System

Abstract

An object of a system according to an embodiment is to enable efficient learning of electronic learning content in accordance with individual needs of a user.SOLUTION: A system according to an embodiment includes a content conversion unit, a voice generation unit, a summary generation unit, a difficulty level adjustment unit, a checking question generation unit, and a translation unit. The content conversion unit converts the content into a content suitable for a hobby or a favorite of the user. The voice generation unit reads aloud the content converted by the content conversion unit in the user's favorite voice. The summary generation unit summarizes the content. The difficulty level adjustment unit adjusts the difficulty level of the content. The checking question generation unit generates checking questions for checking the content. The translation unit translates the content into multiple languages.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 technologies have had the problem that it is difficult to efficiently study electronic study content in accordance with the individual needs of users.

[0005] The system according to the embodiment aims to enable users to efficiently study digitized study content in accordance with their individual needs. [Means for solving the problem]

[0006] The system according to the embodiment includes a content conversion unit, a voice generation unit, a summary generation unit, a difficulty level adjustment unit, a confirmation question generation unit, and a translation unit. The content conversion unit converts the content into content that matches the user's hobbies and interests. The voice generation unit reads the content converted by the content conversion unit in the user's preferred voice. The summary generation unit summarizes the content. The difficulty level adjustment unit adjusts the difficulty level of the content. The confirmation question generation unit generates confirmation questions for content confirmation. The translation unit translates the content into multiple languages. [Effects of the Invention]

[0007] The system according to the embodiment can enable users to efficiently study digitized study content in accordance with their individual needs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The electronic book reader according to the embodiment of the present invention is a system that converts content into content that matches the user's hobbies and interests, reads it aloud in the user's preferred voice, summarizes the content, adjusts the difficulty level, confirms the content, and translates it into multiple languages. This allows the electronic book reader to efficiently advance learning that matches the user's interests and level.

[0029] An e-book reader according to an embodiment includes a content conversion unit, a voice generation unit, a summary generation unit, a difficulty adjustment unit, a confirmation question generation unit, and a translation unit. The content conversion unit converts content into content that matches the user's hobbies and interests. For example, if the user is interested in sports, the generation AI explains the learning content using sports-related examples and metaphors. The content conversion unit can also analyze the user's past learning history, track changes in interests, and dynamically optimize the content. The voice generation unit recites the content converted by the content conversion unit in the user's preferred voice. For example, if the user prefers the voice of a particular voice actor, the generation AI can recite the content by imitating that voice actor's voice. The voice generation unit can also customize the voice to match the user's tone and pace. The summary generation unit summarizes the content. For example, by inputting instructions such as "Summarize the entire content in XX characters or less" or "Tell me more about the word XX," the generation AI generates a summary based on the instructions. The summary generation unit can also analyze the user's past summary requests and automatically select the optimal summary style. The difficulty level adjustment unit adjusts the difficulty level of the content. For example, by inputting an instruction such as "Convert the content into something that even a junior high school student can understand" to the generation AI, the generation AI simplifies the content based on the instruction. The difficulty level adjustment unit can also analyze the user's learning history and dynamically adjust the difficulty level based on the user's past comprehension. The confirmation question generation unit generates confirmation questions for confirming the content of the content. For example, by inputting an instruction such as "Provide questions to confirm the content of Chapter 1" to the generation AI, the generation AI generates confirmation questions based on the instruction. The confirmation question generation unit can also analyze the user's past answer history and automatically generate optimal confirmation questions. The translation unit translates the content into multiple languages. For example, by inputting an instruction such as "Translate the contents of this book into English" to the generation AI, the generation AI translates the content based on the instruction. The translation unit can also analyze the user's past translation history and automatically select the optimal translation style. As a result, the e-book reader according to the embodiment can efficiently promote learning tailored to the user's interests and level.For example, if a user is interested in sports, explanations using sports-related examples will deepen their understanding, and learning will be more enjoyable if the content is read aloud by a specific voice actor.In addition, by utilizing functions such as content summarization, difficulty adjustment, and multilingual translation, the scope of learning can be broadened and a deeper understanding can be achieved.

[0030] The content conversion unit can analyze the user's past learning history, track changes in interests, and dynamically optimize content. For example, the content conversion unit collects the user's past learning history and analyzes changes in interests. For example, it provides learning content tailored to current interests based on topics studied in the past and frequently accessed content. The content conversion unit also tracks changes in interests based on the user's learning history and dynamically optimizes content. For example, if a topic that the user was interested in in the past becomes popular again, it provides new content related to that topic. The content conversion unit also analyzes the learning history and develops an algorithm that predicts changes in the user's interests. For example, it suggests topics that the user is likely to be interested in in the future based on past learning patterns. This makes it possible to optimize content based on changes in the user's interests.

[0031] The content conversion unit can analyze a user's social media activity and customize content based on the user's latest interests and trends. For example, the content conversion unit analyzes a user's social media activity, such as posts and "likes," to build a system that grasps the user's latest interests and trends. For example, it collects and analyzes data from Twitter and Instagram. The content conversion unit also provides content tailored to the user's latest interests based on social media activity data. For example, it suggests learning content related to topics that the user recently "liked." The content conversion unit also monitors the user's social media activity in real time and develops an algorithm that dynamically customizes content based on that data. For example, it provides content that is sensitive to trends. This allows content to be customized based on the user's latest interests and trends.

[0032] The voice generation unit allows the generation AI to customize the voice to match the tone and pace of the user's voice. For example, the voice generation unit analyzes the tone and pace of the user's voice and builds a system in which the generation AI customizes the voice based on that data. For example, it learns and imitates the characteristics of the user's voice. The voice generation unit also develops an algorithm that allows the generation AI to dynamically adjust the voice to match the tone and pace of the user's voice. For example, it adjusts the reading speed to match the rhythm of the user's voice. The voice generation unit also generates customized voice based on the characteristics of the user's voice. For example, it generates voice to match the pitch and volume of the user's voice. This allows the voice to be customized to match the tone and pace of the user's voice.

[0033] The summary generation unit can analyze the user's past summary requests and automatically select the optimal summary style. For example, the summary generation unit collects the user's past summary requests and builds a system that automatically selects the optimal summary style based on that data. For example, it analyzes the content of past requests to determine the summary style. The summary generation unit also develops an algorithm that dynamically selects the user's most preferred summary style based on the summary request history. For example, it learns past request patterns and provides the optimal summary. The summary generation unit also analyzes the user's summary request history and automatically selects the optimal summary style. For example, it provides the optimal summary based on the frequency of requests for a specific summary format or length. This makes it possible to select the optimal summary style based on the user's past summary requests.

[0034] The summary generation unit can supplement the summary by retrieving information from external databases to improve the accuracy of the summary. For example, the summary generation unit builds a system that automatically retrieves and supplements information from related external databases to improve the accuracy of the summary. For example, it obtains related information from academic paper databases and news sites. The summary generation unit also develops an algorithm that complements the content of the summary based on information from external databases. For example, it incorporates related data and statistical information into the summary. The summary generation unit also builds a system that dynamically retrieves and supplements information from external databases to improve the accuracy of the summary. For example, it reflects the latest research results and technological trends in the summary. This makes it possible to improve the accuracy of the summary based on information from external databases.

[0035] The difficulty level adjustment unit can analyze the user's learning history and dynamically adjust the difficulty level based on the user's past level of comprehension. The difficulty level adjustment unit, for example, collects the user's learning history and builds a system that analyzes the user's past level of comprehension based on that data. For example, the level of comprehension is evaluated based on past test results and study time. The difficulty level adjustment unit also develops an algorithm that dynamically adjusts the difficulty level to match the user's level of comprehension based on the learning history. For example, the difficulty level is increased when the level of comprehension is high and decreased when the level of comprehension is low. The difficulty level adjustment unit also analyzes the user's learning history and builds a system that provides an optimal level of difficulty based on the user's past level of comprehension. For example, the difficulty level is adjusted based on the user's past learning patterns. This makes it possible to dynamically adjust the difficulty level based on the user's past level of comprehension.

[0036] The confirmation question generation unit can analyze the user's past answer history and automatically generate optimal confirmation questions. The confirmation question generation unit, for example, collects the user's past answer history and builds a system that automatically generates optimal confirmation questions based on that data. For example, confirmation questions are generated based on the user's past correct answer rate or questions that were answered incorrectly. The confirmation question generation unit also develops an algorithm that dynamically generates confirmation questions that allow the user to study most effectively based on the answer history. For example, it learns past answer patterns and provides optimal questions. The confirmation question generation unit also analyzes the user's answer history and automatically generates optimal confirmation questions. For example, it provides confirmation questions based on questions of a specific topic or difficulty level. This makes it possible to automatically generate optimal confirmation questions based on the user's past answer history.

[0037] The translation unit can analyze the user's past translation history and automatically select the optimal translation style. For example, the translation unit collects the user's past translation history and builds a system that automatically selects the optimal translation style based on that data. For example, it analyzes the content of past translations to determine the translation style. The translation unit also develops an algorithm that dynamically selects the translation style that the user most prefers based on the translation history. For example, it learns past translation patterns and provides the optimal translation. The translation unit also analyzes the user's translation history and automatically selects the optimal translation style. For example, it provides the optimal translation based on the frequency of requests for specific translation formats or phrases. This makes it possible to select the optimal translation style based on the user's past translation history.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] An e-book reader can also be equipped with a health management unit that monitors the user's health status and provides content based on that data. For example, it can analyze the user's heart rate and sleep patterns and provide relaxing content when stress levels are high. The health management unit can also provide refreshing content suitable for post-exercise activities based on the user's exercise history. Furthermore, the health management unit can analyze the user's food records and suggest study content suitable for after meals. This allows the optimal content to be provided based on the user's health status.

[0040] The e-book reader may also include a learning style analysis unit that analyzes the user's learning style and provides content based on that data. For example, if the user prefers visual learning, content that makes extensive use of diagrams and graphs may be provided. If the user prefers auditory learning, the learning style analysis unit may also provide content that makes extensive use of audio and music. Furthermore, the learning style analysis unit may analyze the user's learning pace and suggest breaks at appropriate times. This allows the e-book reader to provide optimal content based on the user's learning style.

[0041] The e-reader may also include a social network analyzer that analyzes the user's social network and provides content based on that data. For example, the e-reader may recommend books that the user's friends are reading. The social network analyzer may also provide content related to topics that the user's friends are interested in. The social network analyzer may also monitor the user's social network activity in real time and dynamically optimize content based on that data. This allows the e-reader to provide optimal content based on the user's social network.

[0042] The e-reader may also include a learning goal setting unit that sets a user's learning goals and provides content based on that data. For example, the e-reader may provide a learning plan to help the user pass a particular exam. The learning goal setting unit may also monitor the user's progress and provide advice on how to achieve the goal. The learning goal setting unit may also dynamically optimize content based on the user's learning goals. This allows the e-reader to provide the most appropriate content based on the user's learning goals.

[0043] The e-book reader may further include a learning history analysis unit that analyzes the user's learning history and provides summaries based on that data. For example, it may summarize what the user has learned in the past. The learning history analysis unit may also select an optimal summarization style based on the user's learning history. Furthermore, the learning history analysis unit may monitor the user's learning history in real time and dynamically optimize summaries based on that data. This allows the e-book reader to provide an optimal summary based on the user's learning history.

[0044] The e-book reader may further include a learning history analysis unit that analyzes the user's learning history and provides review questions based on that data. For example, review questions may be generated based on questions the user has answered incorrectly in the past. The learning history analysis unit may also select optimal review questions based on the user's learning history. Furthermore, the learning history analysis unit may monitor the user's learning history in real time and dynamically optimize the review questions based on that data. This allows the provision of optimal review questions based on the user's learning history.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The content conversion unit converts the content to match the user's hobbies and interests. For example, if the user is interested in sports, the generation AI will explain the learning content using sports-related examples and metaphors. The content conversion unit can also analyze the user's past learning history and track changes in interests to dynamically optimize the content. Step 2: The voice generation unit reads the content converted by the content conversion unit in the user's preferred voice. For example, if the user prefers the voice of a particular voice actor, the generation AI will read the content by imitating that voice actor's voice. The voice generation unit can also customize the voice to match the tone and pace of the user's voice. Step 3: The summary generator summarizes the content. For example, by inputting instructions such as "Summarize the whole thing in XX characters or less" or "Tell me more about the word XX," the AI ​​generates a summary based on those instructions. The summary generator can also analyze the user's past summary requests and automatically select the optimal summary style. Step 4: The difficulty adjustment unit adjusts the difficulty of the content. For example, by inputting instructions such as "Convert it into content that even a junior high school student can understand," the generation AI will simplify the content based on those instructions. The difficulty adjustment unit can also analyze the user's learning history and dynamically adjust the difficulty level based on their past level of comprehension. Step 5: The verification question generation unit generates verification questions to verify the content of the content. For example, by inputting an instruction such as "Provide questions to verify the content of Chapter 1" into the generation AI, the generation AI will generate verification questions based on that instruction. The verification question generation unit can also analyze the user's past response history and automatically generate optimal verification questions. Step 6: The translation department translates the content into multiple languages. For example, by inputting instructions such as "translate the contents of a book into English," the generation AI translates the content based on those instructions. The translation department can also analyze the user's past translation history and automatically select the optimal translation style.

[0047] (Example 2) The electronic book reader according to the embodiment of the present invention is a system that converts content into content that matches the user's hobbies and interests, reads it aloud in the user's preferred voice, summarizes the content, adjusts the difficulty level, confirms the content, and translates it into multiple languages. This allows the electronic book reader to efficiently advance learning that matches the user's interests and level.

[0048] An e-book reader according to an embodiment includes a content conversion unit, a voice generation unit, a summary generation unit, a difficulty adjustment unit, a confirmation question generation unit, and a translation unit. The content conversion unit converts content into content that matches the user's hobbies and interests. For example, if the user is interested in sports, the generation AI explains the learning content using sports-related examples and metaphors. The content conversion unit can also analyze the user's past learning history, track changes in interests, and dynamically optimize the content. The voice generation unit recites the content converted by the content conversion unit in the user's preferred voice. For example, if the user prefers the voice of a particular voice actor, the generation AI can recite the content by imitating that voice actor's voice. The voice generation unit can also customize the voice to match the user's tone and pace. The summary generation unit summarizes the content. For example, by inputting instructions such as "Summarize the entire content in XX characters or less" or "Tell me more about the word XX," the generation AI generates a summary based on the instructions. The summary generation unit can also analyze the user's past summary requests and automatically select the optimal summary style. The difficulty level adjustment unit adjusts the difficulty level of the content. For example, by inputting an instruction such as "Convert the content into something that even a junior high school student can understand" to the generation AI, the generation AI simplifies the content based on the instruction. The difficulty level adjustment unit can also analyze the user's learning history and dynamically adjust the difficulty level based on the user's past comprehension. The confirmation question generation unit generates confirmation questions for confirming the content of the content. For example, by inputting an instruction such as "Provide questions to confirm the content of Chapter 1" to the generation AI, the generation AI generates confirmation questions based on the instruction. The confirmation question generation unit can also analyze the user's past answer history and automatically generate optimal confirmation questions. The translation unit translates the content into multiple languages. For example, by inputting an instruction such as "Translate the contents of this book into English" to the generation AI, the generation AI translates the content based on the instruction. The translation unit can also analyze the user's past translation history and automatically select the optimal translation style. As a result, the e-book reader according to the embodiment can efficiently promote learning tailored to the user's interests and level.For example, if a user is interested in sports, explanations using sports-related examples will deepen their understanding, and learning will be more enjoyable if the content is read aloud by a specific voice actor.In addition, by utilizing functions such as content summarization, difficulty adjustment, and multilingual translation, the scope of learning can be broadened and a deeper understanding can be achieved.

[0049] The content conversion unit can analyze the user's past learning history, track changes in interests, and dynamically optimize content. For example, the content conversion unit collects the user's past learning history and analyzes changes in interests. For example, it provides learning content tailored to current interests based on topics studied in the past and frequently accessed content. The content conversion unit also tracks changes in interests based on the user's learning history and dynamically optimizes content. For example, if a topic that the user was interested in in the past becomes popular again, it provides new content related to that topic. The content conversion unit also analyzes the learning history and develops an algorithm that predicts changes in the user's interests. For example, it suggests topics that the user is likely to be interested in in the future based on past learning patterns. This makes it possible to optimize content based on changes in the user's interests.

[0050] The content conversion unit can detect the user's real-time emotional state using an emotion estimation function and provide content that matches the user's mood at that time. The content conversion unit, for example, builds a system that analyzes facial expressions and vocal tone to detect the user's real-time emotional state. For example, it collects emotional data using a camera or microphone and provides content that matches the user's mood. The content conversion unit also uses the emotion estimation function to dynamically generate content that matches the user's mood. For example, if the user is feeling stressed, it provides relaxing content. The content conversion unit also monitors the user's emotional state in real time and develops an algorithm that optimizes content based on that data. For example, it provides challenging content when the user is feeling strongly positive. This makes it possible to provide content based on the user's real-time emotional state.

[0051] The content conversion unit can analyze a user's social media activity and customize content based on the user's latest interests and trends. For example, the content conversion unit analyzes a user's social media activity, such as posts and "likes," to build a system that grasps the user's latest interests and trends. For example, it collects and analyzes data from Twitter and Instagram. The content conversion unit also provides content tailored to the user's latest interests based on social media activity data. For example, it suggests learning content related to topics that the user recently "liked." The content conversion unit also monitors the user's social media activity in real time and develops an algorithm that dynamically customizes content based on that data. For example, it provides content that is sensitive to trends. This allows content to be customized based on the user's latest interests and trends.

[0052] The voice generation unit allows the generation AI to customize the voice to match the tone and pace of the user's voice. For example, the voice generation unit analyzes the tone and pace of the user's voice and builds a system in which the generation AI customizes the voice based on that data. For example, it learns and imitates the characteristics of the user's voice. The voice generation unit also develops an algorithm that allows the generation AI to dynamically adjust the voice to match the tone and pace of the user's voice. For example, it adjusts the reading speed to match the rhythm of the user's voice. The voice generation unit also generates customized voice based on the characteristics of the user's voice. For example, it generates voice to match the pitch and volume of the user's voice. This allows the voice to be customized to match the tone and pace of the user's voice.

[0053] The voice generation unit can detect the user's emotional state using an emotion estimation function and select a voice tone that matches their mood at that time. For example, the voice generation unit can detect the user's emotional state in real time and build a system in which the generation AI selects a voice tone based on that data. For example, it can collect emotional data using a camera or microphone. The voice generation unit can also use the emotion estimation function to develop an algorithm that dynamically selects a voice tone that matches the user's mood. For example, when the user is feeling relaxed, a calm tone is selected. The voice generation unit can also select the optimal voice tone based on the user's emotional state. For example, when the user has a strong positive emotion, a bright tone is selected. This allows the voice tone to be selected based on the user's emotional state.

[0054] The summary generation unit can analyze the user's past summary requests and automatically select the optimal summary style. For example, the summary generation unit collects the user's past summary requests and builds a system that automatically selects the optimal summary style based on that data. For example, it analyzes the content of past requests to determine the summary style. The summary generation unit also develops an algorithm that dynamically selects the user's most preferred summary style based on the summary request history. For example, it learns past request patterns and provides the optimal summary. The summary generation unit also analyzes the user's summary request history and automatically selects the optimal summary style. For example, it provides the optimal summary based on the frequency of requests for a specific summary format or length. This makes it possible to select the optimal summary style based on the user's past summary requests.

[0055] The summary generation unit can detect the user's emotional state using an emotion estimation function and provide a summary that matches their mood at the time. For example, the summary generation unit builds a system that detects the user's emotional state in real time and uses that data to create a system in which a generation AI provides a summary. For example, it collects emotional data using a camera or microphone and provides a summary that matches the user's mood. The summary generation unit also uses the emotion estimation function to develop an algorithm that dynamically generates summaries that match the user's mood. For example, if the user is feeling stressed, a concise summary is provided. The summary generation unit also allows the generation AI to select the optimal summary based on the user's emotional state. For example, a detailed summary is provided when positive emotions are strong. This makes it possible to provide summaries based on the user's emotional state.

[0056] The summary generation unit can supplement the summary by retrieving information from external databases to improve the accuracy of the summary. For example, the summary generation unit builds a system that automatically retrieves and supplements information from related external databases to improve the accuracy of the summary. For example, it obtains related information from academic paper databases and news sites. The summary generation unit also develops an algorithm that complements the content of the summary based on information from external databases. For example, it incorporates related data and statistical information into the summary. The summary generation unit also builds a system that dynamically retrieves and supplements information from external databases to improve the accuracy of the summary. For example, it reflects the latest research results and technological trends in the summary. This makes it possible to improve the accuracy of the summary based on information from external databases.

[0057] The difficulty level adjustment unit can analyze the user's learning history and dynamically adjust the difficulty level based on the user's past level of comprehension. The difficulty level adjustment unit, for example, collects the user's learning history and builds a system that analyzes the user's past level of comprehension based on that data. For example, the level of comprehension is evaluated based on past test results and study time. The difficulty level adjustment unit also develops an algorithm that dynamically adjusts the difficulty level to match the user's level of comprehension based on the learning history. For example, the difficulty level is increased when the level of comprehension is high and decreased when the level of comprehension is low. The difficulty level adjustment unit also analyzes the user's learning history and builds a system that provides an optimal level of difficulty based on the user's past level of comprehension. For example, the difficulty level is adjusted based on the user's past learning patterns. This makes it possible to dynamically adjust the difficulty level based on the user's past level of comprehension.

[0058] The difficulty adjustment unit can detect the user's emotional state using an emotion estimation function and provide a difficulty level that matches their mood at the time. The difficulty adjustment unit, for example, detects the user's emotional state in real time and builds a system in which the generation AI provides a difficulty level based on that data. For example, it collects emotional data using a camera or microphone and provides a difficulty level that matches their mood. The difficulty adjustment unit also uses the emotion estimation function to develop an algorithm that dynamically adjusts the difficulty level to match the user's mood. For example, if the user is feeling stressed, it provides easy content. The difficulty adjustment unit also allows the generation AI to select the optimal difficulty level based on the user's emotional state. For example, it provides challenging content when positive emotions are strong. This makes it possible to provide a difficulty level based on the user's emotional state.

[0059] The confirmation question generation unit can analyze the user's past answer history and automatically generate optimal confirmation questions. The confirmation question generation unit, for example, collects the user's past answer history and builds a system that automatically generates optimal confirmation questions based on that data. For example, confirmation questions are generated based on the user's past correct answer rate or questions that were answered incorrectly. The confirmation question generation unit also develops an algorithm that dynamically generates confirmation questions that allow the user to study most effectively based on the answer history. For example, it learns past answer patterns and provides optimal questions. The confirmation question generation unit also analyzes the user's answer history and automatically generates optimal confirmation questions. For example, it provides confirmation questions based on questions of a specific topic or difficulty level. This makes it possible to automatically generate optimal confirmation questions based on the user's past answer history.

[0060] The confirmation question generation unit can detect the user's emotional state using an emotion estimation function and provide confirmation questions that match their mood at the time. For example, the confirmation question generation unit detects the user's emotional state in real time and builds a system in which the generation AI provides confirmation questions based on that data. For example, it collects emotional data using a camera or microphone and provides confirmation questions that match the mood. The confirmation question generation unit also uses the emotion estimation function to develop an algorithm that dynamically generates confirmation questions that match the user's mood. For example, if the user is feeling stressed, it provides easy questions. The confirmation question generation unit also allows the generation AI to select the most appropriate confirmation questions based on the user's emotional state. For example, it provides challenging questions when the user is feeling strongly positive. This makes it possible to provide confirmation questions based on the user's emotional state.

[0061] The translation unit can analyze the user's past translation history and automatically select the optimal translation style. For example, the translation unit collects the user's past translation history and builds a system that automatically selects the optimal translation style based on that data. For example, it analyzes the content of past translations to determine the translation style. The translation unit also develops an algorithm that dynamically selects the translation style that the user most prefers based on the translation history. For example, it learns past translation patterns and provides the optimal translation. The translation unit also analyzes the user's translation history and automatically selects the optimal translation style. For example, it provides the optimal translation based on the frequency of requests for specific translation formats or phrases. This makes it possible to select the optimal translation style based on the user's past translation history.

[0062] The translation unit can detect the user's emotional state using an emotion estimation function and provide a translation that matches their mood at the time. For example, the translation unit can detect the user's emotional state in real time and build a system in which a generation AI provides a translation based on that data. For example, it can collect emotional data using a camera or microphone and provide a translation that matches the mood. The translation unit can also use the emotion estimation function to develop an algorithm that dynamically generates a translation that matches the user's mood. For example, if the user is feeling stressed, it can provide a concise translation. The translation unit can also select the optimal translation based on the user's emotional state using the generation AI. For example, if the user is feeling very positive, it can provide a detailed translation. This allows the translation to be provided based on the user's emotional state.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] An e-book reader can also be equipped with a health management unit that monitors the user's health status and provides content based on that data. For example, it can analyze the user's heart rate and sleep patterns and provide relaxing content when stress levels are high. The health management unit can also provide refreshing content suitable for post-exercise activities based on the user's exercise history. Furthermore, the health management unit can analyze the user's food records and suggest study content suitable for after meals. This allows the optimal content to be provided based on the user's health status.

[0065] The e-book reader may also include a learning style analysis unit that analyzes the user's learning style and provides content based on that data. For example, if the user prefers visual learning, content that makes extensive use of diagrams and graphs may be provided. If the user prefers auditory learning, the learning style analysis unit may also provide content that makes extensive use of audio and music. Furthermore, the learning style analysis unit may analyze the user's learning pace and suggest breaks at appropriate times. This allows the e-book reader to provide optimal content based on the user's learning style.

[0066] The e-book reader may further include an emotion estimation unit that estimates the user's emotional state and provides content based on that data. For example, if the user is tired, the emotion estimation unit may provide content that helps the user relax. Alternatively, if the user is excited, the emotion estimation unit may provide content that helps the user concentrate. Furthermore, the emotion estimation unit may monitor the user's emotional state in real time and dynamically optimize content based on that data. This allows the e-book reader to provide optimal content based on the user's emotional state.

[0067] The e-reader may also include a social network analyzer that analyzes the user's social network and provides content based on that data. For example, the e-reader may recommend books that the user's friends are reading. The social network analyzer may also provide content related to topics that the user's friends are interested in. The social network analyzer may also monitor the user's social network activity in real time and dynamically optimize content based on that data. This allows the e-reader to provide optimal content based on the user's social network.

[0068] The e-reader may also include a learning goal setting unit that sets a user's learning goals and provides content based on that data. For example, the e-reader may provide a learning plan to help the user pass a particular exam. The learning goal setting unit may also monitor the user's progress and provide advice on how to achieve the goal. The learning goal setting unit may also dynamically optimize content based on the user's learning goals. This allows the e-reader to provide the most appropriate content based on the user's learning goals.

[0069] The e-book reader may further include an audio tone selector that estimates the user's emotional state and selects an audio tone based on the estimated data. For example, a soft tone may be selected when the user is relaxed. Alternatively, the audio tone selector may select a clear tone when the user is focused. Furthermore, the audio tone selector may monitor the user's emotional state in real time and dynamically optimize the audio tone based on the data. This allows the e-book reader to provide an optimal audio tone based on the user's emotional state.

[0070] The e-book reader may further include a learning history analysis unit that analyzes the user's learning history and provides summaries based on that data. For example, it may summarize what the user has learned in the past. The learning history analysis unit may also select an optimal summarization style based on the user's learning history. Furthermore, the learning history analysis unit may monitor the user's learning history in real time and dynamically optimize summaries based on that data. This allows the e-book reader to provide an optimal summary based on the user's learning history.

[0071] The e-book reader may further include an emotion estimation / summarization unit that estimates the user's emotional state and provides summaries based on the data. For example, if the user is tired, a brief summary may be provided. Alternatively, if the user is excited, the emotion estimation / summarization unit may provide a detailed summary. Furthermore, the emotion estimation / summarization unit may monitor the user's emotional state in real time and dynamically optimize the summary based on the data. This allows the e-book reader to provide an optimal summary based on the user's emotional state.

[0072] The e-book reader may further include a learning history analysis unit that analyzes the user's learning history and provides review questions based on that data. For example, review questions may be generated based on questions the user has answered incorrectly in the past. The learning history analysis unit may also select optimal review questions based on the user's learning history. Furthermore, the learning history analysis unit may monitor the user's learning history in real time and dynamically optimize the review questions based on that data. This allows the provision of optimal review questions based on the user's learning history.

[0073] The e-book reader may further include an emotion estimation confirmation question unit that estimates the user's emotional state and provides confirmation questions based on that data. For example, a more difficult question may be provided if the user is relaxed. The emotion estimation confirmation question unit may also provide an easier question if the user is feeling stressed. Furthermore, the emotion estimation confirmation question unit may monitor the user's emotional state in real time and dynamically optimize the confirmation questions based on that data. This allows the provision of optimal confirmation questions based on the user's emotional state.

[0074] The processing flow of the second embodiment will be briefly explained below.

[0075] Step 1: The content conversion unit converts the content to match the user's hobbies and interests. For example, if the user is interested in sports, the generation AI will explain the learning content using sports-related examples and metaphors. The content conversion unit can also analyze the user's past learning history and track changes in interests to dynamically optimize the content. Step 2: The voice generation unit reads the content converted by the content conversion unit in the user's preferred voice. For example, if the user prefers the voice of a particular voice actor, the generation AI will read the content by imitating that voice actor's voice. The voice generation unit can also customize the voice to match the tone and pace of the user's voice. Step 3: The summary generator summarizes the content. For example, by inputting instructions such as "Summarize the whole thing in XX characters or less" or "Tell me more about the word XX," the AI ​​generates a summary based on those instructions. The summary generator can also analyze the user's past summary requests and automatically select the optimal summary style. Step 4: The difficulty adjustment unit adjusts the difficulty of the content. For example, by inputting instructions such as "Convert it into content that even a junior high school student can understand," the generation AI will simplify the content based on those instructions. The difficulty adjustment unit can also analyze the user's learning history and dynamically adjust the difficulty level based on their past level of comprehension. Step 5: The verification question generation unit generates verification questions to verify the content of the content. For example, by inputting an instruction such as "Provide questions to verify the content of Chapter 1" into the generation AI, the generation AI will generate verification questions based on that instruction. The verification question generation unit can also analyze the user's past response history and automatically generate optimal verification questions. Step 6: The translation department translates the content into multiple languages. For example, by inputting instructions such as "translate the contents of a book into English," the generation AI translates the content based on those instructions. The translation department can also analyze the user's past translation history and automatically select the optimal translation style.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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).

[0085] 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.

[0086] 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.

[0087] 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.

[0088] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0089] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0104] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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."

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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]

[0143] 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 content conversion unit that converts the content into content that matches the user's hobbies and preferences; a voice generating unit that reads the content converted by the content converting unit in a voice of the user's choice; a summary generator that summarizes the content; a difficulty level adjustment unit that adjusts the difficulty level of the content; a verification question generation unit that generates verification questions for verifying the content; a translation unit that translates the content into multiple languages; A system characterized by:

2. The content conversion unit Analyzing the user's past learning history and tracking changes in interests to dynamically optimize content 2. The system of claim 1.

3. The content conversion unit Detecting the user's real-time emotional state with an emotion estimation function and providing content tailored to their mood 2. The system of claim 1.

4. The content conversion unit Analyze the user's social media activity to customize content based on current interests and trends 2. The system of claim 1.

5. The voice generation unit The generative AI customizes the voice to match the user's tone and pace.

2. The system of claim 1.

6. The voice generation unit Detecting the user's emotional state using an emotion estimation function and selecting a voice tone that matches the user's mood 2. The system of claim 1.

7. The summary generation unit Analyzing the user's past summary requests and automatically selecting the most suitable summary style 2. The system of claim 1.

Images