. The explanation unit explains the meaning based on the text read aloud by the reading unit. The example unit teaches natural usage examples based on the meaning explained by the explanation unit. The correction unit corrects the text entered or spoken by the user into natural Japanese expressions based on the usage examples taught by the example unit. The conversation example creation unit creates natural conversation examples based on the user's app usage history and the content corrected by the correction unit, and presents them as questions. [Effects of the Invention]

[0007] The system according to the embodiment allows foreigners to comprehensively learn natural Japanese. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A Japanese language learning support system according to an embodiment of the present invention allows users to efficiently learn Japanese and acquire natural expressions. When a user scans a document or other text, the system reads the text aloud, explains its meaning, and provides examples of fluent usage. It also has a function for correcting user-entered text and spoken content (voice input) to improve fluent Japanese expressions. Furthermore, it can create and present fluent conversation examples based on the user's app usage history. The system is programmed to memorize the user's Japanese language learning trends and incorporate them into usage examples and questions. The more the system is used, the more it learns and masters content suited to the user's everyday environment. For example, when a user scans a document or other text, the system reads the text aloud. The system supports various types of text, such as business documents and news articles. The system then explains the meaning of the text. For example, it explains difficult words and expressions in simple terms. It also provides examples of fluent usage of the text. For example, it shows how the same expression is used in everyday conversation. Furthermore, when a user inputs sentences or spoken content (voice input) into the system, the system corrects them to improve fluent Japanese expressions. For example, it corrects grammatically correct but unnatural expressions to make them more natural. The system can also create and present fluent conversation examples tailored to the user based on the user's app usage history. For example, it presents conversation examples that include phrases and words frequently used by the user. The system memorizes the user's tendencies in learning Japanese and reflects this in the usage examples and questions. For example, it focuses on grammar and expressions that the user often makes mistakes in. The system is also programmed to repeatedly study and acquire content that is suited to the user's everyday environment. For example, it focuses on expressions commonly used in business situations and phrases frequently used in everyday conversation. In this way, the present invention is a system that supports users in efficiently learning Japanese and acquiring natural expressions. This Japanese language learning support system allows users to efficiently learn Japanese and acquire natural expressions.

[0029] A Japanese language learning support system according to an embodiment includes a reading unit, an explanation unit, an example unit, a correction unit, and a conversation example creation unit. The reading unit reads aloud a document or other text when the user scans it. For example, the system supports various types of text, such as business documents and news articles. The explanation unit explains the meaning of the text that has been read aloud. For example, it explains difficult words and expressions in simple terms. The example unit provides examples of natural usage based on the explained meaning. For example, it shows how the same expression is used in everyday conversation. The correction unit corrects sentences entered or spoken by the user to create natural Japanese expressions. For example, it corrects grammatically correct but unnatural expressions to make them more natural. The conversation example creation unit creates and presents natural conversation examples based on the user's app usage history. For example, it presents conversation examples that include phrases and words frequently used by the user. This allows the Japanese language learning support system according to an embodiment to enable the user to efficiently learn Japanese and acquire natural expressions.

[0030] The learning tendency recording unit is provided, and the learning tendency recording unit records the user's learning tendency based on the content corrected by the correction unit. The learning tendency recording unit records the user's learning tendency based on the content corrected by the correction unit. For example, it records grammar and expressions that the user often makes mistakes in and allows the user to focus on learning those. The learning tendency recording unit can also record the user's learning progress and evaluate the effectiveness of the learning. For example, it records how often the user is studying and what content the user is studying. This allows the learning tendency recording unit to grasp the user's learning tendency in detail and provide more effective learning support.

[0031] The system is equipped with an environment adaptation unit, which provides content tailored to the user's daily environment based on conversation examples created by the conversation example creation unit. For example, it can focus on teaching expressions frequently used in business settings or phrases commonly used in everyday conversation. Furthermore, the environment adaptation unit can provide optimal learning content based on the user's living environment and usage patterns. For instance, it can provide different content depending on whether the user is studying at home or at work. This allows the environment adaptation unit to provide content tailored to the user's daily environment, thereby improving learning effectiveness.

[0032] The text-to-speech unit can read aloud documents and other texts as the user reads them. It supports various types of text, such as business documents and news articles. The text-to-speech unit uses speech synthesis technology to read text in a natural voice. For example, it uses a speech synthesis engine to add appropriate intonation and accent according to the content of the text. Furthermore, the text-to-speech unit can adjust the reading speed and volume according to the user's preference. For example, if the user wants to read at a faster speed, the text-to-speech unit can increase the reading speed. This allows the text-to-speech unit to allow users to review documents and other texts aloud.

[0033] The commentary unit can explain the meaning of the spoken text. For example, it can explain difficult words and expressions in simple terms. The commentary unit can use natural language processing technology to analyze the meaning of the text and provide appropriate explanations. For example, it can retrieve the meanings of words and phrases in the text from a dictionary database and explain them clearly to the user. Furthermore, the commentary unit can provide appropriate explanations based on the context of the text. For example, it can grasp the meaning of the entire text and provide explanations appropriate to the context. This makes it easier for the user to understand the meaning of the text.

[0034] The example unit can teach natural usage examples based on the explained meaning. The example unit teaches natural usage examples based on the explained meaning. For example, it shows how the same expression is used in everyday conversation. The example unit can generate appropriate usage examples using natural language generation technology. For example, the example unit retrieves how to use words and phrases in sentences from a learning database and shows them to the user in an easy-to-understand manner. The example unit can also provide appropriate usage examples depending on the user's learning status. For example, the example unit focuses on showing usage examples of expressions that users often make mistakes with. In this way, the example unit enables the user to learn natural Japanese expressions.

[0035] The correction unit can correct sentences entered by the user or spoken content to make them more fluent in Japanese. The correction unit corrects sentences entered by the user or spoken content to make them more fluent in Japanese. For example, it corrects grammatically correct but unnatural expressions to make them more natural. The correction unit can use natural language processing technology to analyze the grammar and expressions of the sentences and make appropriate corrections. For example, the correction unit detects grammatical errors in the sentences and corrects them to correct the expressions. The correction unit can also correct them to appropriate expressions based on the context of the sentences. For example, the correction unit grasps the meaning of the entire sentence and makes corrections according to the context. In this way, the correction unit enables the user to acquire natural Japanese expressions.

[0036] The conversation example generation unit can create and present natural conversation examples based on the user's app usage history. For example, it can present conversation examples that include phrases and words frequently used by the user. The conversation example generation unit can generate appropriate conversation examples using natural language generation technology. For example, it can analyze the user's app usage history and create conversation examples based on expressions and phrases frequently used by the user. Furthermore, the conversation example generation unit can provide appropriate conversation examples according to the user's learning progress. For example, it can focus on presenting conversation examples for expressions that the user frequently misuses. This allows the conversation example generation unit to learn conversation examples based on actual usage situations.

[0037] The text-to-speech unit can change which parts of the text are emphasized based on their importance during reading. For example, it can emphasize important keywords or phrases. It can also emphasize the conclusion of a text. Furthermore, it can emphasize specific data or numbers. By emphasizing important parts, the text-to-speech unit makes it easier for users to grasp important information. Some or all of the above processing in the text-to-speech unit may be performed using AI, for example, or not. For example, the text-to-speech unit can input the importance of the text into the AI ​​and have the AI ​​select the parts to emphasize.

[0038] The text-to-speech unit can apply different reading styles depending on the category of the text during reading. For example, it can read business documents in a formal tone, everyday conversations in a casual tone, and news articles in an objective tone. This allows the text-to-speech unit to read appropriately according to the category of the text. Some or all of the above processing in the text-to-speech unit may be performed using AI, for example, or without AI. For example, the text-to-speech unit can input the category of the text into the AI ​​and have the AI ​​select an appropriate reading style.

[0039] The text-to-speech unit can use region-specific pronunciations and expressions while reading aloud, taking into account the user's geographical location. For example, it can read aloud in the Kansai dialect to users in the Kansai region, in the Tohoku dialect to users in the Tohoku region, and in the Okinawa dialect to users in the Okinawa region. This allows the text-to-speech unit to use region-specific pronunciations and expressions, making the reading more familiar to the user. Some or all of the above processing in the text-to-speech unit may be performed using AI, for example, or without AI. For example, the text-to-speech unit can input the user's geographical location information into the AI ​​and have the AI ​​select region-specific pronunciations and expressions.

[0040] The reading unit can analyze the user's social media activity and prioritize reading out related content when reading out. The reading unit can analyze the user's social media activity and prioritize reading out related content when reading out. For example, the reading unit can prioritize reading out sentences related to topics that the user has recently been interested in. The reading unit can also prioritize reading out posts from accounts the user follows. The reading unit can also prioritize reading out sentences related to topics in groups the user participates in. This allows the reading unit to prioritize reading out content based on the user's interests. Some or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input the user's social media activity data into AI and have the AI ​​select related content.

[0041] The commentary unit can change the method of explanation based on the difficulty of the text when providing an explanation. The commentary unit can change the method of explanation based on the difficulty of the text when providing an explanation. For example, if the text is difficult, the commentary unit can use simple language. For example, if the text is simple, detailed background information can be provided. For example, if the text is of medium difficulty, the commentary unit can provide an appropriate level of detail. This enables the commentary unit to provide an appropriate explanation according to the difficulty of the text. Some or all of the above-mentioned processing in the commentary unit may be performed using AI, for example, or may be performed without using AI. For example, the commentary unit can input the difficulty of the text into AI and have the AI ​​select an appropriate explanation method.

[0042] The commentary unit can apply different commentary methods depending on the category of the text when providing commentary. The commentary unit applies different commentary methods depending on the category of the text when providing commentary. For example, in the case of a business document, a formal commentary can be provided. In the case of everyday conversation, a casual commentary can be provided. In the case of a news article, an objective commentary can be provided. This enables the commentary unit to provide an appropriate commentary depending on the category of the text. Some or all of the above-mentioned processing in the commentary unit may be performed using, for example, AI, or may be performed without using AI. For example, the commentary unit can input the category of the text into AI and have the AI ​​select an appropriate commentary method.

[0043] The commentary unit can select the optimal explanation method by referring to the user's past learning history when providing an explanation. The commentary unit selects the optimal explanation method by referring to the user's past learning history when providing an explanation. For example, the commentary unit may prioritize explanation methods that the user found easy to understand in the past. It may also avoid explanation methods that the user found difficult in the past. It may also suggest the optimal explanation method based on the user's past learning history. This enables the commentary unit to provide the optimal explanation based on the user's past learning history. Some or all of the above-described processing in the commentary unit may be performed using, for example, AI, or may be performed without using AI. For example, the commentary unit may input the user's past learning history data into AI and have the AI ​​select the optimal explanation method.

[0044] The commentary section can add relevant information based on the user's interests during commentary. For example, it can add information related to topics the user is interested in, or it can add posts from accounts the user follows, or it can add information related to topics in groups the user participates in. This allows the commentary section to provide relevant information based on the user's interests. Some or all of the above processing in the commentary section may be performed using AI, for example, or not. For example, the commentary section can input user interest data into AI and have the AI ​​select relevant information.

[0045] The example section can adjust the level of detail of the examples based on the frequency of use of the text. For example, it can provide detailed examples for frequently used texts, concise examples for less frequently used texts, and examples with a moderate level of detail for moderately frequently used texts. This allows the example section to provide examples with an appropriate level of detail according to the frequency of use. Some or all of the above processing in the example section may be performed using AI, for example, or without AI. For example, the example section can input text frequency data into AI and have AI select an appropriate level of detail.

[0046] The exemplification unit can apply different exemplification techniques depending on the category of the text when providing an exemplification. The exemplification unit can apply different exemplification techniques depending on the category of the text when providing an exemplification. For example, in the case of a business document, formal exemplifications can be provided. In the case of everyday conversation, casual exemplifications can be provided. In the case of a news article, objective exemplifications can be provided. This enables the exemplification unit to provide appropriate exemplifications depending on the category of the text. Some or all of the above-mentioned processing in the exemplification unit may be performed using, or without, AI, for example. For example, the exemplification unit can input text category data into AI and have the AI ​​select an appropriate exemplification technique.

[0047] The exemplification unit can select the optimal exemplification method by referring to the user's past use examples when providing an exemplification. The exemplification unit selects the optimal exemplification method by referring to the user's past use examples when providing an exemplification. For example, the exemplification unit can preferentially use exemplification methods that the user found easy to understand in the past. It can also avoid exemplification methods that the user found difficult in the past. It can also suggest the optimal exemplification method from the user's past use examples. This enables the exemplification unit to provide optimal exemplification based on the user's past use examples. Some or all of the above-described processing in the exemplification unit may be performed using, for example, AI, or may be performed without using AI. For example, the exemplification unit can input the user's past use example data into AI and have the AI ​​select the optimal exemplification method.

[0048] The exemplification unit can add related examples based on the user's interests when providing examples. The exemplification unit can add related examples based on the user's interests when providing examples. For example, the exemplification unit can add examples related to topics in which the user is interested. It can also add posts from accounts the user follows. It can also add examples related to topics in groups the user participates in. This allows the exemplification unit to provide related examples based on the user's interests. Some or all of the above-described processing in the exemplification unit may be performed using, or without, AI, for example. For example, the exemplification unit can input user interest data into AI and have the AI ​​select related examples.

[0049] The correction unit can adjust the level of detail of the corrections based on the importance of the sentence when correcting. The correction unit can adjust the level of detail of the corrections based on the importance of the sentence when correcting. For example, for important sentences, detailed corrections can be provided. For sentences of low importance, brief corrections can be provided. For sentences of medium importance, corrections can be made with an appropriate level of detail. This allows the correction unit to provide corrections with an appropriate level of detail according to the importance. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can input sentence importance data into AI and have the AI ​​select an appropriate level of detail.

[0050] The correction unit can apply different correction methods depending on the category of the text when correcting. The correction unit can apply different correction methods depending on the category of the text when correcting. For example, in the case of a business document, formal corrections are provided. In addition, in the case of everyday conversation, casual corrections can be provided. In addition, in the case of a news article, objective corrections can be provided. This enables the correction unit to make appropriate corrections depending on the category of the text. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can input text category data into AI and have the AI ​​select an appropriate correction method.

[0051] When correcting, the correction unit can select the optimal correction method by referring to the user's past learning history. When correcting, the correction unit selects the optimal correction method by referring to the user's past learning history. For example, the correction unit may prioritize correction methods that the user found easy to understand in the past. It may also be possible to avoid correction methods that the user found difficult in the past. It may also be possible to suggest the optimal correction method based on the user's past learning history. This enables the correction unit to perform optimal correction based on the user's past learning history. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit may input the user's past learning history data into AI and have the AI ​​select the optimal correction method.

[0052] The correction unit can add related information based on the user's interests when correcting. The correction unit can add related information based on the user's interests when correcting. For example, it can add information related to topics in which the user is interested. It can also add content posted by accounts the user follows. It can also add information related to topics in groups the user participates in. This allows the correction unit to provide related information based on the user's interests. Some or all of the above-mentioned processing in the correction unit can be performed using, for example, AI, or can be performed without using AI. For example, the correction unit can input the user's interest data into AI and have the AI ​​select related information.

[0053] When creating a conversation example, the conversation example creation unit can create an optimal conversation example by referring to the user's past conversation history. When creating a conversation example, the conversation example creation unit creates an optimal conversation example by referring to the user's past conversation history. For example, the conversation example creation unit creates an optimal conversation example based on phrases used by the user in the past. The conversation example creation unit can also create a conversation example including frequently used expressions from the user's past conversation history. The conversation example creation unit can also analyze the user's past conversation history to create the most effective conversation example. This enables the conversation example creation unit to create an optimal conversation example based on the user's past conversation history. Some or all of the above-mentioned processing in the conversation example creation unit may be performed using, or without, AI, for example. For example, the conversation example creation unit can input the user's past conversation history data into AI and have the AI ​​select an optimal conversation example.

[0054] The conversation example creation unit can add related conversation examples based on the user's interests when creating a conversation example. The conversation example creation unit adds related conversation examples based on the user's interests when creating a conversation example. For example, conversation examples related to topics in which the user is interested can be added. Conversation examples can also be created based on the content posted by accounts the user follows. Conversation examples related to topics in groups the user participates in can also be added. This allows the conversation example creation unit to provide related conversation examples based on the user's interests. Some or all of the above-described processing in the conversation example creation unit may be performed using AI, for example, or may be performed without using AI. For example, the conversation example creation unit can input user interest data into AI and cause the AI ​​to select related conversation examples.

[0055] When creating conversation examples, the conversation example creation unit can create region-specific conversation examples taking into account the user's geographical location information. When creating conversation examples, the conversation example creation unit creates region-specific conversation examples taking into account the user's geographical location information. For example, conversation examples using the Kansai dialect can be created for a user in the Kansai region. Conversation examples using the Tohoku dialect can also be created for a user in the Tohoku region. Conversation examples using the Okinawa dialect can also be created for a user in the Okinawa region. In this way, the conversation example creation unit can provide region-specific conversation examples that are familiar to the user. Some or all of the above-described processing in the conversation example creation unit may be performed using AI, for example, or may be performed without using AI. For example, the conversation example creation unit can input the user's geographical location information to AI and cause the AI ​​to select region-specific conversation examples.

[0056] When creating conversation examples, the conversation example creation unit can analyze the user's social media activity and prioritize creating related conversation examples. When creating conversation examples, the conversation example creation unit analyzes the user's social media activity and prioritize creating related conversation examples. For example, the conversation example creation unit prioritizes creating conversation examples related to topics that the user has recently been interested in. Conversation examples can also be created based on the content posted by accounts the user follows. Conversation examples can also be prioritized to create conversation examples related to topics in groups the user participates in. This allows the conversation example creation unit to prioritize providing conversation examples based on the user's interests. Some or all of the above-mentioned processing in the conversation example creation unit may be performed using, or without, AI, for example. For example, the conversation example creation unit can input the user's social media activity data into AI and cause the AI ​​to select related conversation examples.

[0057] When recording learning tendencies, the learning tendency recording unit can select the optimal recording method by referring to the user's past learning history. When recording learning tendencies, the learning tendency recording unit selects the optimal recording method by referring to the user's past learning history. For example, the unit can prioritize recording methods that the user found easy to understand in the past. It can also avoid recording methods that the user found difficult in the past. It can also suggest the optimal recording method based on the user's past learning history. This enables the learning tendency recording unit to select the optimal recording method based on the user's past learning history. Some or all of the above-mentioned processing in the learning tendency recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning tendency recording unit can input the user's past learning history data into AI and have the AI ​​select the optimal recording method.

[0058] The learning tendency recording unit can add related information based on the user's interests when recording the learning tendency. The learning tendency recording unit can add related information based on the user's interests when recording the learning tendency. For example, information related to topics in which the user is interested can be added. Posts from accounts the user follows can also be added. Information related to topics in groups the user participates in can also be added. This allows the learning tendency recording unit to record related information based on the user's interests. Some or all of the above-described processing in the learning tendency recording unit can be performed using, or without, AI, for example. For example, the learning tendency recording unit can input the user's interest data into AI and have the AI ​​select related information.

[0059] The environment adaptation unit can select the optimal adaptation method by referring to the user's past usage history during environment adaptation. The environment adaptation unit selects the optimal adaptation method by referring to the user's past usage history during environment adaptation. For example, the environment adaptation unit can preferentially use environment adaptation methods that the user has used in the past. It can also avoid environment adaptation methods that the user has had difficulty with in the past. It can also suggest the optimal environment adaptation method based on the user's past usage history. This enables the environment adaptation unit to perform optimal environment adaptation based on the user's past usage history. Some or all of the above-mentioned processing in the environment adaptation unit may be performed using, for example, AI, or may be performed without using AI. For example, the environment adaptation unit can input the user's past usage history data into AI and have the AI ​​select the optimal adaptation method.

[0060] The environment adaptation unit can select the optimal adaptation method in consideration of the user's geographical location information during environment adaptation. The environment adaptation unit selects the optimal adaptation method in consideration of the user's geographical location information during environment adaptation. For example, if the user is in an urban area, city-specific environment adaptation can be provided. Furthermore, if the user is in the suburbs, suburb-specific environment adaptation can be provided. Furthermore, if the user is traveling, destination-specific environment adaptation can be provided. This enables the environment adaptation unit to perform optimal environment adaptation based on the user's geographical location information. Some or all of the above-described processing in the environment adaptation unit may be performed using, for example, AI, or may be performed without using AI. For example, the environment adaptation unit can input the user's geographical location information into AI and have the AI ​​select the optimal adaptation method.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The Japanese language learning support system can also be equipped with customization functions according to the user's learning style. For example, for users who prefer visual learning, explanations that make extensive use of diagrams and graphs can be provided. For users who prefer auditory learning, audio commentary or podcast-style learning materials can be provided. Furthermore, for users who prefer tactile learning, interactive quizzes or game-style learning materials can be provided. This makes it possible to provide the optimal learning experience according to the user's learning style.

[0063] The learning trend recording unit can have a dashboard function that visualizes the user's learning progress. For example, it can display how often the user is studying and what content they are studying in graphs and charts. It can also display the goals and progress the user has achieved within a specific period. It can also provide a reminder function for the user to review content they have studied in the past. This allows the user to understand their learning status at a glance and create an effective study plan.

[0064] The environment adaptation unit can propose a study schedule based on the user's lifestyle. For example, if the user is a morning person, it can send a notification encouraging them to study in the morning. Alternatively, if the user is a night owl, it can send a notification encouraging them to study in the evening. Furthermore, if the user has a habit of studying on a specific day of the week or at a specific time, it can provide study content tailored to that time. This makes it possible to provide effective study support that matches the user's lifestyle.

[0065] The reading unit can have a function to evaluate the user's pronunciation and provide feedback. For example, when the user pronounces a specific word or phrase, the pronunciation is recorded and compared with the correct pronunciation for evaluation. It can also specifically point out areas for improvement in pronunciation and suggest practice methods. Furthermore, it can visualize how the user's pronunciation accuracy improves as they repeatedly practice pronunciation. This allows the user to objectively evaluate their own pronunciation and effectively improve it.

[0066] The explanation unit can provide individually customized explanations based on the user's learning history. For example, it can adjust the content and method of explanations taking into account what the user has learned in the past and points where they often make mistakes. It can also prioritize explanations related to topics in which the user is particularly interested. Furthermore, it can adjust the level of detail and difficulty of the explanations according to the user's level of understanding. This allows users to receive explanations that are best suited to them and progress with their studies efficiently.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The text-to-speech unit reads aloud documents and other texts selected by the user. It supports various types of text, such as business documents and news articles. Step 2: The explanation section explains the meaning of the text that was read aloud. For example, it explains difficult words and expressions in simple terms. Step 3: The example section teaches natural usage examples based on the explained meaning. For example, it shows how to use the same expression in everyday conversation. Step 4: The correction unit corrects the sentences entered or spoken by the user to make them sound more natural in Japanese. For example, it corrects grammatically correct but unnatural expressions to make them sound more natural. Step 5: The conversation example creation unit creates and presents conversation examples based on the user's app usage history. For example, it presents conversation examples that include phrases and words that the user frequently uses.

[0069] (Example 2) A Japanese language learning support system according to an embodiment of the present invention allows users to efficiently learn Japanese and acquire natural expressions. When a user scans a document or other text, the system reads the text aloud, explains its meaning, and provides examples of fluent usage. It also has a function for correcting user-entered text and spoken content (voice input) to improve fluent Japanese expressions. Furthermore, it can create and present fluent conversation examples based on the user's app usage history. The system is programmed to memorize the user's Japanese language learning trends and incorporate them into usage examples and questions. The more the system is used, the more it learns and masters content suited to the user's everyday environment. For example, when a user scans a document or other text, the system reads the text aloud. The system supports various types of text, such as business documents and news articles. The system then explains the meaning of the text. For example, it explains difficult words and expressions in simple terms. It also provides examples of fluent usage of the text. For example, it shows how the same expression is used in everyday conversation. Furthermore, when a user inputs sentences or spoken content (voice input) into the system, the system corrects them to improve fluent Japanese expressions. For example, it corrects grammatically correct but unnatural expressions to make them more natural. The system can also create and present fluent conversation examples tailored to the user based on the user's app usage history. For example, it presents conversation examples that include phrases and words frequently used by the user. The system memorizes the user's tendencies in learning Japanese and reflects this in the usage examples and questions. For example, it focuses on grammar and expressions that the user often makes mistakes in. The system is also programmed to repeatedly study and acquire content that is suited to the user's everyday environment. For example, it focuses on expressions commonly used in business situations and phrases frequently used in everyday conversation. In this way, the present invention is a system that supports users in efficiently learning Japanese and acquiring natural expressions. This Japanese language learning support system allows users to efficiently learn Japanese and acquire natural expressions.

[0070] A Japanese language learning support system according to an embodiment includes a reading unit, an explanation unit, an example unit, a correction unit, and a conversation example creation unit. The reading unit reads aloud a document or other text when the user scans it. For example, the system supports various types of text, such as business documents and news articles. The explanation unit explains the meaning of the text that has been read aloud. For example, it explains difficult words and expressions in simple terms. The example unit provides examples of natural usage based on the explained meaning. For example, it shows how the same expression is used in everyday conversation. The correction unit corrects sentences entered or spoken by the user to create natural Japanese expressions. For example, it corrects grammatically correct but unnatural expressions to make them more natural. The conversation example creation unit creates and presents natural conversation examples based on the user's app usage history. For example, it presents conversation examples that include phrases and words frequently used by the user. This allows the Japanese language learning support system according to an embodiment to enable the user to efficiently learn Japanese and acquire natural expressions.

[0071] The learning tendency recording unit is provided, and the learning tendency recording unit records the user's learning tendency based on the content corrected by the correction unit. The learning tendency recording unit records the user's learning tendency based on the content corrected by the correction unit. For example, it records grammar and expressions that the user often makes mistakes in and allows the user to focus on learning those. The learning tendency recording unit can also record the user's learning progress and evaluate the effectiveness of the learning. For example, it records how often the user is studying and what content the user is studying. This allows the learning tendency recording unit to grasp the user's learning tendency in detail and provide more effective learning support.

[0072] The system is equipped with an environment adaptation unit, which provides content tailored to the user's daily environment based on conversation examples created by the conversation example creation unit. For example, it can focus on teaching expressions frequently used in business settings or phrases commonly used in everyday conversation. Furthermore, the environment adaptation unit can provide optimal learning content based on the user's living environment and usage patterns. For instance, it can provide different content depending on whether the user is studying at home or at work. This allows the environment adaptation unit to provide content tailored to the user's daily environment, thereby improving learning effectiveness.

[0073] The text-to-speech unit can read aloud documents and other texts as the user reads them. It supports various types of text, such as business documents and news articles. The text-to-speech unit uses speech synthesis technology to read text in a natural voice. For example, it uses a speech synthesis engine to add appropriate intonation and accent according to the content of the text. Furthermore, the text-to-speech unit can adjust the reading speed and volume according to the user's preference. For example, if the user wants to read at a faster speed, the text-to-speech unit can increase the reading speed. This allows the text-to-speech unit to allow users to review documents and other texts aloud.

[0074] The commentary unit can explain the meaning of the spoken text. For example, it can explain difficult words and expressions in simple terms. The commentary unit can use natural language processing technology to analyze the meaning of the text and provide appropriate explanations. For example, it can retrieve the meanings of words and phrases in the text from a dictionary database and explain them clearly to the user. Furthermore, the commentary unit can provide appropriate explanations based on the context of the text. For example, it can grasp the meaning of the entire text and provide explanations appropriate to the context. This makes it easier for the user to understand the meaning of the text.

[0075] The example unit can teach natural usage examples based on the explained meaning. The example unit teaches natural usage examples based on the explained meaning. For example, it shows how the same expression is used in everyday conversation. The example unit can generate appropriate usage examples using natural language generation technology. For example, the example unit retrieves how to use words and phrases in sentences from a learning database and shows them to the user in an easy-to-understand manner. The example unit can also provide appropriate usage examples depending on the user's learning status. For example, the example unit focuses on showing usage examples of expressions that users often make mistakes with. In this way, the example unit enables the user to learn natural Japanese expressions.

[0076] The correction unit can correct sentences entered by the user or spoken content to make them more fluent in Japanese. The correction unit corrects sentences entered by the user or spoken content to make them more fluent in Japanese. For example, it corrects grammatically correct but unnatural expressions to make them more natural. The correction unit can use natural language processing technology to analyze the grammar and expressions of the sentences and make appropriate corrections. For example, the correction unit detects grammatical errors in the sentences and corrects them to correct the expressions. The correction unit can also correct them to appropriate expressions based on the context of the sentences. For example, the correction unit grasps the meaning of the entire sentence and makes corrections according to the context. In this way, the correction unit enables the user to acquire natural Japanese expressions.

[0077] The conversation example generation unit can create and present natural conversation examples based on the user's app usage history. For example, it can present conversation examples that include phrases and words frequently used by the user. The conversation example generation unit can generate appropriate conversation examples using natural language generation technology. For example, it can analyze the user's app usage history and create conversation examples based on expressions and phrases frequently used by the user. Furthermore, the conversation example generation unit can provide appropriate conversation examples according to the user's learning progress. For example, it can focus on presenting conversation examples for expressions that the user frequently misuses. This allows the conversation example generation unit to learn conversation examples based on actual usage situations.

[0078] The reading unit can estimate the user's emotions and adjust the reading speed and tone based on the estimated user emotions. The reading unit can estimate the user's emotions and adjust the reading speed and tone based on the estimated user emotions. For example, if the user is relaxed, the reading unit can read at a relaxed speed and in a gentle tone. If the user is in a hurry, the reading unit can read at a fast speed and in a concise tone. If the user is excited, the reading unit can read in a lively tone. This enables the reading unit to read according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reading unit can be performed using, for example, AI, or without AI. For example, the reading unit can input user emotion data into the generation AI and cause the generation AI to adjust the reading speed and tone based on the emotion.

[0079] The text-to-speech unit can change which parts of the text are emphasized based on their importance during reading. For example, it can emphasize important keywords or phrases. It can also emphasize the conclusion of a text. Furthermore, it can emphasize specific data or numbers. By emphasizing important parts, the text-to-speech unit makes it easier for users to grasp important information. Some or all of the above processing in the text-to-speech unit may be performed using AI, for example, or not. For example, the text-to-speech unit can input the importance of the text into the AI ​​and have the AI ​​select the parts to emphasize.

[0080] The text-to-speech unit can apply different reading styles depending on the category of the text during reading. For example, it can read business documents in a formal tone, everyday conversations in a casual tone, and news articles in an objective tone. This allows the text-to-speech unit to read appropriately according to the category of the text. Some or all of the above processing in the text-to-speech unit may be performed using AI, for example, or without AI. For example, the text-to-speech unit can input the category of the text into the AI ​​and have the AI ​​select an appropriate reading style.

[0081] The reading unit can estimate the user's emotions and adjust the order of the content to be read based on the estimated user emotions. The reading unit can estimate the user's emotions and adjust the order of the content to be read based on the estimated user emotions. For example, if the user is stressed, important information can be read first. If the user is relaxed, the content can be read in order. If the user is in a hurry, the main points can be read first. This allows the reading unit to read the content in an order that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reading unit can be performed using, for example, AI, or without AI. For example, the reading unit can input user emotion data into the generation AI and cause the generation AI to adjust the order of the content based on the emotion.

[0082] The text-to-speech unit can use region-specific pronunciations and expressions while reading aloud, taking into account the user's geographical location. For example, it can read aloud in the Kansai dialect to users in the Kansai region, in the Tohoku dialect to users in the Tohoku region, and in the Okinawa dialect to users in the Okinawa region. This allows the text-to-speech unit to use region-specific pronunciations and expressions, making the reading more familiar to the user. Some or all of the above processing in the text-to-speech unit may be performed using AI, for example, or without AI. For example, the text-to-speech unit can input the user's geographical location information into the AI ​​and have the AI ​​select region-specific pronunciations and expressions.

[0083] The reading unit can analyze the user's social media activity and prioritize reading out related content when reading out. The reading unit can analyze the user's social media activity and prioritize reading out related content when reading out. For example, the reading unit can prioritize reading out sentences related to topics that the user has recently been interested in. The reading unit can also prioritize reading out posts from accounts the user follows. The reading unit can also prioritize reading out sentences related to topics in groups the user participates in. This allows the reading unit to prioritize reading out content based on the user's interests. Some or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input the user's social media activity data into AI and have the AI ​​select related content.

[0084] The commentary unit can estimate the user's emotions and adjust the level of detail of the commentary based on the estimated user's emotions. The commentary unit can estimate the user's emotions and adjust the level of detail of the commentary based on the estimated user's emotions. For example, if the user is relaxed, the commentary unit can provide detailed commentary. If the user is in a hurry, the commentary unit can provide concise commentary. If the user is excited, the commentary unit can provide commentary with a level of detail that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the commentary unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the commentary unit can input user's emotion data into the generation AI and cause the generation AI to adjust the level of detail of the commentary based on the emotion.

[0085] The commentary unit can change the method of explanation based on the difficulty of the text when providing an explanation. The commentary unit can change the method of explanation based on the difficulty of the text when providing an explanation. For example, if the text is difficult, the commentary unit can use simple language. For example, if the text is simple, detailed background information can be provided. For example, if the text is of medium difficulty, the commentary unit can provide an appropriate level of detail. This enables the commentary unit to provide an appropriate explanation according to the difficulty of the text. Some or all of the above-mentioned processing in the commentary unit may be performed using AI, for example, or may be performed without using AI. For example, the commentary unit can input the difficulty of the text into AI and have the AI ​​select an appropriate explanation method.

[0086] The commentary unit can apply different commentary methods depending on the category of the text when providing commentary. The commentary unit applies different commentary methods depending on the category of the text when providing commentary. For example, in the case of a business document, a formal commentary can be provided. In the case of everyday conversation, a casual commentary can be provided. In the case of a news article, an objective commentary can be provided. This enables the commentary unit to provide an appropriate commentary depending on the category of the text. Some or all of the above-mentioned processing in the commentary unit may be performed using, for example, AI, or may be performed without using AI. For example, the commentary unit can input the category of the text into AI and have the AI ​​select an appropriate commentary method.

[0087] The commentary unit can estimate the user's emotions and adjust the order of commentary based on the estimated user emotions. The commentary unit can estimate the user's emotions and adjust the order of commentary based on the estimated user emotions. For example, if the user is stressed, important information can be explained first. If the user is relaxed, the commentary can be explained in order. If the user is in a hurry, the commentary unit can explain the main points first. This allows the commentary unit to provide commentary in an order that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the commentary unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the commentary unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the order of commentary based on the emotion.

[0088] The commentary unit can select the optimal explanation method by referring to the user's past learning history when providing an explanation. The commentary unit selects the optimal explanation method by referring to the user's past learning history when providing an explanation. For example, the commentary unit may prioritize explanation methods that the user found easy to understand in the past. It may also avoid explanation methods that the user found difficult in the past. It may also suggest the optimal explanation method based on the user's past learning history. This enables the commentary unit to provide the optimal explanation based on the user's past learning history. Some or all of the above-described processing in the commentary unit may be performed using, for example, AI, or may be performed without using AI. For example, the commentary unit may input the user's past learning history data into AI and have the AI ​​select the optimal explanation method.

[0089] The commentary section can add relevant information based on the user's interests during commentary. For example, it can add information related to topics the user is interested in, or it can add posts from accounts the user follows, or it can add information related to topics in groups the user participates in. This allows the commentary section to provide relevant information based on the user's interests. Some or all of the above processing in the commentary section may be performed using AI, for example, or not. For example, the commentary section can input user interest data into AI and have the AI ​​select relevant information.

[0090] The example unit can estimate the user's emotions and adjust the presentation of the examples based on the estimated emotions. For example, if the user is relaxed, the example will be presented in a relaxed manner. If the user is in a hurry, the example can be presented in a concise manner. If the user is excited, the example can be presented with visually stimulating effects. In this way, the example unit can provide examples in a manner that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the example unit may be performed using AI, or not using AI. For example, the example unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the presentation based on the emotion.

[0091] The example section can adjust the level of detail of the examples based on the frequency of use of the text. For example, it can provide detailed examples for frequently used texts, concise examples for less frequently used texts, and examples with a moderate level of detail for moderately frequently used texts. This allows the example section to provide examples with an appropriate level of detail according to the frequency of use. Some or all of the above processing in the example section may be performed using AI, for example, or without AI. For example, the example section can input text frequency data into AI and have AI select an appropriate level of detail.

[0092] The exemplification unit can apply different exemplification techniques depending on the category of the text when providing an exemplification. The exemplification unit can apply different exemplification techniques depending on the category of the text when providing an exemplification. For example, in the case of a business document, formal exemplifications can be provided. In the case of everyday conversation, casual exemplifications can be provided. In the case of a news article, objective exemplifications can be provided. This enables the exemplification unit to provide appropriate exemplifications depending on the category of the text. Some or all of the above-mentioned processing in the exemplification unit may be performed using, or without, AI, for example. For example, the exemplification unit can input text category data into AI and have the AI ​​select an appropriate exemplification technique.

[0093] The example section can estimate the user's emotions and adjust the order of examples based on the estimated emotions. For example, if the user is stressed, important information may be presented first. If the user is relaxed, examples may be presented in order. If the user is in a hurry, key points may be presented first. In this way, the example section can provide examples in an order that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the example section may be performed using AI, or not using AI. For example, the example section can input user emotion data into a generative AI and have the generative AI perform emotion-based order adjustments.

[0094] The exemplification unit can select the optimal exemplification method by referring to the user's past use examples when providing an exemplification. The exemplification unit selects the optimal exemplification method by referring to the user's past use examples when providing an exemplification. For example, the exemplification unit can preferentially use exemplification methods that the user found easy to understand in the past. It can also avoid exemplification methods that the user found difficult in the past. It can also suggest the optimal exemplification method from the user's past use examples. This enables the exemplification unit to provide optimal exemplification based on the user's past use examples. Some or all of the above-described processing in the exemplification unit may be performed using, for example, AI, or may be performed without using AI. For example, the exemplification unit can input the user's past use example data into AI and have the AI ​​select the optimal exemplification method.

[0095] The exemplification unit can add related examples based on the user's interests when providing examples. The exemplification unit can add related examples based on the user's interests when providing examples. For example, the exemplification unit can add examples related to topics in which the user is interested. It can also add posts from accounts the user follows. It can also add examples related to topics in groups the user participates in. This allows the exemplification unit to provide related examples based on the user's interests. Some or all of the above-described processing in the exemplification unit may be performed using, or without, AI, for example. For example, the exemplification unit can input user interest data into AI and have the AI ​​select related examples.

[0096] The editing unit can estimate the user's emotions and adjust the editing style based on those emotions. For example, if the user is relaxed, the editing unit will use a polite style. If the user is in a hurry, the editing unit can use a concise style. If the user is excited, the editing unit can use a style that incorporates visually stimulating effects. This allows the editing unit to provide editing in a style that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the editing unit may be performed using AI or not. For example, the editing unit can input user emotion data into a generative AI and have the generative AI adjust the style of expression based on the emotion.

[0097] The correction unit can adjust the level of detail of the corrections based on the importance of the sentence when correcting. The correction unit can adjust the level of detail of the corrections based on the importance of the sentence when correcting. For example, for important sentences, detailed corrections can be provided. For sentences of low importance, brief corrections can be provided. For sentences of medium importance, corrections can be made with an appropriate level of detail. This allows the correction unit to provide corrections with an appropriate level of detail according to the importance. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can input sentence importance data into AI and have the AI ​​select an appropriate level of detail.

[0098] The correction unit can apply different correction methods depending on the category of the text when correcting. The correction unit can apply different correction methods depending on the category of the text when correcting. For example, in the case of a business document, formal corrections are provided. In addition, in the case of everyday conversation, casual corrections can be provided. In addition, in the case of a news article, objective corrections can be provided. This enables the correction unit to make appropriate corrections depending on the category of the text. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can input text category data into AI and have the AI ​​select an appropriate correction method.

[0099] The editing unit can estimate the user's emotions and adjust the editing order based on the estimated emotions. For example, if the user is stressed, important information will be edited first. If the user is relaxed, editing can be done in order. If the user is in a hurry, key points can be edited first. This allows the editing unit to provide edits in an order that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editing unit may be performed using AI or not using AI. For example, the editing unit can input user emotion data into the generative AI and have the generative AI perform emotion-based order adjustments.

[0100] When correcting, the correction unit can select the optimal correction method by referring to the user's past learning history. When correcting, the correction unit selects the optimal correction method by referring to the user's past learning history. For example, the correction unit may prioritize correction methods that the user found easy to understand in the past. It may also be possible to avoid correction methods that the user found difficult in the past. It may also be possible to suggest the optimal correction method based on the user's past learning history. This enables the correction unit to perform optimal correction based on the user's past learning history. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit may input the user's past learning history data into AI and have the AI ​​select the optimal correction method.

[0101] The correction unit can add related information based on the user's interests when correcting. The correction unit can add related information based on the user's interests when correcting. For example, it can add information related to topics in which the user is interested. It can also add content posted by accounts the user follows. It can also add information related to topics in groups the user participates in. This allows the correction unit to provide related information based on the user's interests. Some or all of the above-mentioned processing in the correction unit can be performed using, for example, AI, or can be performed without using AI. For example, the correction unit can input the user's interest data into AI and have the AI ​​select related information.

[0102] The conversation example creation unit can estimate the user's emotions and adjust the expression method of the conversation example based on the estimated user's emotions. The conversation example creation unit can estimate the user's emotions and adjust the expression method of the conversation example based on the estimated user's emotions. For example, if the user is relaxed, the conversation example can be created using a relaxed expression method. If the user is in a hurry, the conversation example can be created using a concise expression method. If the user is excited, the conversation example can be created using an expression method that adds a visually stimulating effect. This allows the conversation example creation unit to provide conversation examples using an expression method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the conversation example creation unit may be performed using AI, for example, or without AI. For example, the conversation example creation unit can input user's emotion data into the generation AI and cause the generation AI to adjust the expression method based on the emotion.

[0103] When creating a conversation example, the conversation example creation unit can create an optimal conversation example by referring to the user's past conversation history. When creating a conversation example, the conversation example creation unit creates an optimal conversation example by referring to the user's past conversation history. For example, the conversation example creation unit creates an optimal conversation example based on phrases used by the user in the past. The conversation example creation unit can also create a conversation example including frequently used expressions from the user's past conversation history. The conversation example creation unit can also analyze the user's past conversation history to create the most effective conversation example. This enables the conversation example creation unit to create an optimal conversation example based on the user's past conversation history. Some or all of the above-mentioned processing in the conversation example creation unit may be performed using, or without, AI, for example. For example, the conversation example creation unit can input the user's past conversation history data into AI and have the AI ​​select an optimal conversation example.

[0104] The conversation example creation unit can add related conversation examples based on the user's interests when creating a conversation example. The conversation example creation unit adds related conversation examples based on the user's interests when creating a conversation example. For example, conversation examples related to topics in which the user is interested can be added. Conversation examples can also be created based on the content posted by accounts the user follows. Conversation examples related to topics in groups the user participates in can also be added. This allows the conversation example creation unit to provide related conversation examples based on the user's interests. Some or all of the above-described processing in the conversation example creation unit may be performed using AI, for example, or may be performed without using AI. For example, the conversation example creation unit can input user interest data into AI and cause the AI ​​to select related conversation examples.

[0105] The conversation example creation unit can estimate the user's emotions and adjust the order of conversation examples based on the estimated user emotions. The conversation example creation unit can estimate the user's emotions and adjust the order of conversation examples based on the estimated user emotions. For example, if the user is feeling stressed, important information can be presented first as a conversation example. Also, if the user is relaxed, the conversation examples can be presented in order. Also, if the user is in a hurry, the conversation example creation unit can present the conversation examples in the order that suits the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the conversation example creation unit can be performed using, for example, AI, or without AI. For example, the conversation example creation unit can input user emotion data into the generation AI and cause the generation AI to adjust the order based on the emotion.

[0106] When creating conversation examples, the conversation example creation unit can create region-specific conversation examples taking into account the user's geographical location information. When creating conversation examples, the conversation example creation unit creates region-specific conversation examples taking into account the user's geographical location information. For example, conversation examples using the Kansai dialect can be created for a user in the Kansai region. Conversation examples using the Tohoku dialect can also be created for a user in the Tohoku region. Conversation examples using the Okinawa dialect can also be created for a user in the Okinawa region. In this way, the conversation example creation unit can provide region-specific conversation examples that are familiar to the user. Some or all of the above-described processing in the conversation example creation unit may be performed using AI, for example, or may be performed without using AI. For example, the conversation example creation unit can input the user's geographical location information to AI and cause the AI ​​to select region-specific conversation examples.

[0107] When creating conversation examples, the conversation example creation unit can analyze the user's social media activity and prioritize creating related conversation examples. When creating conversation examples, the conversation example creation unit analyzes the user's social media activity and prioritize creating related conversation examples. For example, the conversation example creation unit prioritizes creating conversation examples related to topics that the user has recently been interested in. Conversation examples can also be created based on the content posted by accounts the user follows. Conversation examples can also be prioritized to create conversation examples related to topics in groups the user participates in. This allows the conversation example creation unit to prioritize providing conversation examples based on the user's interests. Some or all of the above-mentioned processing in the conversation example creation unit may be performed using, or without, AI, for example. For example, the conversation example creation unit can input the user's social media activity data into AI and cause the AI ​​to select related conversation examples.

[0108] The learning tendency recording unit can estimate the user's emotions and adjust the learning tendency recording method based on the estimated user's emotions. The learning tendency recording unit can estimate the user's emotions and adjust the learning tendency recording method based on the estimated user's emotions. For example, if the user is relaxed, detailed learning tendencies can be recorded. If the user is in a hurry, brief learning tendencies can be recorded. If the user is excited, learning tendencies can be recorded with visually stimulating effects. This allows the learning tendency recording unit to record learning tendencies using a recording method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the learning tendency recording unit can be performed using AI, for example, or without AI. For example, the learning tendency recording unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the recording method based on the emotion.

[0109] When recording learning tendencies, the learning tendency recording unit can select the optimal recording method by referring to the user's past learning history. When recording learning tendencies, the learning tendency recording unit selects the optimal recording method by referring to the user's past learning history. For example, the unit can prioritize recording methods that the user found easy to understand in the past. It can also avoid recording methods that the user found difficult in the past. It can also suggest the optimal recording method based on the user's past learning history. This enables the learning tendency recording unit to select the optimal recording method based on the user's past learning history. Some or all of the above-mentioned processing in the learning tendency recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning tendency recording unit can input the user's past learning history data into AI and have the AI ​​select the optimal recording method.

[0110] The learning tendency recording unit can estimate the user's emotions and adjust the recording frequency of the learning tendency based on the estimated user's emotions. The learning tendency recording unit can estimate the user's emotions and adjust the recording frequency of the learning tendency based on the estimated user's emotions. For example, if the user is relaxed, the learning tendency can be recorded frequently. If the user is in a hurry, the recording frequency can be reduced. If the user is excited, the recording can be performed with a visually stimulating effect. This allows the learning tendency recording unit to record the learning tendency at a frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the learning tendency recording unit can be performed using, for example, an AI, or without an AI. For example, the learning tendency recording unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the recording frequency based on the emotion.

[0111] The learning tendency recording unit can add related information based on the user's interests when recording the learning tendency. The learning tendency recording unit can add related information based on the user's interests when recording the learning tendency. For example, information related to topics in which the user is interested can be added. Posts from accounts the user follows can also be added. Information related to topics in groups the user participates in can also be added. This allows the learning tendency recording unit to record related information based on the user's interests. Some or all of the above-described processing in the learning tendency recording unit can be performed using, or without, AI, for example. For example, the learning tendency recording unit can input the user's interest data into AI and have the AI ​​select related information.

[0112] The environment adaptation unit can estimate the user's emotions and adjust the method of environment adaptation based on the estimated user emotions. The environment adaptation unit can estimate the user's emotions and adjust the method of environment adaptation based on the estimated user emotions. For example, if the user is relaxed, detailed environment adaptation can be provided. If the user is in a hurry, brief environment adaptation can be provided. If the user is excited, environment adaptation with visually stimulating effects can be provided. This allows the environment adaptation unit to provide environment adaptation in a manner appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the environment adaptation unit can be performed using AI, for example, or without AI. For example, the environment adaptation unit can input user emotion data into the generation AI and cause the generation AI to adjust the method based on the emotion.

[0113] The environment adaptation unit can select the optimal adaptation method by referring to the user's past usage history during environment adaptation. The environment adaptation unit selects the optimal adaptation method by referring to the user's past usage history during environment adaptation. For example, the environment adaptation unit can preferentially use environment adaptation methods that the user has used in the past. It can also avoid environment adaptation methods that the user has had difficulty with in the past. It can also suggest the optimal environment adaptation method based on the user's past usage history. This enables the environment adaptation unit to perform optimal environment adaptation based on the user's past usage history. Some or all of the above-mentioned processing in the environment adaptation unit may be performed using, for example, AI, or may be performed without using AI. For example, the environment adaptation unit can input the user's past usage history data into AI and have the AI ​​select the optimal adaptation method.

[0114] The environment adaptation unit can estimate the user's emotions and adjust the frequency of environment adaptation based on the estimated emotions. For example, if the user is relaxed, environment adaptation is performed more frequently. If the user is in a hurry, the frequency of environment adaptation can be reduced. If the user is excited, environment adaptation can be performed with visually stimulating effects. This allows the environment adaptation unit to provide environment adaptation at a frequency appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the environment adaptation unit may be performed using AI, for example, or without AI. For example, the environment adaptation unit can input user emotion data into a generative AI and have the generative AI perform emotion-based frequency adjustments.

[0115] The environment adaptation unit can select the optimal adaptation method in consideration of the user's geographical location information during environment adaptation. The environment adaptation unit selects the optimal adaptation method in consideration of the user's geographical location information during environment adaptation. For example, if the user is in an urban area, city-specific environment adaptation can be provided. Furthermore, if the user is in the suburbs, suburb-specific environment adaptation can be provided. Furthermore, if the user is traveling, destination-specific environment adaptation can be provided. This enables the environment adaptation unit to perform optimal environment adaptation based on the user's geographical location information. Some or all of the above-described processing in the environment adaptation unit may be performed using, for example, AI, or may be performed without using AI. For example, the environment adaptation unit can input the user's geographical location information into AI and have the AI ​​select the optimal adaptation method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reading unit, commentary unit, example unit, correction unit, conversation example creation unit, learning tendency recording unit, and environment adaptation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reading unit is realized by the output device 40 of the smart device 14 and the specific processing unit 290 of the data processing device 12. The commentary unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The example unit is realized, for example, by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing device 12. The correction unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The conversation example creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The learning tendency recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The environment adaptation unit is realized, for example, by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reading unit, commentary unit, example unit, correction unit, conversation example creation unit, learning tendency recording unit, and environment adaptation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reading unit is realized by the speaker 240 of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. The commentary unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The example unit is realized, for example, by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. The correction unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The conversation example creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The learning tendency recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The environment adaptation unit is realized, for example, by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reading unit, commentary unit, example unit, correction unit, conversation example creation unit, learning tendency recording unit, and environment adaptation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reading unit is realized by the speaker 240 of the headset type terminal 314 and the specific processing unit 290 of the data processing device 12. The commentary unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The example unit is realized, for example, by the control unit 46A of the headset type terminal 314 and the specific processing unit 290 of the data processing device 12. The correction unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The conversation example creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The learning tendency recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The environment adaptation unit is realized by, for example, the control unit 46A of the headset type terminal 314 and the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reading unit, commentary unit, example unit, correction unit, conversation example creation unit, learning tendency recording unit, and environment adaptation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reading unit is realized by the speaker 240 of the robot 414 and the specific processing unit 290 of the data processing device 12. The commentary unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The example unit is realized, for example, by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing device 12. The correction unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The conversation example creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The learning tendency recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The environment adaptation unit is realized, for example, by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing device 12.

[0116] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0117] The Japanese language learning support system can also be equipped with customization functions according to the user's learning style. For example, for users who prefer visual learning, explanations that make extensive use of diagrams and graphs can be provided. For users who prefer auditory learning, audio commentary or podcast-style learning materials can be provided. Furthermore, for users who prefer tactile learning, interactive quizzes or game-style learning materials can be provided. This makes it possible to provide the optimal learning experience according to the user's learning style.

[0118] The learning trend recording unit can have a dashboard function that visualizes the user's learning progress. For example, it can display how often the user is studying and what content they are studying in graphs and charts. It can also display the goals and progress the user has achieved within a specific period. It can also provide a reminder function for the user to review content they have studied in the past. This allows the user to understand their learning status at a glance and create an effective study plan.

[0119] The environment adaptation unit can propose a study schedule based on the user's lifestyle. For example, if the user is a morning person, it can send a notification encouraging them to study in the morning. Alternatively, if the user is a night owl, it can send a notification encouraging them to study in the evening. Furthermore, if the user has a habit of studying on a specific day of the week or at a specific time, it can provide study content tailored to that time. This makes it possible to provide effective study support that matches the user's lifestyle.

[0120] The reading unit can have a function to evaluate the user's pronunciation and provide feedback. For example, when the user pronounces a specific word or phrase, the pronunciation is recorded and compared with the correct pronunciation for evaluation. It can also specifically point out areas for improvement in pronunciation and suggest practice methods. Furthermore, it can visualize how the user's pronunciation accuracy improves as they repeatedly practice pronunciation. This allows the user to objectively evaluate their own pronunciation and effectively improve it.

[0121] The explanation unit can provide individually customized explanations based on the user's learning history. For example, it can adjust the content and method of explanations taking into account what the user has learned in the past and points where they often make mistakes. It can also prioritize explanations related to topics in which the user is particularly interested. Furthermore, it can adjust the level of detail and difficulty of the explanations according to the user's level of understanding. This allows users to receive explanations that are best suited to them and progress with their studies efficiently.

[0122] The example unit can estimate the user's emotions and adjust the content of the examples based on the estimated user's emotions. For example, if the user is relaxed, the example unit can provide humorous examples. If the user is stressed, the example unit can provide simple and easy-to-understand examples. Furthermore, if the user is excited, the example unit can provide visually stimulating examples. In this way, the example unit can provide optimal examples according to the user's emotions.

[0123] The correction unit can estimate the user's emotions and adjust the correction feedback method based on the estimated user's emotions. For example, if the user is relaxed, detailed feedback can be provided. If the user is in a hurry, brief feedback can be provided. Furthermore, if the user is excited, feedback with visually stimulating effects can be provided. In this way, the correction unit can provide optimal feedback according to the user's emotions.

[0124] The conversation example creation unit can estimate the user's emotions and adjust the topics of conversation examples based on the estimated user's emotions. For example, if the user is relaxed, conversation examples with a relaxed atmosphere can be provided. Also, if the user is feeling stressed, conversation examples that will help relieve stress can be provided. Furthermore, if the user is excited, conversation examples that will help share the excitement can be provided. In this way, the conversation example creation unit can provide optimal conversation examples according to the user's emotions.

[0125] The learning tendency recording unit can estimate the user's emotions and provide feedback to improve the user's motivation to learn based on the estimated user's emotions. For example, if the user is relaxed, positive feedback can be provided. If the user is feeling stressed, an encouraging message can be provided. Furthermore, if the user is excited, feedback that makes the user feel a sense of accomplishment can be provided. In this way, the learning tendency recording unit can provide optimal feedback according to the user's emotions and improve the user's motivation to learn.

[0126] The environmental adaptation unit can estimate the user's emotions and adjust the learning environment based on the estimated user's emotions. For example, if the user is relaxed, a quiet environment can be provided to encourage learning. If the user is feeling stressed, relaxing music can be provided. Furthermore, if the user is excited, environmental settings can be provided to increase concentration. In this way, the environmental adaptation unit can provide an optimal learning environment according to the user's emotions.

[0127] The processing flow of the second embodiment will be briefly explained below.

[0128] Step 1: The reading section reads out loud the text of a document or other document that the user has scanned. For example, it supports various types of text, such as business documents and news articles. Step 2: The explanation section explains the meaning of the text that has been read aloud. For example, it explains difficult words and expressions in simple terms. Step 3: The example section provides examples of practical usage based on the explained meaning, for example, showing how the same expression is used in everyday conversation. Step 4: The correction unit corrects the sentences entered or spoken by the user to make them sound more natural in Japanese. For example, it corrects grammatically correct but unnatural expressions to make them sound more natural. Step 5: The conversation example creation unit creates and presents conversation examples based on the user's app usage history. For example, it presents conversation examples that include phrases and words that the user frequently uses.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0131] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0134] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] 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.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] 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.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0150] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] 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.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] 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.

[0157] 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.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0159] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0168] 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.

[0169] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0171] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0172] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0173] 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.

[0174] 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.

[0175] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0176] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0177] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0178] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0179] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0180] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0181] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0182] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0184] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0185] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0186] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0189] 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.

[0190] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0191] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0192] 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.

[0193] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0194] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0195] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0197] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0198] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0199] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0200] [Explanation of symbols]

[0201] 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 reading section that reads out the text, an explanation unit that explains the meaning based on the sentence read out by the reading unit; an example unit that teaches natural usage examples based on the meaning explained by the explanation unit; a correction unit that corrects sentences entered by the user or spoken content into natural Japanese expressions based on the usage examples taught by the example unit; a conversation example creation unit that creates natural conversation examples based on the content corrected by the correction unit and the user's application usage history, and presents the examples as questions. A system characterized by:

2. A learning tendency recording unit is provided for recording the user's learning tendency based on the content corrected by the correction unit. The system of claim 1 .

3. An environment adaptation unit that provides content that matches the user's daily environment based on the conversation examples created by the conversation example creation unit is provided. The system of claim 1 .

4. The reading unit When the user scans a document or other text, it is read aloud The system of claim 1 .

5. The commentary section Explain the meaning of the text read aloud The system of claim 1 .

6. The example part is Based on the explained meaning, we provide examples of effective usage. The system of claim 1 .

7. The correction unit Corrects the text entered or spoken by the user to make it sound more natural in Japanese. The system of claim 1 .

8. The conversation example creation unit Create and present conversation examples based on the user's app usage history The system of claim 1 .

Images