System

Abstract

An object of a system according to an embodiment is to provide an opportunity to use a foreign language in daily life and construct an effective learning environment.SOLUTION: A system according to an embodiment includes a virtual foreigner generation unit, a message transmission unit, and a difficulty level setting unit. The virtual foreigner generation section generates a virtual foreigner using the generated AI. The message transmission unit transmits a foreign language message from the virtual foreigner generated by the virtual foreigner generation unit to the user in daily life. The difficulty level setting unit sets the difficulty level of the conversation in accordance with the foreign language level of the user.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 faced the challenge of providing an effective learning environment because there are few opportunities to use foreign languages ​​in daily life.

[0005] The system according to the embodiment aims to provide opportunities to use a foreign language in daily life and to create an effective learning environment. [Means for solving the problem]

[0006] The system according to the embodiment includes a virtual foreigner generation unit, a message sending unit, and a difficulty setting unit. The virtual foreigner generation unit generates a virtual foreigner using a generation AI. The message sending unit allows the virtual foreigner generated by the virtual foreigner generation unit to send messages in a foreign language to the user in everyday life. The difficulty setting unit sets the difficulty of the conversation depending on the user's foreign language level. [Effects of the Invention]

[0007] The system according to the embodiment provides an opportunity to use a foreign language in daily life, and can create an effective learning environment. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) A foreign language learning app according to an embodiment of the present invention is a system that uses a generation AI to create virtual foreigners and provide users with opportunities to use foreign languages ​​in their daily lives. This allows users to create a learning environment similar to studying abroad while still in Japan, thereby promoting the improvement of their foreign language skills.

[0029] A foreign language learning app according to an embodiment includes a virtual alien generation unit, a message sending unit, and a difficulty setting unit. The virtual alien generation unit generates a virtual alien using a generation AI. For example, the generation AI generates a virtual alien that speaks a specific language based on a user's prompt. The generation AI can also customize the virtual alien character based on the user's hobbies and interests. The generation AI can also generate a virtual alien's reaction based on the user's emotions. For example, if a user inputs a prompt such as "I want to learn French," the generation AI generates a virtual alien that speaks French and starts a conversation with the user. Based on the user's hobbies and interests, the generation AI generates a virtual alien that is knowledgeable about sports for a sports-loving user. The generation AI estimates the user's emotions, and if the user is happy, the virtual alien also expresses joy. The message sending unit causes the virtual alien generated by the virtual alien generation unit to send a foreign language message to the user in daily life. For example, the message sending unit sends a foreign language message in accordance with the user's daily rhythm. The message sending unit can also generate conversations on topics based on the user's lifestyle and interests. The message sending unit can also use the emotion estimation function to provide encouragement and advice based on the user's emotions. For example, the message sending unit works with the user's calendar or schedule app to send messages in a foreign language at appropriate times. The message sending unit provides sports-related conversations based on the user's hobbies and interests. The message sending unit analyzes the user's emotions in real time and suggests relaxation methods when the user is tired. The difficulty setting unit sets the difficulty of the conversations based on the user's foreign language level. For example, the difficulty setting unit analyzes the user's learning progress in real time and automatically adjusts the difficulty to an appropriate level. The difficulty setting unit can also generate an individual learning plan based on the user's past conversation history. The difficulty setting unit can also use the emotion estimation function to adjust the difficulty level based on the user's stress level. For example, the difficulty setting unit analyzes the user's learning progress in real time and, when the user learns a new word, provides a conversation containing that word.The difficulty setting unit evaluates the user's learning progress based on the user's conversation history and proposes an appropriate learning plan. The difficulty setting unit analyzes the user's emotions in real time and provides conversations of a difficulty level corresponding to the user's stress level. As a result, the foreign language learning app according to the embodiment provides the user with opportunities to use a foreign language in their daily lives, creating a learning environment similar to that of studying abroad. For example, by replying to messages in a foreign language sent by the generation AI, the user can develop the habit of using the foreign language on a daily basis. Furthermore, the user can progress in learning at a level appropriate to their level according to the difficulty level of the conversations provided by the generation AI.

[0030] The virtual alien generation unit can generate a virtual alien that speaks a specific language based on a user's prompts. The virtual alien generation unit customizes the virtual alien character based on the user's hobbies and interests, for example. For example, for a user who likes sports, a virtual alien that is knowledgeable about sports is generated. The virtual alien generation unit also sets the virtual alien's personality and background in detail to generate a character that the user can relate to. For example, for a user who likes traveling, a virtual alien that travels around the world is set. The virtual alien generation unit also customizes the virtual alien's appearance and speaking style based on the user's profile information. For example, for a young user, a virtual alien of the same age is generated to create a sense of familiarity. In this way, a virtual alien that speaks a language the user wants to learn can be generated.

[0031] The message sending unit can send messages in a foreign language in accordance with the user's lifestyle. The message sending unit, for example, works in conjunction with the user's calendar or schedule app to send messages in a foreign language at appropriate times. For example, a message is sent during the morning commute. The message sending unit also builds a system that sends messages in a foreign language in accordance with the user's lifestyle. For example, a message is sent during lunch break or before bed. The message sending unit also sends messages in a foreign language related to specific events or plans based on the user's schedule. For example, a message containing business terms is sent before a meeting. In this way, messages in a foreign language can be sent in accordance with the user's lifestyle.

[0032] The difficulty setting unit can analyze the user's learning progress in real time and automatically adjust the difficulty level to an appropriate level. The difficulty setting unit, for example, builds a system that analyzes the user's learning progress in real time and automatically generates conversations of an appropriate level of difficulty. For example, when a user learns a new word, it provides a conversation that includes that word. The difficulty setting unit also evaluates the learning progress based on the user's past conversation history and adjusts the difficulty level. For example, it provides a conversation that includes grammar that the user finds difficult. The difficulty setting unit also collects the user's learning data in real time and develops a system that dynamically adjusts the difficulty level. For example, as the user improves, the difficulty level of the conversation is gradually increased. This makes it possible to automatically adjust the difficulty level of the conversation according to the user's learning progress.

[0033] The virtual alien generation unit can customize the virtual alien character based on the user's hobbies or interests. The virtual alien generation unit customizes the virtual alien character based on, for example, the user's hobbies and interests. For example, for a user who likes sports, a virtual alien who is knowledgeable about sports is generated. The virtual alien generation unit also sets the personality and background of the virtual alien in detail to generate a character that the user can relate to. For example, for a user who likes traveling, a virtual alien who travels around the world is set. The virtual alien generation unit also customizes the appearance and speaking style of the virtual alien based on the user's profile information. For example, for a young user, a virtual alien of the same age is generated to create a sense of familiarity. This allows the virtual alien character to be customized based on the user's hobbies and interests.

[0034] The virtual alien generation unit can generate multiple virtual aliens, allowing the user to converse with different characters simultaneously. The virtual alien generation unit, for example, provides a function that allows the user to converse with multiple virtual aliens simultaneously. For example, it generates a scenario in which different characters take turns speaking. The virtual alien generation unit also allows the user to enjoy a variety of conversations by having multiple virtual aliens converse on different topics. For example, one person talks about travel, while another talks about cooking. The virtual alien generation unit also provides a function that allows the user to converse with multiple virtual aliens in a group chat format. For example, the virtual aliens lead the conversation, and the user participates. This allows the user to converse with multiple virtual aliens simultaneously.

[0035] The message sending unit can provide communication using images and videos in addition to messages in a foreign language. The message sending unit, for example, builds a system that sends related images and videos in addition to messages in a foreign language. For example, sending photos and videos of food along with recipes. The message sending unit also provides a function that allows users to reply to messages in a foreign language with images and videos. For example, sending photos of travel destinations can continue a conversation about travel. The message sending unit also provides teaching materials using images and videos to support learning of a foreign language. For example, sending videos for pronunciation practice or images showing the meaning of words. This makes it possible to provide communication using images and videos in addition to messages in a foreign language.

[0036] The message sending unit can automatically generate messages according to different time periods or situations, and adapt to the user's lifestyle. The message sending unit automatically generates foreign language messages appropriate for different time periods to suit the user's lifestyle. For example, it sends a greeting in the morning and a good night message in the evening. The message sending unit also builds a system that automatically generates foreign language messages according to specific situations. For example, it sends route guidance phrases when the user is out. The message sending unit also sends foreign language messages at appropriate times based on the user's schedule and activities. For example, it provides conversations about food at mealtimes. This allows messages to be automatically generated according to different time periods and situations, and adapted to the user's lifestyle.

[0037] The difficulty level setting unit can generate an individualized study plan based on the user's past conversation history. The difficulty level setting unit, for example, analyzes the user's past conversation history and builds a system that generates an individualized study plan. For example, it provides a plan that focuses on studying grammar or vocabulary that the user finds difficult. The difficulty level setting unit also evaluates the user's learning progress based on the user's conversation history and suggests an appropriate study plan. For example, it provides conversations that include expressions that the user frequently makes mistakes with. The difficulty level setting unit also analyzes the user's past conversation data and develops a system that sets individual study goals. For example, if the user is interested in a particular topic, it provides conversations related to that topic. This makes it possible to generate an individualized study plan based on the user's past conversation history.

[0038] The difficulty setting unit can provide conversations that focus on specific skills. For example, the difficulty setting unit builds a system that provides conversations that focus on specific skills in accordance with the user's learning goals. For example, a conversation is provided to improve listening skills. The difficulty setting unit also generates conversations specialized for specific skills based on the user's learning data. For example, a dialogue-style conversation is provided to improve speaking skills. The difficulty setting unit also develops a system that evaluates the user's learning progress and suggests a learning plan that focuses on specific skills. For example, a conversation including reading comprehension questions is provided to improve reading skills. This makes it possible to provide conversations that focus on specific skills.

[0039] The difficulty setting unit can generate conversations based on a specific theme selected by the user. The difficulty setting unit, for example, builds a system that generates conversations that arouse interest based on a specific theme selected by the user. For example, it provides conversations about movies selected by the user. The difficulty setting unit also automatically generates conversations related to a specific theme to arouse the interest of the user. For example, it provides conversations about sports selected by the user. The difficulty setting unit also develops a system that provides conversations that arouse interest based on a theme selected by the user. For example, it provides conversations about travel destinations selected by the user. This makes it possible to generate conversations based on a specific theme selected by the user.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The foreign language learning app may further include a pronunciation evaluation unit that evaluates the user's pronunciation. The pronunciation evaluation unit analyzes the user's pronunciation and evaluates its accuracy and fluency. For example, when the user pronounces a specific word or phrase, it displays a score indicating how closely the pronunciation sounds to a native speaker. The pronunciation evaluation unit can also identify weak points in the user's pronunciation and provide specific advice for improvement. For example, if the user has difficulty pronouncing a specific sound, it can suggest exercises to practice that sound. The pronunciation evaluation unit can also record the user's pronunciation progress and visualize growth by comparing it with past data. This allows the user to objectively evaluate their pronunciation skills and effectively improve them.

[0042] A foreign language learning app may further include a culture introduction section. The culture introduction section provides information about the culture of the country or region where the user is learning the language. For example, it may provide explanations about specific holidays or traditional events. The culture introduction section may also select cultural topics that the user may be interested in and provide related articles and videos. For example, it may provide content on themes such as cooking, music, and movies. The culture introduction section may also provide information about the history and geography of the country or region where the user is learning the language. This allows the user to gain a deeper understanding not only of the language but also of the culture behind it.

[0043] A foreign language learning app may further include a learning section that incorporates game elements. The learning section provides game-style quizzes and puzzles so that users can learn while having fun. For example, it may provide a quiz to guess the meaning of a word or a puzzle to select the correct usage of grammar. The learning section may also include a ranking function that allows users to compete with other users. For example, it may display a ranking based on the number of correct answers or scores in a quiz, and reward users who rank highly. The learning section may also provide a function that allows users to earn badges or titles according to the goals they achieve. This allows users to learn in a fun, game-like manner.

[0044] Foreign language learning apps can also be equipped with customization features tailored to a user's learning style. The customization feature allows users to set up a study plan that suits their learning style and pace. For example, if a user prefers visual learning, the app can provide learning materials that make extensive use of images and videos. The customization feature can also create a study plan that focuses on specific skills or topics according to the user's learning goals. For example, a user who wants to improve their listening skills can be provided with learning materials specifically tailored to listening. The customization feature can also monitor the user's learning progress in real time and adjust the study plan as needed. This allows users to study effectively in a way that best suits them.

[0045] The foreign language learning app may further include a feedback unit that analyzes the user's learning data and provides feedback to maximize the effectiveness of learning. The feedback unit analyzes the user's learning data and evaluates the user's learning progress and results. For example, it may identify which vocabulary or grammar the user is weak at and provide advice on focusing on those areas. The feedback unit may also suggest specific improvements to maximize the effectiveness of the user's learning. For example, it may suggest ways to adjust the learning pace or effective review methods. The feedback unit may also visualize the user's learning results and provide feedback to increase motivation. This allows the user to understand their learning progress and study effectively.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The virtual alien generation unit generates a virtual alien using the generation AI. For example, the generation AI generates a virtual alien that speaks a specific language based on a user's prompt. The generation AI can also customize the virtual alien's character based on the user's hobbies and interests. Furthermore, the generation AI can generate reactions of the virtual alien according to the user's emotions. Step 2: The message sending unit causes the virtual foreigner generated by the virtual foreigner generation unit to send messages in a foreign language to the user in their daily lives. For example, the message sending unit sends messages in a foreign language in accordance with the user's daily rhythm. The message sending unit can also generate conversations on topics based on the user's lifestyle habits and interests. Furthermore, the message sending unit can provide encouragement or advice according to the user's emotions using an emotion estimation function. Step 3: The difficulty setting unit sets the difficulty of the conversation according to the user's foreign language level. For example, the difficulty setting unit analyzes the user's learning progress in real time and automatically adjusts the difficulty to an appropriate level. The difficulty setting unit can also generate an individual learning plan based on the user's past conversation history. Furthermore, the difficulty setting unit can also adjust the difficulty according to the user's stress level using an emotion estimation function.

[0048] (Example 2) A foreign language learning app according to an embodiment of the present invention is a system that uses a generation AI to create virtual foreigners and provide users with opportunities to use foreign languages ​​in their daily lives. This allows users to create a learning environment similar to studying abroad while still in Japan, thereby promoting the improvement of their foreign language skills.

[0049] A foreign language learning app according to an embodiment includes a virtual alien generation unit, a message sending unit, and a difficulty setting unit. The virtual alien generation unit generates a virtual alien using a generation AI. For example, the generation AI generates a virtual alien that speaks a specific language based on a user's prompt. The generation AI can also customize the virtual alien character based on the user's hobbies and interests. The generation AI can also generate a virtual alien's reaction based on the user's emotions. For example, if a user inputs a prompt such as "I want to learn French," the generation AI generates a virtual alien that speaks French and starts a conversation with the user. Based on the user's hobbies and interests, the generation AI generates a virtual alien that is knowledgeable about sports for a sports-loving user. The generation AI estimates the user's emotions, and if the user is happy, the virtual alien also expresses joy. The message sending unit causes the virtual alien generated by the virtual alien generation unit to send a foreign language message to the user in daily life. For example, the message sending unit sends a foreign language message in accordance with the user's daily rhythm. The message sending unit can also generate conversations on topics based on the user's lifestyle and interests. The message sending unit can also use the emotion estimation function to provide encouragement and advice based on the user's emotions. For example, the message sending unit works with the user's calendar or schedule app to send messages in a foreign language at appropriate times. The message sending unit provides sports-related conversations based on the user's hobbies and interests. The message sending unit analyzes the user's emotions in real time and suggests relaxation methods when the user is tired. The difficulty setting unit sets the difficulty of the conversations based on the user's foreign language level. For example, the difficulty setting unit analyzes the user's learning progress in real time and automatically adjusts the difficulty to an appropriate level. The difficulty setting unit can also generate an individual learning plan based on the user's past conversation history. The difficulty setting unit can also use the emotion estimation function to adjust the difficulty level based on the user's stress level. For example, the difficulty setting unit analyzes the user's learning progress in real time and, when the user learns a new word, provides a conversation containing that word.The difficulty setting unit evaluates the user's learning progress based on the user's conversation history and proposes an appropriate learning plan. The difficulty setting unit analyzes the user's emotions in real time and provides conversations of a difficulty level corresponding to the user's stress level. As a result, the foreign language learning app according to the embodiment provides the user with opportunities to use a foreign language in their daily lives, creating a learning environment similar to that of studying abroad. For example, by replying to messages in a foreign language sent by the generation AI, the user can develop the habit of using the foreign language on a daily basis. Furthermore, the user can progress in learning at a level appropriate to their level according to the difficulty level of the conversations provided by the generation AI.

[0050] The virtual alien generation unit can generate a virtual alien that speaks a specific language based on a user's prompts. The virtual alien generation unit customizes the virtual alien character based on the user's hobbies and interests, for example. For example, for a user who likes sports, a virtual alien that is knowledgeable about sports is generated. The virtual alien generation unit also sets the virtual alien's personality and background in detail to generate a character that the user can relate to. For example, for a user who likes traveling, a virtual alien that travels around the world is set. The virtual alien generation unit also customizes the virtual alien's appearance and speaking style based on the user's profile information. For example, for a young user, a virtual alien of the same age is generated to create a sense of familiarity. In this way, a virtual alien that speaks a language the user wants to learn can be generated.

[0051] The message sending unit can send messages in a foreign language in accordance with the user's lifestyle. The message sending unit, for example, works in conjunction with the user's calendar or schedule app to send messages in a foreign language at appropriate times. For example, a message is sent during the morning commute. The message sending unit also builds a system that sends messages in a foreign language in accordance with the user's lifestyle. For example, a message is sent during lunch break or before bed. The message sending unit also sends messages in a foreign language related to specific events or plans based on the user's schedule. For example, a message containing business terms is sent before a meeting. In this way, messages in a foreign language can be sent in accordance with the user's lifestyle.

[0052] The difficulty setting unit can analyze the user's learning progress in real time and automatically adjust the difficulty level to an appropriate level. The difficulty setting unit, for example, builds a system that analyzes the user's learning progress in real time and automatically generates conversations of an appropriate level of difficulty. For example, when a user learns a new word, it provides a conversation that includes that word. The difficulty setting unit also evaluates the learning progress based on the user's past conversation history and adjusts the difficulty level. For example, it provides a conversation that includes grammar that the user finds difficult. The difficulty setting unit also collects the user's learning data in real time and develops a system that dynamically adjusts the difficulty level. For example, as the user improves, the difficulty level of the conversation is gradually increased. This makes it possible to automatically adjust the difficulty level of the conversation according to the user's learning progress.

[0053] The virtual alien generation unit can customize the virtual alien character based on the user's hobbies or interests. The virtual alien generation unit customizes the virtual alien character based on, for example, the user's hobbies and interests. For example, for a user who likes sports, a virtual alien who is knowledgeable about sports is generated. The virtual alien generation unit also sets the personality and background of the virtual alien in detail to generate a character that the user can relate to. For example, for a user who likes traveling, a virtual alien who travels around the world is set. The virtual alien generation unit also customizes the appearance and speaking style of the virtual alien based on the user's profile information. For example, for a young user, a virtual alien of the same age is generated to create a sense of familiarity. This allows the virtual alien character to be customized based on the user's hobbies and interests.

[0054] The virtual alien generation unit can use the emotion estimation function to generate a reaction of the virtual alien according to the user's emotion. The virtual alien generation unit, for example, estimates the emotion from the user's input content and generates a reaction of the virtual alien according to that emotion. For example, if the user is happy, the virtual alien also expresses joy. Furthermore, the virtual alien generation unit uses the emotion estimation function to have the virtual alien offer words of encouragement if the user is feeling stressed. For example, if the user is tired, the virtual alien generation unit suggests ways to relax. Furthermore, the virtual alien generation unit changes the tone and expression of the virtual alien according to the user's emotion. For example, if the user is sad, the virtual alien speaks in a gentle tone. In this way, a reaction of the virtual alien according to the user's emotion can be generated.

[0055] The virtual alien generation unit can generate multiple virtual aliens, allowing the user to converse with different characters simultaneously. The virtual alien generation unit, for example, provides a function that allows the user to converse with multiple virtual aliens simultaneously. For example, it generates a scenario in which different characters take turns speaking. The virtual alien generation unit also allows the user to enjoy a variety of conversations by having multiple virtual aliens converse on different topics. For example, one person talks about travel, while another talks about cooking. The virtual alien generation unit also provides a function that allows the user to converse with multiple virtual aliens in a group chat format. For example, the virtual aliens lead the conversation, and the user participates. This allows the user to converse with multiple virtual aliens simultaneously.

[0056] The virtual alien generation unit can automatically select a virtual alien character according to the user's emotions. For example, the virtual alien generation unit analyzes the user's emotions in real time and automatically selects a virtual alien character that best suits those emotions. For example, if the user is depressed, a character that is good at encouraging them is selected. The virtual alien generation unit also uses an emotion estimation function to build a system that suggests a character according to the user's emotions. For example, if the user is excited, a similarly energetic character is selected. The virtual alien generation unit also develops a system that dynamically changes the optimal virtual alien based on the user's emotion data. For example, the character is switched every time the user's emotions change. This makes it possible to automatically select a virtual alien character according to the user's emotions.

[0057] The message sending unit can use the emotion estimation function to provide encouragement and advice according to the user's emotions. The message sending unit, for example, analyzes the user's emotions in real time and provides encouragement and advice in a foreign language according to the emotions. For example, if the user is tired, the message sending unit suggests relaxation methods. Furthermore, the message sending unit uses the emotion estimation function to provide advice in a foreign language for relieving stress if the user is feeling stressed. For example, the message sending unit recommends deep breathing or light exercise. Furthermore, the message sending unit transmits a message in a foreign language to elicit positive emotions based on the user's emotion data. For example, if the user is feeling down, the message sending unit sends words of encouragement. In this way, encouragement and advice according to the user's emotions can be provided.

[0058] The message sending unit can provide communication using images and videos in addition to messages in a foreign language. The message sending unit, for example, builds a system that sends related images and videos in addition to messages in a foreign language. For example, sending photos and videos of food along with recipes. The message sending unit also provides a function that allows users to reply to messages in a foreign language with images and videos. For example, sending photos of travel destinations can continue a conversation about travel. The message sending unit also provides teaching materials using images and videos to support learning of a foreign language. For example, sending videos for pronunciation practice or images showing the meaning of words. This makes it possible to provide communication using images and videos in addition to messages in a foreign language.

[0059] The message sending unit can automatically generate messages according to different time periods or situations, and adapt to the user's lifestyle. The message sending unit automatically generates foreign language messages appropriate for different time periods to suit the user's lifestyle. For example, it sends a greeting in the morning and a good night message in the evening. The message sending unit also builds a system that automatically generates foreign language messages according to specific situations. For example, it sends route guidance phrases when the user is out. The message sending unit also sends foreign language messages at appropriate times based on the user's schedule and activities. For example, it provides conversations about food at mealtimes. This allows messages to be automatically generated according to different time periods and situations, and adapted to the user's lifestyle.

[0060] The message sending unit can provide daily life advice in accordance with the user's emotions using the emotion estimation function. The message sending unit, for example, analyzes the user's emotions in real time and provides daily life advice in a foreign language in accordance with the emotions. For example, if the user is tired, the message sending unit suggests relaxation methods. Furthermore, if the user is feeling stressed, the message sending unit can use the emotion estimation function to provide stress relief advice in the foreign language. For example, deep breathing or light exercise can be recommended. Furthermore, the message sending unit can send a message in the foreign language to elicit positive emotions based on the user's emotion data. For example, if the user is feeling down, words of encouragement can be sent. In this way, daily life advice can be provided in accordance with the user's emotions.

[0061] The difficulty level setting unit can generate an individualized study plan based on the user's past conversation history. The difficulty level setting unit, for example, analyzes the user's past conversation history and builds a system that generates an individualized study plan. For example, it provides a plan that focuses on studying grammar or vocabulary that the user finds difficult. The difficulty level setting unit also evaluates the user's learning progress based on the user's conversation history and suggests an appropriate study plan. For example, it provides conversations that include expressions that the user frequently makes mistakes with. The difficulty level setting unit also analyzes the user's past conversation data and develops a system that sets individual study goals. For example, if the user is interested in a particular topic, it provides conversations related to that topic. This makes it possible to generate an individualized study plan based on the user's past conversation history.

[0062] The difficulty level setting unit can use the emotion estimation function to adjust the difficulty level according to the user's stress level. The difficulty level setting unit, for example, builds a system that analyzes the user's emotions in real time and provides conversations of a difficulty level according to the stress level. For example, if the user is feeling stressed, an easy conversation is provided. The difficulty level setting unit also uses the emotion estimation function to monitor the user's stress level and adjust the difficulty level to an appropriate level. For example, if the user is relaxed, a more difficult conversation is provided. The difficulty level setting unit also develops a system that suggests a study plan according to the user's stress level based on the user's emotion data. For example, if the user is tired, a conversation on a relaxing topic is provided. This makes it possible to adjust the difficulty level according to the user's stress level.

[0063] The difficulty setting unit can provide conversations that focus on specific skills. For example, the difficulty setting unit builds a system that provides conversations that focus on specific skills in accordance with the user's learning goals. For example, a conversation is provided to improve listening skills. The difficulty setting unit also generates conversations specialized for specific skills based on the user's learning data. For example, a dialogue-style conversation is provided to improve speaking skills. The difficulty setting unit also develops a system that evaluates the user's learning progress and suggests a learning plan that focuses on specific skills. For example, a conversation including reading comprehension questions is provided to improve reading skills. This makes it possible to provide conversations that focus on specific skills.

[0064] The difficulty setting unit can generate conversations based on a specific theme selected by the user. The difficulty setting unit, for example, builds a system that generates conversations that arouse interest based on a specific theme selected by the user. For example, it provides conversations about movies selected by the user. The difficulty setting unit also automatically generates conversations related to a specific theme to arouse the interest of the user. For example, it provides conversations about sports selected by the user. The difficulty setting unit also develops a system that provides conversations that arouse interest based on a theme selected by the user. For example, it provides conversations about travel destinations selected by the user. This makes it possible to generate conversations based on a specific theme selected by the user.

[0065] The difficulty level setting unit can use the emotion estimation function to provide feedback to improve motivation according to the user's emotions. The difficulty level setting unit, for example, analyzes the user's emotions in real time and builds a system that provides feedback to improve motivation. For example, if the user is feeling down, it sends words of encouragement. The difficulty level setting unit also uses the emotion estimation function to provide feedback according to the user's emotions. For example, if the user is successful, it sends words of praise. The difficulty level setting unit also develops a system that provides feedback to improve motivation based on the user's emotion data. For example, if the user is tired, it suggests ways to relax. In this way, it is possible to provide feedback to improve motivation according to the user's emotions.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The foreign language learning app may further include a pronunciation evaluation unit that evaluates the user's pronunciation. The pronunciation evaluation unit analyzes the user's pronunciation and evaluates its accuracy and fluency. For example, when the user pronounces a specific word or phrase, it displays a score indicating how closely the pronunciation sounds to a native speaker. The pronunciation evaluation unit can also identify weak points in the user's pronunciation and provide specific advice for improvement. For example, if the user has difficulty pronouncing a specific sound, it can suggest exercises to practice that sound. The pronunciation evaluation unit can also record the user's pronunciation progress and visualize growth by comparing it with past data. This allows the user to objectively evaluate their pronunciation skills and effectively improve them.

[0068] A foreign language learning app may further include a culture introduction section. The culture introduction section provides information about the culture of the country or region where the user is learning the language. For example, it may provide explanations about specific holidays or traditional events. The culture introduction section may also select cultural topics that the user may be interested in and provide related articles and videos. For example, it may provide content on themes such as cooking, music, and movies. The culture introduction section may also provide information about the history and geography of the country or region where the user is learning the language. This allows the user to gain a deeper understanding not only of the language but also of the culture behind it.

[0069] A foreign language learning app may further include a learning section that incorporates game elements. The learning section provides game-style quizzes and puzzles so that users can learn while having fun. For example, it may provide a quiz to guess the meaning of a word or a puzzle to select the correct usage of grammar. The learning section may also include a ranking function that allows users to compete with other users. For example, it may display a ranking based on the number of correct answers or scores in a quiz, and reward users who rank highly. The learning section may also provide a function that allows users to earn badges or titles according to the goals they achieve. This allows users to learn in a fun, game-like manner.

[0070] Foreign language learning apps can also be equipped with customization features tailored to a user's learning style. The customization feature allows users to set up a study plan that suits their learning style and pace. For example, if a user prefers visual learning, the app can provide learning materials that make extensive use of images and videos. The customization feature can also create a study plan that focuses on specific skills or topics according to the user's learning goals. For example, a user who wants to improve their listening skills can be provided with learning materials specifically tailored to listening. The customization feature can also monitor the user's learning progress in real time and adjust the study plan as needed. This allows users to study effectively in a way that best suits them.

[0071] The foreign language learning app may further include a feedback unit that analyzes the user's learning data and provides feedback to maximize the effectiveness of learning. The feedback unit analyzes the user's learning data and evaluates the user's learning progress and results. For example, it may identify which vocabulary or grammar the user is weak at and provide advice on focusing on those areas. The feedback unit may also suggest specific improvements to maximize the effectiveness of the user's learning. For example, it may suggest ways to adjust the learning pace or effective review methods. The feedback unit may also visualize the user's learning results and provide feedback to increase motivation. This allows the user to understand their learning progress and study effectively.

[0072] The foreign language learning app may further include an emotion adjustment unit that adjusts learning content based on the user's emotions. The emotion adjustment unit analyzes the user's emotions in real time and provides learning content that corresponds to the user's emotions. For example, if the user is tired, it provides learning content that will help the user relax. The emotion adjustment unit may also provide learning content to reduce stress if the user is feeling stressed. For example, it may provide simple conversation or listening practice. The emotion adjustment unit may also adjust the pace of learning according to the user's emotions. For example, if the user is lacking concentration, it may provide learning content that can be completed in a short amount of time. This allows the user to study in an optimal learning environment that corresponds to their emotions.

[0073] Foreign language learning apps can also include elements that increase a user's motivation to learn based on their emotions. For example, if a user has positive emotions toward learning, the app can send praise and encouraging messages. On the other hand, if a user has negative emotions toward learning, the app can provide advice and reminders to increase motivation. For example, the app can send messages emphasizing the importance of learning and the joy of achieving goals. Emotion-based motivational elements can also provide individually customized feedback based on the user's emotional data. This allows users to progress in their studies while receiving optimal support according to their emotions.

[0074] The foreign language learning app can further include an element that evaluates the learning progress based on the user's emotions. For example, if the user has positive emotions about learning, the learning progress can be evaluated in detail and specific feedback can be provided. On the other hand, if the user has negative emotions about learning, the learning progress can be evaluated briefly and an encouraging message can be sent. For example, if the user feels anxious about learning, a message reflecting on past successes can be sent. The emotion-based progress evaluation element can also provide an individually customized evaluation based on the user's emotional data. This allows the user to progress through their studies while receiving an evaluation that is optimal for their emotions.

[0075] A foreign language learning app can further include an element that sets learning goals based on the user's emotions. For example, if the user has positive emotions about learning, it can set high goals and provide challenging learning content. On the other hand, if the user has negative emotions about learning, it can set easy-to-achieve goals and provide simple learning content. For example, if the user feels anxious about learning, it can set goals that allow them to accumulate small successes. The emotion-based goal setting element can also provide individually customized goals based on the user's emotional data. This allows the user to proceed with their studies while setting optimal goals that correspond to their emotions.

[0076] A foreign language learning app can further include an element that provides learning feedback based on the user's emotions. For example, if the user has positive emotions about learning, feedback suggesting specific areas for improvement or next steps can be provided. Alternatively, if the user has negative emotions about learning, encouraging messages or feedback suggesting ways to relax can be provided. For example, if the user feels anxious about learning, a message reflecting on past successful experiences can be sent. Furthermore, emotion-based feedback elements can provide individually customized feedback based on the user's emotional data. This allows the user to progress through their studies while receiving optimal feedback according to their emotions.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: The virtual alien generation unit generates a virtual alien using the generation AI. For example, the generation AI generates a virtual alien that speaks a specific language based on a user's prompt. The generation AI can also customize the virtual alien's character based on the user's hobbies and interests. Furthermore, the generation AI can generate reactions of the virtual alien according to the user's emotions. Step 2: The message sending unit causes the virtual foreigner generated by the virtual foreigner generation unit to send messages in a foreign language to the user in their daily lives. For example, the message sending unit sends messages in a foreign language in accordance with the user's daily rhythm. The message sending unit can also generate conversations on topics based on the user's lifestyle habits and interests. Furthermore, the message sending unit can provide encouragement or advice according to the user's emotions using an emotion estimation function. Step 3: The difficulty setting unit sets the difficulty of the conversation according to the user's foreign language level. For example, the difficulty setting unit analyzes the user's learning progress in real time and automatically adjusts the difficulty to an appropriate level. The difficulty setting unit can also generate an individual learning plan based on the user's past conversation history. Furthermore, the difficulty setting unit can also adjust the difficulty according to the user's stress level using an emotion estimation function.

[0079] 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.

[0080] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0081] 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.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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).

[0088] 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.

[0089] 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.

[0090] 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.

[0091] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0092] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0093] 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.

[0094] 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.

[0095] 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 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.

[0096] 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.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] 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.

[0109] 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.

[0110] 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 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.

[0111] 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.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0124] 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.

[0125] 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.

[0126] 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 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0133] 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."

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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. [Explanation of symbols]

[0146] 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 virtual alien generation unit that generates a virtual alien using a generation AI; a message sending unit that sends a message in a foreign language to a user in daily life using the virtual foreigner generated by the virtual foreigner generating unit; a difficulty level setting unit that sets the difficulty level of the conversation according to the foreign language level of the user. A system characterized by:

2. The virtual foreigner generation unit Generating the virtual alien speaking a particular language based on a prompt from the user.

2. The system of claim 1.

3. The message sending unit Sending messages in the foreign language in accordance with the user's daily rhythm 2. The system of claim 1.

4. The difficulty level setting unit Analyze the user's learning progress in real time and automatically adjust the difficulty level to an appropriate level.

2. The system of claim 1.

5. The virtual foreigner generation unit Generate a reaction of the virtual alien according to the emotion of the user.

2. The system of claim 1.

6. The message sending unit Providing the encouragement or advice according to the user's emotions 2. The system of claim 1.

7. The difficulty level setting unit The difficulty level is adjusted according to the stress level of the user.

2. The system of claim 1.

8. The virtual foreigner generation unit Automatically selecting the virtual alien character according to the user's emotion.

2. The system of claim 1.

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