System

Abstract

An object of a system according to an embodiment is to provide contents of an English conversation in accordance with a user's preference and daily life.SOLUTION: A system includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information on a user. The analysis unit analyzes the information collected by the collection unit, and generates the content of the English conversation according to the user's preference and daily life. The providing unit provides the content of the English conversation generated by the analysis unit by voice.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to provide English conversation content that is tailored to the user's preferences and daily routines.

[0005] The system according to the embodiment aims to provide English conversation content that is suited to the user's preferences and daily routine. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit and generates English conversation content tailored to the user's preferences and daily routine. The provision unit provides the English conversation content generated by the analysis unit in audio form. [Effects of the Invention]

[0007] The system according to the embodiment can provide English conversation content that matches the user's preferences and daily routine. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An English conversation support system according to an embodiment of the present invention generates English conversation content based on user information and provides it in audio format. The English conversation support system collects user information, analyzes it using AI, and generates English conversation content tailored to the user's preferences and daily routine. The generated English conversation content is tailored to the user's conversation level and vocabulary. For example, the English conversation support system collects information such as the user's photos and videos, YouTube viewing history, Netflix and TV viewing history, calendar, emails and memos, location information, and shopping history (online and offline). The English conversation support system then analyzes the collected information using AI to generate English conversation content tailored to the user's preferences and daily routine. For example, if the user enjoys traveling, the system generates travel-related English conversation content. The English conversation support system also speaks the generated English conversation content to the user aloud. For example, the AI ​​can ask questions such as, "How was your recent trip?" aloud, allowing the user to practice English conversation. Furthermore, the English conversation support system can also create AI robots, whose appearance and voice can be tailored to the user's preferences. For example, a user can create an AI robot with the appearance and voice of their favorite character and have the robot speak English conversations to them, making English conversation practice more enjoyable. This allows the English conversation support system to provide English conversation practice based on information from the user's daily life. For example, a user can generate English conversation content based on photos and videos taken at a travel destination, and the AI ​​robot can speak those content to the user, allowing the user to practice English conversation while reminiscing about their travel memories. Furthermore, using an AI robot can make English conversation practice more enjoyable.

[0029] The English conversation support system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects user information. The user information may include, but is not limited to, photos and videos, YouTube browsing history, viewing history on Netflix or TV, calendars, emails and memos, location information, and shopping history (online and offline). The collection unit collects this information from, for example, a smartphone or PC. The collection unit may also collect information with the user's consent. For example, the collection unit may obtain the user's consent using a pop-up notification or a checkbox. The analysis unit analyzes the information collected by the collection unit and generates English conversation content tailored to the user's preferences and daily routine. The analysis unit may analyze the information using, for example, data mining or a machine learning algorithm. The analysis unit may also adjust the English conversation content to suit the user's conversation level and vocabulary. For example, the analysis unit may generate content tailored to the user's level, from simple English conversation for beginners to advanced English conversation for advanced learners. The provision unit provides the English conversation content generated by the analysis unit in audio format. The providing unit generates the content of the English conversation by voice using, for example, voice synthesis technology. The providing unit can also provide the generated content of the English conversation through an AI robot. For example, the providing unit creates an AI robot that has the appearance and voice of a character that the user likes, and the robot speaks the English conversation. In this way, the English conversation support system according to the embodiment can support the user's English conversation practice by generating the content of the English conversation based on the user's information and providing it by voice.

[0030] The collection unit can collect information such as the user's photos and videos, YouTube browsing history, Netflix and Tver viewing history, calendar, emails and memos, location information, and shopping purchase history (online and offline). The collection unit, for example, collects the user's photos and videos from a smartphone or PC. For example, the collection unit can acquire photos and videos from the smartphone's camera roll. The collection unit can also collect the user's YouTube browsing history. For example, the collection unit can access the user's YouTube account and acquire the viewing history. The collection unit can also collect the user's viewing history from Netflix, Tver, etc. For example, the collection unit can access the user's Netflix account and acquire the viewing history. The collection unit can also collect calendar information. For example, the collection unit can acquire schedule information from Google Calendar or Outlook Calendar. The collection unit can also collect email and memo information. For example, the collection unit can acquire the contents of emails and memos from Gmail or a memo app. The collection unit can also collect location information. For example, the collection unit can acquire GPS data and Wi-Fi location information. Furthermore, the collection unit can also collect shopping history. For example, the collection unit may acquire online shopping history and offline purchase history. By collecting user information from a variety of sources, more accurate English conversation content can be generated.

[0031] The analysis unit can understand the user's preferences and daily routine based on the collected information and generate English conversation content tailored to the user's preferences and daily routine. The analysis unit can analyze the collected information using, for example, data mining technology. For example, the analysis unit can analyze the content of the user's photos and videos to understand the user's preferences. The analysis unit can also analyze the collected information using machine learning algorithms. For example, the analysis unit can analyze the user's YouTube browsing history or Netflix viewing history to understand the user's interests. The analysis unit can also analyze the user's calendar information and the content of emails and memos. For example, the analysis unit can understand the user's schedule and daily activities. The analysis unit can also analyze location information and shopping purchase history. For example, the analysis unit can understand the user's behavioral patterns and purchasing tendencies. This allows the analysis unit to generate English conversation content tailored to the user's preferences and daily routine. For example, if the user likes to travel, the analysis unit can generate English conversation content related to travel. The analysis unit can also adjust the English conversation content to match the user's speaking level and vocabulary. For example, the analysis unit generates content according to the user's level, from simple English conversation for beginners to advanced English conversation for advanced learners. This allows for more effective English conversation practice by generating English conversation content that matches the user's preferences and daily life.

[0032] The providing unit can speak the generated English conversation content to the user by voice. The providing unit, for example, generates the English conversation content by voice using speech synthesis technology. For example, the providing unit converts text data into voice data and speaks it to the user. The providing unit can also provide the generated English conversation content through an AI robot. For example, the providing unit creates an AI robot with the appearance and voice of a character the user likes, and the robot speaks the English conversation. Furthermore, the providing unit can adjust the English conversation content to suit the user's conversation level and vocabulary. For example, the providing unit provides content according to the user's level, from simple English conversation for beginners to advanced English conversation for advanced learners. In this way, the user can actually practice English conversation by providing the generated English conversation content by voice. For example, the providing unit allows the AI ​​to speak questions such as, "How was your recent trip?" to the user by voice, allowing the user to practice English conversation. This can support the user's English conversation practice.

[0033] The English conversation support system is provided with a means for obtaining user consent. The English conversation support system is provided with a means for obtaining user consent. For example, the English conversation support system may display a pop-up notification to request the user's consent. The English conversation support system may also display a check box to allow the user to select whether or not to consent. Furthermore, when obtaining the user's consent, the English conversation support system may provide a detailed explanation of the consent. For example, the English conversation support system may clearly explain the type of information to be collected and the purpose of use. In this way, by obtaining the user's consent, it is possible to collect information while protecting privacy.

[0034] The English conversation support system includes a means for customizing the AI ​​robot. The English conversation support system includes a means for customizing the AI ​​robot. For example, the English conversation support system allows a user to customize the AI ​​robot by uploading an image or voice of a character that the user likes. The English conversation support system can also allow the user to select the appearance and voice of the AI ​​robot. Furthermore, the English conversation support system can add functions to the AI ​​robot. For example, the English conversation support system can add new conversation skills or actions to the AI ​​robot. This allows the user to customize the AI ​​robot to their preferences, making English conversation practice more enjoyable.

[0035] The customization unit can customize the AI ​​robot by uploading an image or voice of a character selected by the user. For example, the customization unit can customize the appearance of the AI ​​robot by uploading an image of a character that the user likes. For example, the customization unit can change the appearance of the AI ​​robot using an image of an anime character or a celebrity. The customization unit can also customize the voice of the AI ​​robot by uploading a voice that the user likes. For example, the customization unit can change the voice of the AI ​​robot using the voice of a celebrity or an anime character. This allows the user to create an AI robot that has the appearance and voice of a character that the user likes, making English conversation practice more enjoyable.

[0036] The collection unit can analyze the user's past information collection history and select the optimal collection method. For example, the collection unit prioritizes collecting information from apps that the user has used frequently in the past. For example, the collection unit collects information from apps in which the user has spent a lot of time in the past. The collection unit can also collect related information based on the history of videos that the user has liked to watch in the past. For example, the collection unit analyzes the genres and themes of videos that the user has watched in the past and collects related information. Furthermore, the collection unit can collect information from websites on which the user has spent a lot of time in the past. For example, the collection unit analyzes the content of websites that the user has frequently visited in the past and collects related information. This enables more effective information collection by analyzing the past information collection history.

[0037] When collecting information, the collection unit can filter the information based on the user's current activity status and areas of interest. For example, when the user is traveling, the collection unit prioritizes collecting travel-related information. For example, the collection unit collects travel-related information based on the user's current location information. Furthermore, when the user is at work, the collection unit can prioritize collecting work-related information. For example, the collection unit collects work-related information based on the user's calendar information. Furthermore, when the user is engaged in hobby-related activities, the collection unit can prioritize collecting information related to the hobby. For example, the collection unit collects hobby-related information based on the user's social media activity. In this way, more relevant information can be collected by filtering information based on the user's current activity status and areas of interest.

[0038] When collecting information, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit prioritizes collecting voice data. For example, the collection unit analyzes the user's voice input and collects related information. Furthermore, if the user uses text input, the collection unit can also prioritize collecting text data. For example, the collection unit analyzes the user's text input and collects related information. Furthermore, if the user uses a lot of images, the collection unit can also prioritize collecting image data. For example, the collection unit analyzes the user's image input and collects related information. This enables efficient information collection by selecting the optimal collection means depending on the user's input method.

[0039] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific city, the collection unit prioritizes collecting information related to that city. For example, the collection unit collects information related to the city based on the user's current location information. Furthermore, if the user is in a specific country, the collection unit can also prioritize collecting information related to the country. For example, the collection unit collects information related to the country based on the user's current location information. Furthermore, if the user is in a specific region, the collection unit can also prioritize collecting information related to the region. For example, the collection unit collects information related to the region based on the user's current location information. In this way, highly relevant information can be collected by taking into account the user's geographical location information.

[0040] When collecting information, the collection unit can analyze the user's social media activities and collect related information. The collection unit, for example, collects information about accounts the user follows on social media. For example, the collection unit analyzes the content of posts from the accounts the user follows and collects related information. The collection unit can also collect information related to posts the user has "liked" on social media. For example, the collection unit analyzes the content of posts the user has "liked" and collects related information. The collection unit can also collect information related to posts the user has shared on social media. For example, the collection unit analyzes the content of posts the user has shared and collects related information. In this way, highly relevant information can be collected by analyzing the user's social media activities.

[0041] When collecting information, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit preferentially collects information from information sources that the user has previously rated highly. For example, the collection unit collects information from websites and apps that the user has previously rated highly. The collection unit can also collect information by avoiding information sources that the user has previously rated poorly. For example, the collection unit collects information by avoiding websites and apps that the user has previously rated poorly. Furthermore, the collection unit can adjust the collection method based on feedback provided by the user in the past. For example, the collection unit analyzes the user's feedback and optimizes the collection method. This enables more effective information collection by reflecting the user's past feedback.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis of information of high importance. For example, the analysis unit performs a detailed analysis of information related to the user's important schedules and events. The analysis unit can also perform a simplified analysis of information of low importance. For example, the analysis unit performs a simplified analysis of information related to the user's daily activities. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail of information of medium importance. For example, the analysis unit analyzes information related to the user's hobbies and interests with an appropriate level of detail. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies an image analysis algorithm to photos and videos. For example, the analysis unit analyzes the content of the user's photos and videos and extracts related information. The analysis unit can also apply a natural language processing algorithm to text data. For example, the analysis unit analyzes the content of the user's emails and notes and extracts related information. Furthermore, the analysis unit can perform analysis using a geographic information system (GIS) on location information. For example, the analysis unit analyzes the user's location information and extracts related geographic information. This enables more accurate analysis by applying different analysis algorithms depending on the category of information.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit improves the accuracy of the analysis, for example, by referring to analysis results that the user has previously rated highly. For example, the analysis unit learns the patterns of analysis results that the user has previously rated highly, and analyzes new data with similar patterns. The analysis unit can also improve the accuracy of the analysis by avoiding analysis results that the user has previously rated poorly. For example, the analysis unit learns the patterns of analysis results that the user has previously rated poorly, and avoids new data with similar patterns. Furthermore, the analysis unit can adjust the analysis algorithm based on the user's past feedback. For example, the analysis unit analyzes the user's feedback and optimizes the parameters of the analysis algorithm. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0045] During analysis, the analysis unit can determine the priority of analysis based on when the information was collected. For example, the analysis unit prioritizes analysis of the most recent information. For example, the analysis unit prioritizes analysis of information recently collected by the user. The analysis unit can also analyze older information as needed. For example, the analysis unit analyzes information collected by the user in the past as needed. Furthermore, the analysis unit can analyze information of medium recency with a moderate priority. For example, the analysis unit analyzes information collected by the user several weeks ago with a moderate priority. In this way, efficient analysis is possible by determining the priority of analysis based on when the information was collected.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of information with high relevance. For example, the analysis unit prioritizes analysis of information related to the user's current interests. The analysis unit can also postpone analysis of information with low relevance. For example, the analysis unit postpones analysis of information with low relevance to the user's interests. Furthermore, the analysis unit can also analyze information with medium relevance in an appropriate order. For example, the analysis unit analyzes information related to the user's interests in an appropriate order. In this way, adjusting the order of analysis based on the relevance of the information enables efficient analysis.

[0047] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user is a beginner, the analysis unit provides analysis results while avoiding technical terms. For example, the analysis unit explains the analysis results in simple terms without using technical terms. Furthermore, if the user is an intermediate user, the analysis unit can provide analysis results using appropriate technical terms. For example, the analysis unit explains the analysis results using appropriate technical terms. Furthermore, if the user is an advanced user, the analysis unit can provide detailed analysis results using a lot of technical terms. For example, the analysis unit explains the detailed analysis results using technical terms. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand.

[0048] When providing the content, the providing unit can analyze the user's past English conversation history and select the optimal providing method. For example, the providing unit can prioritize providing English conversation topics that the user has used in the past. For example, the providing unit can analyze topics that the user has used in the past and provide related English conversation content. The providing unit can also provide English conversation topics that the user has had difficulty with in the past while avoiding them. For example, the providing unit can analyze topics that the user has had difficulty with in the past and provide English conversation content that avoids them. Furthermore, the providing unit can select the optimal providing method based on the user's past English conversation history. For example, the providing unit can analyze the user's past English conversation history and select the optimal providing method. In this way, more effective English conversation practice can be provided by analyzing the user's past English conversation history.

[0049] The providing unit can customize the English conversation content based on the user's current living situation when providing the content. For example, if the user is traveling, the providing unit can provide travel-related English conversation content. For example, the providing unit can provide travel-related English conversation content based on the user's current location information. Furthermore, if the user is at work, the providing unit can also provide work-related English conversation content. For example, the providing unit can provide work-related English conversation content based on the user's calendar information. Furthermore, if the user is engaged in a hobby-related activity, the providing unit can also provide English conversation content related to the hobby. For example, the providing unit can provide English conversation content related to the hobby based on the user's social media activity. In this way, by customizing the English conversation content based on the user's current living situation, more relevant English conversation practice can be provided.

[0050] The providing unit can improve the content of the English conversation by reflecting the user's feedback when providing the content. The providing unit improves the content of the next lesson, for example, based on the content of the English conversation that the user has given a high rating. For example, the providing unit analyzes the content of the English conversation that the user has given a high rating and improves the content of the next lesson. The providing unit can also improve the content of the next lesson by avoiding the content of the English conversation that the user has given a low rating. For example, the providing unit analyzes the content of the English conversation that the user has given a low rating and improves the content of the next lesson by avoiding it. Furthermore, the providing unit can customize the content of the English conversation based on the user's feedback. For example, the providing unit analyzes the user's feedback and customizes the content of the English conversation. In this way, more effective English conversation practice can be provided by reflecting the user's feedback.

[0051] When providing the English conversation content, the providing unit can select the most appropriate English conversation content by taking into account the user's geographical location information. For example, if the user is in a specific city, the providing unit provides English conversation content related to the city. For example, the providing unit provides English conversation content related to the city based on the user's current location information. Furthermore, if the user is in a specific country, the providing unit can also provide English conversation content related to the country. For example, the providing unit provides English conversation content related to the country based on the user's current location information. Furthermore, if the user is in a specific region, the providing unit can also provide English conversation content related to the region. For example, the providing unit provides English conversation content related to the region based on the user's current location information. In this way, highly relevant English conversation practice can be provided by taking into account the user's geographical location information.

[0052] When providing the content, the providing unit can analyze the user's social media activity and suggest English conversation content. The providing unit, for example, provides English conversation content related to accounts the user follows on social media. For example, the providing unit analyzes the content of posts from accounts the user follows and provides the related English conversation content. The providing unit can also provide English conversation content related to posts the user has "liked" on social media. For example, the providing unit analyzes the content of posts the user has "liked" and provides the related English conversation content. The providing unit can also provide English conversation content related to posts the user has shared on social media. For example, the providing unit analyzes the content of posts the user has shared and provides the related English conversation content. In this way, highly relevant English conversation practice can be provided by analyzing the user's social media activity.

[0053] The providing unit can customize the content of the English conversation by reflecting the user's past feedback when providing the content. The providing unit, for example, customizes the next content based on the content of the English conversation that the user has given a high rating. For example, the providing unit analyzes the content of the English conversation that the user has given a high rating and customizes the next content. The providing unit can also customize the next content by avoiding the content of the English conversation that the user has given a low rating. For example, the providing unit analyzes the content of the English conversation that the user has given a low rating and customizes the next content by avoiding that content. Furthermore, the providing unit can also customize the content of the English conversation based on the user's feedback. For example, the providing unit analyzes the user's feedback and customizes the content of the English conversation. In this way, more effective English conversation practice can be provided by reflecting the user's past feedback.

[0054] When obtaining consent, the consent unit can select the optimal consent method by referring to the user's past consent history. The consent unit adjusts the consent acquisition method based on, for example, the content to which the user has consented in the past. For example, the consent unit analyzes the content to which the user has consented in the past and adjusts the consent acquisition method. The consent unit can also adjust the consent acquisition method to avoid content that the user has rejected in the past. For example, the consent unit analyzes the content that the user has rejected in the past and adjusts the consent acquisition method to avoid that content. Furthermore, the consent unit can also select the optimal consent acquisition method based on the user's past consent history. For example, the consent unit analyzes the user's past consent history and selects the optimal consent acquisition method. In this way, by referring to the user's past consent history, more appropriate consent can be obtained.

[0055] When obtaining consent, the consent unit can customize the consent content based on the user's current situation. For example, if the user is traveling, the consent unit provides travel-related consent content. For example, the consent unit provides travel-related consent content based on the user's current location information. Furthermore, if the user is at work, the consent unit can also provide work-related consent content. For example, the consent unit provides work-related consent content based on the user's calendar information. Furthermore, if the user is engaged in a hobby-related activity, the consent unit can also provide consent content related to the hobby. For example, the consent unit provides consent content related to the hobby based on the user's social media activity. This makes it possible to obtain more appropriate consent by customizing the consent content based on the user's current situation.

[0056] When obtaining consent, the consent unit can select the optimal consent method by taking into account the user's device information. For example, if the user is using a smartphone, the consent unit provides a consent acquisition method that is tailored to the screen size. For example, the consent unit displays a pop-up notification optimized for the smartphone screen size. Furthermore, if the user is using a tablet, the consent unit can also provide a consent acquisition method that is optimized for a larger screen. For example, the consent unit displays a pop-up notification optimized for the tablet screen size. Furthermore, if the user is using a smartwatch, the consent unit can also provide a simple and highly visible consent acquisition method. For example, the consent unit displays a pop-up notification optimized for the smartwatch screen size. This enables more appropriate consent acquisition by taking into account the user's device information.

[0057] When obtaining consent, the consent unit can make the consent content multilingual according to the user's language setting. The consent unit automatically sets the consent content based on, for example, the language setting of the user's device. For example, the consent unit detects the language setting of the user's device and displays the consent content in that language. The consent unit can also provide a language switching function when the user uses multiple languages. For example, the consent unit allows the user to select the language to use. Furthermore, when the user selects a specific language, the consent unit can also provide the consent content in that language. For example, the consent unit displays the consent content in the language selected by the user. This makes it possible to obtain more appropriate consent by making the consent content multilingual according to the user's language setting.

[0058] During customization, the customization unit can select the optimal customization method by referring to the user's past customization history. The customization unit, for example, preferentially provides customization options that the user has used in the past. For example, the customization unit analyzes customization options that the user has used in the past and provides related customization options. The customization unit can also provide customization options that the user has avoided in the past, excluding those options. For example, the customization unit analyzes customization options that the user has avoided in the past and provides customization options that exclude those options. Furthermore, the customization unit can select the optimal customization method based on the user's past customization history. For example, the customization unit analyzes the user's past customization history and selects the optimal customization method. This enables more appropriate customization by referring to the user's past customization history.

[0059] During customization, the customization unit can adjust the customization content based on the user's current preferences. For example, the customization unit performs customization based on the appearance and voice of a character currently preferred by the user. For example, the customization unit performs customization using an image and voice of a character currently preferred by the user. The customization unit can also provide customization content related to topics in which the user is currently interested. For example, the customization unit provides customization options related to topics in which the user is currently interested. Furthermore, the customization unit can adjust the customization content based on the user's current living situation. For example, the customization unit analyzes the user's current living situation and adjusts the customization content based on the analysis. This allows for more appropriate customization by adjusting the customization content based on the user's current preferences.

[0060] During customization, the customization unit can select the optimal customization method by taking into account the user's geographical location information. For example, if the user is in a specific city, the customization unit provides customization content related to the city. For example, the customization unit provides customization content related to the city based on the user's current location information. Furthermore, if the user is in a specific country, the customization unit can also provide customization content related to the country. For example, the customization unit provides customization content related to the country based on the user's current location information. Furthermore, if the user is in a specific region, the customization unit can also provide customization content related to the region. For example, the customization unit provides customization content related to the region based on the user's current location information. This enables more appropriate customization by taking into account the user's geographical location information.

[0061] During customization, the customization unit can analyze the user's social media activity and suggest customization content. The customization unit, for example, provides customization content related to accounts the user follows on social media. For example, the customization unit analyzes the content of posts from the accounts the user follows and provides the related customization content. The customization unit can also provide customization content related to posts the user has "liked" on social media. For example, the customization unit analyzes the content of posts the user has "liked" and provides the related customization content. The customization unit can also provide customization content related to posts the user has shared on social media. For example, the customization unit analyzes the content of posts the user has shared and provides the related customization content. This enables more appropriate customization by analyzing the user's social media activity.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The collection unit may also collect the user's health data. For example, the collection unit may collect heart rate, step count, and sleep data from a smartwatch or fitness tracker. The collection unit may also collect the user's food records and exercise records. Furthermore, the collection unit may collect data from devices that measure the user's stress level and energy level. This allows the system to generate English conversation content based on the user's health status.

[0064] The analysis unit can analyze the user's health data and generate English conversation content based on the user's health condition. For example, the analysis unit can analyze the user's heart rate and stress level and generate relaxing English conversation content. The analysis unit can also analyze the user's sleep data and generate English conversation content including sleep advice. Furthermore, the analysis unit can analyze the user's food records and generate English conversation content related to healthy eating. This can support a healthier lifestyle by generating English conversation content based on the user's health condition.

[0065] The English conversation support system can analyze the user's learning history and adjust the content of the English conversation based on the user's learning progress. For example, the English conversation support system can generate new English conversation content based on words and phrases the user has learned in the past. The English conversation support system can also provide English conversation content to help the user improve areas in which they are weak. Furthermore, the English conversation support system can adjust the difficulty level of the English conversation to match the user's learning pace. This makes it possible to provide English conversation practice that is in line with the user's learning progress.

[0066] The English conversation support system can analyze a user's social media activity and generate English conversation content based on the user's interests. For example, the English conversation support system can generate relevant English conversation content based on the content of posts from accounts the user follows. The English conversation support system can also generate interesting English conversation content based on the content of posts the user has "liked." Furthermore, the English conversation support system can generate shareable English conversation content based on the content of posts the user has shared. This makes it possible to provide English conversation practice based on the user's social media activity.

[0067] The English conversation support system can suggest English conversation practice times based on the user's lifestyle. For example, the English conversation support system can analyze the user's sleep patterns and suggest optimal practice times. The English conversation support system can also suggest efficient practice times taking into account the user's work and school schedules. Furthermore, the English conversation support system can analyze the user's free time and suggest practice times when the user is able to relax. This allows the system to provide English conversation practice that fits the user's lifestyle.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The collection unit collects user information. This information may include, for example, photos and videos, YouTube browsing history, Netflix and TVer viewing history, calendars, emails and memos, location information, and shopping purchase history (online and offline). The collection unit collects this information from smartphones and PCs. The collection unit may also collect information with the user's consent. For example, the user's consent may be obtained using a pop-up notification or a checkbox. Step 2: The analysis unit analyzes the information collected by the collection unit and generates English conversation content tailored to the user's preferences and daily routine. The analysis unit analyzes the information using data mining and machine learning algorithms and can adjust the English conversation content to suit the user's conversation level and vocabulary. For example, it generates content tailored to the user's level, from simple English conversation for beginners to advanced English conversation for advanced learners. Step 3: The providing unit provides the English conversation content generated by the analyzing unit as audio. The providing unit can also generate the English conversation content as audio using speech synthesis technology and provide it through an AI robot. For example, a user can create an AI robot with the appearance and voice of their favorite character, and the robot can speak the English conversation.

[0070] (Example 2) An English conversation support system according to an embodiment of the present invention generates English conversation content based on user information and provides it in audio format. The English conversation support system collects user information, analyzes it using AI, and generates English conversation content tailored to the user's preferences and daily routine. The generated English conversation content is tailored to the user's conversation level and vocabulary. For example, the English conversation support system collects information such as the user's photos and videos, YouTube viewing history, Netflix and TV viewing history, calendar, emails and memos, location information, and shopping history (online and offline). The English conversation support system then analyzes the collected information using AI to generate English conversation content tailored to the user's preferences and daily routine. For example, if the user enjoys traveling, the system generates travel-related English conversation content. The English conversation support system also speaks the generated English conversation content to the user aloud. For example, the AI ​​can ask questions such as, "How was your recent trip?" aloud, allowing the user to practice English conversation. Furthermore, the English conversation support system can also create AI robots, whose appearance and voice can be tailored to the user's preferences. For example, a user can create an AI robot with the appearance and voice of their favorite character and have the robot speak English conversations to them, making English conversation practice more enjoyable. This allows the English conversation support system to provide English conversation practice based on information from the user's daily life. For example, a user can generate English conversation content based on photos and videos taken at a travel destination, and the AI ​​robot can speak those content to the user, allowing the user to practice English conversation while reminiscing about their travel memories. Furthermore, using an AI robot can make English conversation practice more enjoyable.

[0071] The English conversation support system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects user information. The user information may include, but is not limited to, photos and videos, YouTube browsing history, viewing history on Netflix or TV, calendars, emails and memos, location information, and shopping history (online and offline). The collection unit collects this information from, for example, a smartphone or PC. The collection unit may also collect information with the user's consent. For example, the collection unit may obtain the user's consent using a pop-up notification or a checkbox. The analysis unit analyzes the information collected by the collection unit and generates English conversation content tailored to the user's preferences and daily routine. The analysis unit may analyze the information using, for example, data mining or a machine learning algorithm. The analysis unit may also adjust the English conversation content to suit the user's conversation level and vocabulary. For example, the analysis unit may generate content tailored to the user's level, from simple English conversation for beginners to advanced English conversation for advanced learners. The provision unit provides the English conversation content generated by the analysis unit in audio format. The providing unit generates the content of the English conversation by voice using, for example, voice synthesis technology. The providing unit can also provide the generated content of the English conversation through an AI robot. For example, the providing unit creates an AI robot that has the appearance and voice of a character that the user likes, and the robot speaks the English conversation. In this way, the English conversation support system according to the embodiment can support the user's English conversation practice by generating the content of the English conversation based on the user's information and providing it by voice.

[0072] The collection unit can collect information such as the user's photos and videos, YouTube browsing history, Netflix and Tver viewing history, calendar, emails and memos, location information, and shopping purchase history (online and offline). The collection unit, for example, collects the user's photos and videos from a smartphone or PC. For example, the collection unit can acquire photos and videos from the smartphone's camera roll. The collection unit can also collect the user's YouTube browsing history. For example, the collection unit can access the user's YouTube account and acquire the viewing history. The collection unit can also collect the user's viewing history from Netflix, Tver, etc. For example, the collection unit can access the user's Netflix account and acquire the viewing history. The collection unit can also collect calendar information. For example, the collection unit can acquire schedule information from Google Calendar or Outlook Calendar. The collection unit can also collect email and memo information. For example, the collection unit can acquire the contents of emails and memos from Gmail or a memo app. The collection unit can also collect location information. For example, the collection unit can acquire GPS data and Wi-Fi location information. Furthermore, the collection unit can also collect shopping history. For example, the collection unit may acquire online shopping history and offline purchase history. By collecting user information from a variety of sources, more accurate English conversation content can be generated.

[0073] The analysis unit can understand the user's preferences and daily routine based on the collected information and generate English conversation content tailored to the user's preferences and daily routine. The analysis unit can analyze the collected information using, for example, data mining technology. For example, the analysis unit can analyze the content of the user's photos and videos to understand the user's preferences. The analysis unit can also analyze the collected information using machine learning algorithms. For example, the analysis unit can analyze the user's YouTube browsing history or Netflix viewing history to understand the user's interests. The analysis unit can also analyze the user's calendar information and the content of emails and memos. For example, the analysis unit can understand the user's schedule and daily activities. The analysis unit can also analyze location information and shopping purchase history. For example, the analysis unit can understand the user's behavioral patterns and purchasing tendencies. This allows the analysis unit to generate English conversation content tailored to the user's preferences and daily routine. For example, if the user likes to travel, the analysis unit can generate English conversation content related to travel. The analysis unit can also adjust the English conversation content to match the user's speaking level and vocabulary. For example, the analysis unit generates content according to the user's level, from simple English conversation for beginners to advanced English conversation for advanced learners. This allows for more effective English conversation practice by generating English conversation content that matches the user's preferences and daily life.

[0074] The providing unit can speak the generated English conversation content to the user by voice. The providing unit, for example, generates the English conversation content by voice using speech synthesis technology. For example, the providing unit converts text data into voice data and speaks it to the user. The providing unit can also provide the generated English conversation content through an AI robot. For example, the providing unit creates an AI robot with the appearance and voice of a character the user likes, and the robot speaks the English conversation. Furthermore, the providing unit can adjust the English conversation content to suit the user's conversation level and vocabulary. For example, the providing unit provides content according to the user's level, from simple English conversation for beginners to advanced English conversation for advanced learners. In this way, the user can actually practice English conversation by providing the generated English conversation content by voice. For example, the providing unit allows the AI ​​to speak questions such as, "How was your recent trip?" to the user by voice, allowing the user to practice English conversation. This can support the user's English conversation practice.

[0075] The English conversation support system is provided with a means for obtaining user consent. The English conversation support system is provided with a means for obtaining user consent. For example, the English conversation support system may display a pop-up notification to request the user's consent. The English conversation support system may also display a check box to allow the user to select whether or not to consent. Furthermore, when obtaining the user's consent, the English conversation support system may provide a detailed explanation of the consent. For example, the English conversation support system may clearly explain the type of information to be collected and the purpose of use. In this way, by obtaining the user's consent, it is possible to collect information while protecting privacy.

[0076] The English conversation support system includes a means for customizing the AI ​​robot. The English conversation support system includes a means for customizing the AI ​​robot. For example, the English conversation support system allows a user to customize the AI ​​robot by uploading an image or voice of a character that the user likes. The English conversation support system can also allow the user to select the appearance and voice of the AI ​​robot. Furthermore, the English conversation support system can add functions to the AI ​​robot. For example, the English conversation support system can add new conversation skills or actions to the AI ​​robot. This allows the user to customize the AI ​​robot to their preferences, making English conversation practice more enjoyable.

[0077] The customization unit can customize the AI ​​robot by uploading an image or voice of a character selected by the user. For example, the customization unit can customize the appearance of the AI ​​robot by uploading an image of a character that the user likes. For example, the customization unit can change the appearance of the AI ​​robot using an image of an anime character or a celebrity. The customization unit can also customize the voice of the AI ​​robot by uploading a voice that the user likes. For example, the customization unit can change the voice of the AI ​​robot using the voice of a celebrity or an anime character. This allows the user to create an AI robot that has the appearance and voice of a character that the user likes, making English conversation practice more enjoyable.

[0078] The collection unit can adjust the timing of information collection based on the estimated user emotion using a means for estimating the user's emotion. The collection unit, for example, estimates the user's emotion using facial expression recognition technology. For example, the collection unit analyzes the user's facial expression captured by a camera to estimate the emotion. The collection unit can also estimate the user's emotion using voice analysis technology. For example, the collection unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the collection unit can adjust the timing of information collection based on the user's emotion. For example, the collection unit collects information at night if the user is relaxed. Furthermore, the collection unit can collect information in between work if the user is busy. Furthermore, the collection unit can collect information during times when the user is stressed. In this way, by adjusting the timing of information collection according to the user's emotion, information can be collected at a more appropriate time. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] The collection unit can analyze the user's past information collection history and select the optimal collection method. For example, the collection unit prioritizes collecting information from apps that the user has used frequently in the past. For example, the collection unit collects information from apps in which the user has spent a lot of time in the past. The collection unit can also collect related information based on the history of videos that the user has liked to watch in the past. For example, the collection unit analyzes the genres and themes of videos that the user has watched in the past and collects related information. Furthermore, the collection unit can collect information from websites on which the user has spent a lot of time in the past. For example, the collection unit analyzes the content of websites that the user has frequently visited in the past and collects related information. This enables more effective information collection by analyzing the past information collection history.

[0080] When collecting information, the collection unit can filter the information based on the user's current activity status and areas of interest. For example, when the user is traveling, the collection unit prioritizes collecting travel-related information. For example, the collection unit collects travel-related information based on the user's current location information. Furthermore, when the user is at work, the collection unit can prioritize collecting work-related information. For example, the collection unit collects work-related information based on the user's calendar information. Furthermore, when the user is engaged in hobby-related activities, the collection unit can prioritize collecting information related to the hobby. For example, the collection unit collects hobby-related information based on the user's social media activity. In this way, more relevant information can be collected by filtering information based on the user's current activity status and areas of interest.

[0081] When collecting information, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit prioritizes collecting voice data. For example, the collection unit analyzes the user's voice input and collects related information. Furthermore, if the user uses text input, the collection unit can also prioritize collecting text data. For example, the collection unit analyzes the user's text input and collects related information. Furthermore, if the user uses a lot of images, the collection unit can also prioritize collecting image data. For example, the collection unit analyzes the user's image input and collects related information. This enables efficient information collection by selecting the optimal collection means depending on the user's input method.

[0082] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the collection unit can analyze the user's facial expressions captured by a camera to estimate the emotions. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit can analyze the tone and speed of the user's voice to estimate the emotions. The collection unit can also determine the priority of information to be collected based on the user's emotions. For example, if the user is excited, the collection unit can prioritize collecting entertainment-related information. If the user is calm, the collection unit can prioritize collecting education-related information. If the user is tired, the collection unit can prioritize collecting relaxing information. This allows more appropriate information to be collected by determining the priority of information to be collected based on 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific city, the collection unit prioritizes collecting information related to that city. For example, the collection unit collects information related to the city based on the user's current location information. Furthermore, if the user is in a specific country, the collection unit can also prioritize collecting information related to the country. For example, the collection unit collects information related to the country based on the user's current location information. Furthermore, if the user is in a specific region, the collection unit can also prioritize collecting information related to the region. For example, the collection unit collects information related to the region based on the user's current location information. In this way, highly relevant information can be collected by taking into account the user's geographical location information.

[0084] When collecting information, the collection unit can analyze the user's social media activities and collect related information. The collection unit, for example, collects information about accounts the user follows on social media. For example, the collection unit analyzes the content of posts from the accounts the user follows and collects related information. The collection unit can also collect information related to posts the user has "liked" on social media. For example, the collection unit analyzes the content of posts the user has "liked" and collects related information. The collection unit can also collect information related to posts the user has shared on social media. For example, the collection unit analyzes the content of posts the user has shared and collects related information. In this way, highly relevant information can be collected by analyzing the user's social media activities.

[0085] When collecting information, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit preferentially collects information from information sources that the user has previously rated highly. For example, the collection unit collects information from websites and apps that the user has previously rated highly. The collection unit can also collect information by avoiding information sources that the user has previously rated poorly. For example, the collection unit collects information by avoiding websites and apps that the user has previously rated poorly. Furthermore, the collection unit can adjust the collection method based on feedback provided by the user in the past. For example, the collection unit analyzes the user's feedback and optimizes the collection method. This enables more effective information collection by reflecting the user's past feedback.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. The analysis unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the analysis unit can analyze the user's facial expressions captured by a camera to estimate the emotions. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the analysis unit can adjust the presentation method of the analysis based on the user's emotions. For example, if the user is relaxed, the analysis unit can present the analysis results in soft expressions. If the user is nervous, the analysis unit can present the analysis results in concise and clear expressions. Furthermore, if the user is excited, the analysis unit can present the analysis results in visually stimulating expressions. In this way, by adjusting the presentation method of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0087] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis of information of high importance. For example, the analysis unit performs a detailed analysis of information related to the user's important schedules and events. The analysis unit can also perform a simplified analysis of information of low importance. For example, the analysis unit performs a simplified analysis of information related to the user's daily activities. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail of information of medium importance. For example, the analysis unit analyzes information related to the user's hobbies and interests with an appropriate level of detail. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information.

[0088] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies an image analysis algorithm to photos and videos. For example, the analysis unit analyzes the content of the user's photos and videos and extracts related information. The analysis unit can also apply a natural language processing algorithm to text data. For example, the analysis unit analyzes the content of the user's emails and notes and extracts related information. Furthermore, the analysis unit can perform analysis using a geographic information system (GIS) on location information. For example, the analysis unit analyzes the user's location information and extracts related geographic information. This enables more accurate analysis by applying different analysis algorithms depending on the category of information.

[0089] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit improves the accuracy of the analysis, for example, by referring to analysis results that the user has previously rated highly. For example, the analysis unit learns the patterns of analysis results that the user has previously rated highly, and analyzes new data with similar patterns. The analysis unit can also improve the accuracy of the analysis by avoiding analysis results that the user has previously rated poorly. For example, the analysis unit learns the patterns of analysis results that the user has previously rated poorly, and avoids new data with similar patterns. Furthermore, the analysis unit can adjust the analysis algorithm based on the user's past feedback. For example, the analysis unit analyzes the user's feedback and optimizes the parameters of the analysis algorithm. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0090] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the analysis unit can analyze the user's facial expressions captured with a camera to estimate emotions. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate emotions. The analysis unit can also adjust the length of the analysis based on the user's emotions. For example, the analysis unit can provide a short and concise analysis result if the user is in a hurry. The analysis unit can also provide a detailed analysis result if the user is relaxed. The analysis unit can also provide a visually stimulating analysis result if the user is excited. This allows for more appropriate analysis results to be provided by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] During analysis, the analysis unit can determine the priority of analysis based on when the information was collected. For example, the analysis unit prioritizes analysis of the most recent information. For example, the analysis unit prioritizes analysis of information recently collected by the user. The analysis unit can also analyze older information as needed. For example, the analysis unit analyzes information collected by the user in the past as needed. Furthermore, the analysis unit can analyze information of medium recency with a moderate priority. For example, the analysis unit analyzes information collected by the user several weeks ago with a moderate priority. In this way, efficient analysis is possible by determining the priority of analysis based on when the information was collected.

[0092] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of information with high relevance. For example, the analysis unit prioritizes analysis of information related to the user's current interests. The analysis unit can also postpone analysis of information with low relevance. For example, the analysis unit postpones analysis of information with low relevance to the user's interests. Furthermore, the analysis unit can also analyze information with medium relevance in an appropriate order. For example, the analysis unit analyzes information related to the user's interests in an appropriate order. In this way, adjusting the order of analysis based on the relevance of the information enables efficient analysis.

[0093] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user is a beginner, the analysis unit provides analysis results while avoiding technical terms. For example, the analysis unit explains the analysis results in simple terms without using technical terms. Furthermore, if the user is an intermediate user, the analysis unit can provide analysis results using appropriate technical terms. For example, the analysis unit explains the analysis results using appropriate technical terms. Furthermore, if the user is an advanced user, the analysis unit can provide detailed analysis results using a lot of technical terms. For example, the analysis unit explains the detailed analysis results using technical terms. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand.

[0094] The providing unit can estimate the user's emotions and adjust the content of the English conversation to be provided based on the estimated user's emotions. The providing unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the providing unit can analyze the user's facial expressions captured by a camera to estimate the emotions. The providing unit can also estimate the user's emotions using voice analysis technology. For example, the providing unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the providing unit can adjust the content of the English conversation to be provided based on the user's emotions. For example, if the user is relaxed, the providing unit can provide English conversation with relaxed content. Furthermore, if the user is nervous, the providing unit can provide English conversation with simple and reassuring content. Furthermore, if the user is excited, the providing unit can provide English conversation with content that allows the user to share the excitement. This allows the content of the English conversation to be adjusted according to the user's emotions, thereby providing more appropriate English conversation practice. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0095] When providing the content, the providing unit can analyze the user's past English conversation history and select the optimal providing method. For example, the providing unit can prioritize providing English conversation topics that the user has used in the past. For example, the providing unit can analyze topics that the user has used in the past and provide related English conversation content. The providing unit can also provide English conversation topics that the user has had difficulty with in the past while avoiding them. For example, the providing unit can analyze topics that the user has had difficulty with in the past and provide English conversation content that avoids them. Furthermore, the providing unit can select the optimal providing method based on the user's past English conversation history. For example, the providing unit can analyze the user's past English conversation history and select the optimal providing method. In this way, more effective English conversation practice can be provided by analyzing the user's past English conversation history.

[0096] The providing unit can customize the English conversation content based on the user's current living situation when providing the content. For example, if the user is traveling, the providing unit can provide travel-related English conversation content. For example, the providing unit can provide travel-related English conversation content based on the user's current location information. Furthermore, if the user is at work, the providing unit can also provide work-related English conversation content. For example, the providing unit can provide work-related English conversation content based on the user's calendar information. Furthermore, if the user is engaged in a hobby-related activity, the providing unit can also provide English conversation content related to the hobby. For example, the providing unit can provide English conversation content related to the hobby based on the user's social media activity. In this way, by customizing the English conversation content based on the user's current living situation, more relevant English conversation practice can be provided.

[0097] The providing unit can improve the content of the English conversation by reflecting the user's feedback when providing the content. The providing unit improves the content of the next lesson, for example, based on the content of the English conversation that the user has given a high rating. For example, the providing unit analyzes the content of the English conversation that the user has given a high rating and improves the content of the next lesson. The providing unit can also improve the content of the next lesson by avoiding the content of the English conversation that the user has given a low rating. For example, the providing unit analyzes the content of the English conversation that the user has given a low rating and improves the content of the next lesson by avoiding it. Furthermore, the providing unit can customize the content of the English conversation based on the user's feedback. For example, the providing unit analyzes the user's feedback and customizes the content of the English conversation. In this way, more effective English conversation practice can be provided by reflecting the user's feedback.

[0098] The providing unit can estimate the user's emotions and prioritize the English conversations to be provided based on the estimated user's emotions. The providing unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the providing unit can analyze the user's facial expressions captured with a camera to estimate the emotions. The providing unit can also estimate the user's emotions using voice analysis technology. For example, the providing unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the providing unit can prioritize the English conversations to be provided based on the user's emotions. For example, if the user is relaxed, the providing unit can prioritize English conversations with relaxing content. Furthermore, if the user is nervous, the providing unit can prioritize English conversations with simple and reassuring content. Furthermore, if the user is excited, the providing unit can prioritize English conversations with content that allows the user to share the excitement. This allows for more appropriate English conversation practice by prioritizing English conversations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0099] When providing the English conversation content, the providing unit can select the most appropriate English conversation content by taking into account the user's geographical location information. For example, if the user is in a specific city, the providing unit provides English conversation content related to the city. For example, the providing unit provides English conversation content related to the city based on the user's current location information. Furthermore, if the user is in a specific country, the providing unit can also provide English conversation content related to the country. For example, the providing unit provides English conversation content related to the country based on the user's current location information. Furthermore, if the user is in a specific region, the providing unit can also provide English conversation content related to the region. For example, the providing unit provides English conversation content related to the region based on the user's current location information. In this way, highly relevant English conversation practice can be provided by taking into account the user's geographical location information.

[0100] When providing the content, the providing unit can analyze the user's social media activity and suggest English conversation content. The providing unit, for example, provides English conversation content related to accounts the user follows on social media. For example, the providing unit analyzes the content of posts from accounts the user follows and provides the related English conversation content. The providing unit can also provide English conversation content related to posts the user has "liked" on social media. For example, the providing unit analyzes the content of posts the user has "liked" and provides the related English conversation content. The providing unit can also provide English conversation content related to posts the user has shared on social media. For example, the providing unit analyzes the content of posts the user has shared and provides the related English conversation content. In this way, highly relevant English conversation practice can be provided by analyzing the user's social media activity.

[0101] The providing unit can customize the content of the English conversation by reflecting the user's past feedback when providing the content. The providing unit, for example, customizes the next content based on the content of the English conversation that the user has given a high rating. For example, the providing unit analyzes the content of the English conversation that the user has given a high rating and customizes the next content. The providing unit can also customize the next content by avoiding the content of the English conversation that the user has given a low rating. For example, the providing unit analyzes the content of the English conversation that the user has given a low rating and customizes the next content by avoiding that content. Furthermore, the providing unit can also customize the content of the English conversation based on the user's feedback. For example, the providing unit analyzes the user's feedback and customizes the content of the English conversation. In this way, more effective English conversation practice can be provided by reflecting the user's past feedback.

[0102] The consent unit can estimate the user's emotions and adjust the consent acquisition method based on the estimated user's emotions. The consent unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the consent unit can analyze the user's facial expressions captured by a camera to estimate the emotions. The consent unit can also estimate the user's emotions using voice analysis technology. For example, the consent unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the consent unit can adjust the consent acquisition method based on the user's emotions. For example, the consent unit can provide a consent acquisition method that includes detailed explanations when the user is relaxed. Furthermore, the consent unit can provide a concise and clear consent acquisition method when the user is nervous. Furthermore, the consent unit can provide a method that allows for quick consent acquisition when the user is in a hurry. This allows for more appropriate consent acquisition by adjusting the consent acquisition method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0103] When obtaining consent, the consent unit can select the optimal consent method by referring to the user's past consent history. The consent unit adjusts the consent acquisition method based on, for example, the content to which the user has consented in the past. For example, the consent unit analyzes the content to which the user has consented in the past and adjusts the consent acquisition method. The consent unit can also adjust the consent acquisition method to avoid content that the user has rejected in the past. For example, the consent unit analyzes the content that the user has rejected in the past and adjusts the consent acquisition method to avoid that content. Furthermore, the consent unit can also select the optimal consent acquisition method based on the user's past consent history. For example, the consent unit analyzes the user's past consent history and selects the optimal consent acquisition method. In this way, by referring to the user's past consent history, more appropriate consent can be obtained.

[0104] When obtaining consent, the consent unit can customize the consent content based on the user's current situation. For example, if the user is traveling, the consent unit provides travel-related consent content. For example, the consent unit provides travel-related consent content based on the user's current location information. Furthermore, if the user is at work, the consent unit can also provide work-related consent content. For example, the consent unit provides work-related consent content based on the user's calendar information. Furthermore, if the user is engaged in a hobby-related activity, the consent unit can also provide consent content related to the hobby. For example, the consent unit provides consent content related to the hobby based on the user's social media activity. This makes it possible to obtain more appropriate consent by customizing the consent content based on the user's current situation.

[0105] The consent unit can estimate the user's emotions and determine the priority of consent based on the estimated user emotions. The consent unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the consent unit can analyze the user's facial expressions captured by a camera to estimate the emotions. The consent unit can also estimate the user's emotions using voice analysis technology. For example, the consent unit can analyze the tone and speed of the user's voice to estimate the emotions. The consent unit can also determine the priority of consent based on the user's emotions. For example, the consent unit can prioritize providing detailed consent details if the user is relaxed. The consent unit can also prioritize providing concise consent details if the user is nervous. The consent unit can also prioritize providing content that can quickly obtain consent if the user is in a hurry. This enables more appropriate consent acquisition by determining the priority of consent based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0106] When obtaining consent, the consent unit can select the optimal consent method by taking into account the user's device information. For example, if the user is using a smartphone, the consent unit provides a consent acquisition method that is tailored to the screen size. For example, the consent unit displays a pop-up notification optimized for the smartphone screen size. Furthermore, if the user is using a tablet, the consent unit can also provide a consent acquisition method that is optimized for a larger screen. For example, the consent unit displays a pop-up notification optimized for the tablet screen size. Furthermore, if the user is using a smartwatch, the consent unit can also provide a simple and highly visible consent acquisition method. For example, the consent unit displays a pop-up notification optimized for the smartwatch screen size. This enables more appropriate consent acquisition by taking into account the user's device information.

[0107] When obtaining consent, the consent unit can make the consent content multilingual according to the user's language setting. The consent unit automatically sets the consent content based on, for example, the language setting of the user's device. For example, the consent unit detects the language setting of the user's device and displays the consent content in that language. The consent unit can also provide a language switching function when the user uses multiple languages. For example, the consent unit allows the user to select the language to use. Furthermore, when the user selects a specific language, the consent unit can also provide the consent content in that language. For example, the consent unit displays the consent content in the language selected by the user. This makes it possible to obtain more appropriate consent by making the consent content multilingual according to the user's language setting.

[0108] The customization unit can estimate the user's emotions and adjust the customization method based on the estimated user emotions. The customization unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the customization unit can analyze the user's facial expressions captured by a camera to estimate the emotions. The customization unit can also estimate the user's emotions using voice analysis technology. For example, the customization unit can analyze the tone and speed of the user's voice to estimate the emotions. The customization unit can also adjust the customization method based on the user's emotions. For example, the customization unit can provide detailed customization options when the user is relaxed. The customization unit can also provide concise and clear customization options when the user is nervous. The customization unit can also provide visually stimulating customization options when the user is excited. This enables more appropriate customization by adjusting the customization method 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0109] During customization, the customization unit can select the optimal customization method by referring to the user's past customization history. The customization unit, for example, preferentially provides customization options that the user has used in the past. For example, the customization unit analyzes customization options that the user has used in the past and provides related customization options. The customization unit can also provide customization options that the user has avoided in the past, excluding those options. For example, the customization unit analyzes customization options that the user has avoided in the past and provides customization options that exclude those options. Furthermore, the customization unit can select the optimal customization method based on the user's past customization history. For example, the customization unit analyzes the user's past customization history and selects the optimal customization method. This enables more appropriate customization by referring to the user's past customization history.

[0110] During customization, the customization unit can adjust the customization content based on the user's current preferences. For example, the customization unit performs customization based on the appearance and voice of a character currently preferred by the user. For example, the customization unit performs customization using an image and voice of a character currently preferred by the user. The customization unit can also provide customization content related to topics in which the user is currently interested. For example, the customization unit provides customization options related to topics in which the user is currently interested. Furthermore, the customization unit can adjust the customization content based on the user's current living situation. For example, the customization unit analyzes the user's current living situation and adjusts the customization content based on the analysis. This allows for more appropriate customization by adjusting the customization content based on the user's current preferences.

[0111] The customization unit can estimate the user's emotions and determine customization priorities based on the estimated user emotions. The customization unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the customization unit can analyze the user's facial expressions captured by a camera to estimate the emotions. The customization unit can also estimate the user's emotions using voice analysis technology. For example, the customization unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the customization unit can determine the customization priorities based on the user's emotions. For example, the customization unit can prioritize detailed customization options when the user is relaxed. Furthermore, the customization unit can prioritize concise customization options when the user is nervous. Furthermore, the customization unit can prioritize visually stimulating customization options when the user is excited. This enables more appropriate customization by determining the customization priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0112] During customization, the customization unit can select the optimal customization method by taking into account the user's geographical location information. For example, if the user is in a specific city, the customization unit provides customization content related to the city. For example, the customization unit provides customization content related to the city based on the user's current location information. Furthermore, if the user is in a specific country, the customization unit can also provide customization content related to the country. For example, the customization unit provides customization content related to the country based on the user's current location information. Furthermore, if the user is in a specific region, the customization unit can also provide customization content related to the region. For example, the customization unit provides customization content related to the region based on the user's current location information. This enables more appropriate customization by taking into account the user's geographical location information.

[0113] During customization, the customization unit can analyze the user's social media activity and suggest customization content. The customization unit, for example, provides customization content related to accounts the user follows on social media. For example, the customization unit analyzes the content of posts from the accounts the user follows and provides the related customization content. The customization unit can also provide customization content related to posts the user has "liked" on social media. For example, the customization unit analyzes the content of posts the user has "liked" and provides the related customization content. The customization unit can also provide customization content related to posts the user has shared on social media. For example, the customization unit analyzes the content of posts the user has shared and provides the related customization content. This enables more appropriate customization by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the above-described elements, including the collection unit, analysis unit, provision unit, consent acquisition unit, customization unit, and emotion estimation unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user information using the camera 42 and microphone 38B of the smart device 14 and transmits the collected information to the data processing device 12 via the control unit 46A. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information and generates English conversation content tailored to the user's preferences and daily routine. The provision unit, implemented, for example, by the control unit 46A of the smart device 14, provides the generated English conversation content via audio. The consent acquisition unit, implemented, for example, by the control unit 46A of the smart device 14, obtains user consent using a pop-up notification or a checkbox. The customization unit, implemented, for example, by the control unit 46A of the smart device 14, customizes the AI ​​robot by uploading images and voices of characters the user likes. The emotion estimation means estimates the user's emotion using, for example, the camera 42 or the microphone 38B of the smart device 14, and adjusts the timing of collecting information using the control unit 46A. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, consent acquisition means, customization means, and emotion estimation means, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected information to the data processing device 12 via the control unit 46A. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information and generates English conversation content tailored to the user's preferences and daily routine. The provision unit, implemented, for example, by the control unit 46A of the smart glasses 214, provides the generated English conversation content via audio. The consent acquisition means, implemented, for example, by the control unit 46A of the smart glasses 214, obtains user consent using a pop-up notification or a checkbox. The customization means, implemented, for example, by the control unit 46A of the smart glasses 214, allows the user to customize the AI ​​robot by uploading images and voices of their favorite characters. The emotion estimation means estimates the user's emotion using, for example, the camera 42 and microphone 238 of the smart glasses 214, and adjusts the timing of collecting information using the control unit 46A. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, consent acquisition means, customization means, and emotion estimation means, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the collected information to the data processing device 12 by the control unit 46A. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to generate English conversation content tailored to the user's preferences and daily routine. The provision unit, for example, is realized by the control unit 46A of the headset-type terminal 314 and provides the generated English conversation content by voice. The consent acquisition means, for example, is realized by the control unit 46A of the headset-type terminal 314 and obtains user consent using a pop-up notification or a checkbox. The customization means, for example, is realized by the control unit 46A of the headset-type terminal 314 and allows the user to customize the AI ​​robot by uploading images and voices of their favorite characters. The emotion estimation means estimates the user's emotion using, for example, the camera 42 and microphone 238 of the headset terminal 314, and adjusts the timing of collecting information by the control unit 46A. === Hard Collateral 1-4 === Each of the above-described elements, including the collection unit, analysis unit, provision unit, consent acquisition unit, customization unit, and emotion estimation unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the robot 414 and transmits the collected information to the data processing device 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information, and generates English conversation content tailored to the user's preferences and daily routine. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and provides the generated English conversation content via audio. The consent acquisition unit is implemented, for example, by the control unit 46A of the robot 414, and obtains user consent using a pop-up notification or a checkbox. The customization unit is implemented, for example, by the control unit 46A of the robot 414, and allows the user to customize the AI ​​robot by uploading images and voices of their favorite characters. The emotion estimation means estimates the user's emotion using, for example, the camera 42 and microphone 238 of the robot 414, and adjusts the timing of collecting information by the control unit 46A.

[0114] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0115] The analysis unit can also estimate the user's emotions and generate English conversation content based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can generate English conversation content that is relaxed. If the user is nervous, the analysis unit can also generate English conversation content that is simple and reassuring. Furthermore, if the user is excited, the analysis unit can also generate English conversation content that allows the user to share the excitement. In this way, by generating English conversation content that corresponds to the user's emotions, more effective English conversation practice can be provided.

[0116] The collection unit may also collect the user's health data. For example, the collection unit may collect heart rate, step count, and sleep data from a smartwatch or fitness tracker. The collection unit may also collect the user's food records and exercise records. Furthermore, the collection unit may collect data from devices that measure the user's stress level and energy level. This allows the system to generate English conversation content based on the user's health status.

[0117] The analysis unit can analyze the user's health data and generate English conversation content based on the user's health condition. For example, the analysis unit can analyze the user's heart rate and stress level and generate relaxing English conversation content. The analysis unit can also analyze the user's sleep data and generate English conversation content including sleep advice. Furthermore, the analysis unit can analyze the user's food records and generate English conversation content related to healthy eating. This can support a healthier lifestyle by generating English conversation content based on the user's health condition.

[0118] The providing unit can estimate the user's emotions and adjust the method of providing English conversation based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide English conversation in a soft tone. If the user is nervous, the providing unit can also provide English conversation in a concise and clear tone. Furthermore, if the user is excited, the providing unit can also provide English conversation in an energetic tone. In this way, by adjusting the method of providing English conversation according to the user's emotions, more effective English conversation practice can be provided.

[0119] The English conversation support system can analyze the user's learning history and adjust the content of the English conversation based on the user's learning progress. For example, the English conversation support system can generate new English conversation content based on words and phrases the user has learned in the past. The English conversation support system can also provide English conversation content to help the user improve areas in which they are weak. Furthermore, the English conversation support system can adjust the difficulty level of the English conversation to match the user's learning pace. This makes it possible to provide English conversation practice that is in line with the user's learning progress.

[0120] The English conversation support system can estimate the user's emotions and evaluate the learning progress based on the estimated user's emotions. For example, if the user is relaxed, the English conversation support system can evaluate the learning progress positively. If the user is nervous, the English conversation support system can also evaluate the learning progress cautiously. Furthermore, if the user is excited, the English conversation support system can also evaluate the learning progress energetically. This allows the system to provide more appropriate feedback by evaluating the learning progress according to the user's emotions.

[0121] The English conversation support system can analyze a user's social media activity and generate English conversation content based on the user's interests. For example, the English conversation support system can generate relevant English conversation content based on the content of posts from accounts the user follows. The English conversation support system can also generate interesting English conversation content based on the content of posts the user has "liked." Furthermore, the English conversation support system can generate shareable English conversation content based on the content of posts the user has shared. This makes it possible to provide English conversation practice based on the user's social media activity.

[0122] The English conversation support system can estimate the user's emotions and provide feedback on the English conversation based on the estimated user's emotions. For example, the English conversation support system can provide positive feedback if the user is relaxed. The English conversation support system can also provide encouraging feedback if the user is nervous. Furthermore, the English conversation support system can also provide energetic feedback if the user is excited. In this way, by providing feedback according to the user's emotions, it is possible to support more effective English conversation practice.

[0123] The English conversation support system can suggest English conversation practice times based on the user's lifestyle. For example, the English conversation support system can analyze the user's sleep patterns and suggest optimal practice times. The English conversation support system can also suggest efficient practice times taking into account the user's work and school schedules. Furthermore, the English conversation support system can analyze the user's free time and suggest practice times when the user is able to relax. This allows the system to provide English conversation practice that fits the user's lifestyle.

[0124] The English conversation support system can estimate the user's emotions and adjust the frequency of English conversation practice based on the estimated user's emotions. For example, the English conversation support system can increase the frequency of practice when the user is relaxed. Also, the English conversation support system can decrease the frequency of practice when the user is nervous. Furthermore, the English conversation support system can adjust the frequency of practice to provide energetic practice when the user is excited. In this way, by adjusting the frequency of practice according to the user's emotions, more effective English conversation practice can be provided.

[0125] The processing flow of the second embodiment will be briefly explained below.

[0126] Step 1: The collection unit collects user information. This information may include, for example, photos and videos, YouTube browsing history, Netflix and TVer viewing history, calendars, emails and memos, location information, and shopping purchase history (online and offline). The collection unit collects this information from smartphones and PCs. The collection unit may also collect information with the user's consent. For example, the user's consent may be obtained using a pop-up notification or a checkbox. Step 2: The analysis unit analyzes the information collected by the collection unit and generates English conversation content tailored to the user's preferences and daily routine. The analysis unit analyzes the information using data mining and machine learning algorithms and can adjust the English conversation content to suit the user's conversation level and vocabulary. For example, it generates content tailored to the user's level, from simple English conversation for beginners to advanced English conversation for advanced learners. Step 3: The providing unit provides the English conversation content generated by the analyzing unit as audio. The providing unit can also generate the English conversation content as audio using speech synthesis technology and provide it through an AI robot. For example, a user can create an AI robot with the appearance and voice of their favorite character, and the robot can speak the English conversation.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0132] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0178] 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.

[0179] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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).

[0184] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0185] 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."

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] [Explanation of symbols]

[0199] 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 collection unit that collects user information; an analysis unit that analyzes the information collected by the collection unit and generates English conversation content that matches the user's preferences and daily routine; a providing unit that provides the content of the English conversation generated by the analyzing unit in audio form. A system characterized by:

2. The collecting unit Collects information such as user photos and videos, YouTube browsing history, Netflix and Tver viewing history, calendar, emails and memos, location information, and shopping purchase history (online and offline) 2. The system of claim 1.

3. The analysis unit Based on collected information, the system understands the user's preferences and daily life, and generates English conversation content tailored to them.

2. The system of claim 1.

4. The providing unit Speak the generated English conversation to the user 2. The system of claim 1.

5. Provide a means to obtain user consent 2. The system of claim 1.

6. Equipped with a customization section for customizing AI robots 2. The system of claim 1.

7. The customization unit Customize your AI robot by uploading images and voices of characters of your choice. The system of claim 6 .

8. The collecting unit By using a means for estimating a user's emotion, the timing of collecting information is adjusted based on the estimated user's emotion.

2. The system of claim 1.

9. The collecting unit Analyze the user's past information collection history and select the optimal collection method 2. The system of claim 1.

Images