System

Abstract

An object of a system according to an embodiment is to efficiently perform learning during sleep.SOLUTION: A system includes a voice generation part, a question answering part, and a dream generation part. The sound generation unit generates a sound course on the basis of a topic specified by a user. The question answering unit can ask a question while the user is sleeping. The dream generator generates dream episodes and scenarios related to a particular topic.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 techniques do not adequately provide means for efficient learning during sleep, and there is room for improvement.

[0005] The system according to the embodiment aims to enable efficient learning during sleep. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice generation unit, a question and answer unit, and a dream generation unit. The voice generation unit generates a voice course based on a topic specified by a user. The question and answer unit allows the user to ask questions while sleeping. The dream generation unit generates dream episodes and scenarios related to a specific topic. [Effects of the Invention]

[0007] The system according to the embodiment allows efficient learning during sleep. [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 sleep learning system according to the embodiment of the present invention utilizes a generation AI to enable a user to efficiently learn while sleeping. This allows the sleep learning system to efficiently learn while the user is sleeping.

[0029] A sleep learning system according to an embodiment includes an audio generation unit, a question-and-answer unit, and a dream generation unit. The audio generation unit generates an audio course based on a topic specified by a user. For example, the generation AI generates an audio course, such as a history lecture or a foreign language audiobook, based on prompts containing instructions about what the user wants to learn. The generation AI can also adjust the content of the audio course so that the user can learn in a relaxed state. The question-and-answer unit provides a function that allows the user to ask questions while sleeping. For example, when a user asks a question about the pronunciation of a foreign word or a historical event, the generation AI provides an answer to the question. The generation AI generates an appropriate answer based on prompts containing instructions about what the user wants to ask. The dream generation unit generates dream episodes and scenarios related to a specific topic. For example, the generation AI generates a dream scenario about basic mathematical concepts based on prompts containing instructions about what the user wants to learn. This allows the user to learn while dreaming and solidify the information in their memory. This allows the sleep learning system according to an embodiment to efficiently learn while sleeping. For example, a user can learn while relaxing by listening to an audio course and deepening their learning by asking questions and receiving answers while sleeping. Learning in dreams also helps to solidify the information in your memory.

[0030] The audio generation unit can analyze the user's past learning history and generate an individually customized audio course. In the audio generation unit, for example, the generation AI analyzes the user's past learning history to understand the user's level of understanding and interest in a specific topic. For example, an individually customized audio course is generated based on the user's past learning content and test results. In addition, the audio generation unit uses the generation AI to create an audio course tailored to the user's individual learning needs based on the user's learning history. For example, an audio course focusing on areas in which the user is weak is provided. In addition, the audio generation unit uses the generation AI to analyze the user's learning history and generate an audio course including new topics related to past learning content. For example, a new episode related to a history topic the user has learned is added. This makes it possible to provide an audio course customized based on the user's learning history.

[0031] The audio generation unit can add background sounds and sound effects to audio courses to provide a more immersive learning experience. For example, the generation AI of the audio generation unit can add natural or environmental sounds to audio courses to provide a relaxing learning environment. For example, an audio course can be generated with the sounds of a forest or waves playing in the background. The audio generation unit can also add sound effects to audio courses to emphasize the learning content. For example, in a history lecture, adding sound effects of battle scenes can enhance the sense of realism. The generation AI of the audio generation unit can also combine music with audio courses to improve concentration during learning. For example, an audio course can be provided with classical music playing in the background. This can provide a more immersive learning experience.

[0032] The question answering unit can analyze the user's past question history and provide related additional information and supplemental explanations. In the question answering unit, for example, the generation AI analyzes the user's past question history and provides related additional information. For example, it provides new information related to questions the user has asked in the past. In addition, the question answering unit has the generation AI provide supplemental explanations based on the user's question history. For example, it adds detailed explanations to questions the user has asked in the past. In addition, the question answering unit has the generation AI analyze the user's question history and provide additional information on related topics. For example, if the user asks a question about history, it explains the relevant historical background. This makes it possible to provide related information based on the user's past question history.

[0033] The question-answering unit can evaluate the user's pronunciation and intonation and provide feedback on areas for improvement. In the question-answering unit, for example, the generation AI analyzes and evaluates the user's pronunciation and intonation. For example, when a user pronounces a word in a foreign language, the accuracy of the pronunciation is evaluated. The question-answering unit also provides feedback on the user's pronunciation and intonation. For example, it specifically points out areas for improvement in the words pronounced by the user. The question-answering unit also suggests ways to improve pronunciation and intonation based on the user's pronunciation data using the generation AI. For example, it provides practice methods to help the user master correct pronunciation. This can support the improvement of the user's pronunciation and intonation.

[0034] The dream generation unit can analyze the content of the user's past dreams and generate more effective learning scenarios. For example, the dream generation unit uses a generation AI to analyze the content of the user's past dreams and generate scenarios that are useful for learning. For example, it provides related learning content based on scenes the user saw in their dreams. The dream generation unit also uses a generation AI to create effective learning scenarios based on the content of the user's dreams. For example, it incorporates events the user experienced in their dreams into the learning content. The dream generation unit also uses a generation AI to analyze the user's dream data and generate scenarios to improve learning effectiveness. For example, it reflects places and people the user saw in their dreams in the learning scenario. This makes it possible to provide effective learning scenarios based on the content of the user's past dreams.

[0035] The dream generation unit can add interactive elements to the dream scenario, allowing the user to make choices and take action in the dream. For example, the generation AI in the dream generation unit adds interactive elements to the dream scenario, allowing the user to make choices and take action in the dream. For example, the user selects options in the dream and the story progresses. In addition, the generation AI in the dream generation unit designs a scenario so that the user can learn interactively in the dream. For example, the user deepens the learning content by solving problems in the dream. In addition, the generation AI in the dream generation unit incorporates interactive quizzes and tasks into the dream scenario, allowing the user to advance their learning in the dream. For example, the user can confirm their knowledge by answering quizzes in the dream. This allows the user to learn interactively in the dream.

[0036] The dream generation unit can customize the dream scenario according to different learning topics or themes. In the dream generation unit, for example, the generation AI customizes the dream scenario according to the user's learning topic. For example, if the user wants to learn mathematics, it provides a dream scenario including mathematics problems. In addition, in the dream generation unit, the generation AI creates a dream scenario according to different themes. For example, if the user wants to learn history, it provides a dream scenario that recreates historical events. In addition, in the dream generation unit, the generation AI customizes the dream scenario according to the user's learning needs. For example, if the user wants to learn science, it provides a dream scenario that includes scientific experiments. In this way, it is possible to provide dream scenarios according to different learning topics or themes.

[0037] The dream generation unit can add a function that allows dream scenarios to be shared with other users and for collaborative learning. The dream generation unit, for example, allows the generation AI to share dream scenarios with other users, providing a collaborative learning function. For example, a user experiences the same dream scenario with a friend and shares the learning content. Furthermore, by sharing dream scenarios, the dream generation unit provides an environment in which users can learn collaboratively with other users. For example, users solve problems in a dream as a group. Furthermore, the dream generation unit adds a function that allows the generation AI to share dream scenarios and for collaborative learning with other users. For example, a user can have a discussion with other users in a dream. This allows collaborative learning with other users.

[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] The sleep learning system may further include a timing analysis unit that analyzes the user's sleep patterns and provides optimal learning timing. For example, it may detect when the user enters deep sleep and provide learning content at that timing. The timing analysis unit may also analyze the user's sleep cycle and identify the most effective learning timing. For example, providing learning content during REM sleep may promote memory consolidation. The timing analysis unit may also create an individually customized learning schedule based on the user's sleep data. For example, it may provide learning content during the time period when the user is most relaxed. This allows the system to provide optimal learning timing based on the user's sleep patterns.

[0040] The sleep learning system may further include a health monitoring unit that monitors the user's health condition and adjusts the learning content. For example, the health monitoring unit may measure the user's heart rate and blood pressure and evaluate the user's stress level. The health monitoring unit may also provide learning content with a relaxing effect based on the user's health data. For example, if the user is feeling high stress, the health monitoring unit may provide an audio course with a relaxing effect. The health monitoring unit may also adjust the difficulty of the learning content according to the user's health condition. For example, if the user is tired, the health monitoring unit may provide an easy learning course. This allows the system to provide optimal learning content based on the user's health condition.

[0041] The sleep learning system may further include a progress visualization unit that visualizes the user's learning progress. For example, it may display the content the user has learned and the goals they have achieved in graphs and charts. The progress visualization unit may also create an individual learning plan based on the user's learning data. For example, it may provide a learning plan that focuses on areas in which the user is weak. The progress visualization unit may also monitor the user's learning progress in real time and provide feedback. For example, when the user achieves a goal, it may provide feedback that gives the user a sense of accomplishment. This makes it possible to visualize the user's learning progress and increase motivation.

[0042] The sleep learning system may further include a learning style adaptation unit that customizes the content according to the user's learning style. For example, if the user prefers visual learning, an audio course including visual content may be provided. The learning style adaptation unit may also analyze the user's learning style and suggest the optimal learning method. For example, if the user prefers auditory learning, an audio-based learning course may be provided. The learning style adaptation unit may also adjust the format of the learning content according to the user's learning style. For example, if the user prefers hands-on learning, a learning course including interactive quizzes and assignments may be provided. This allows the user to have an optimal learning experience according to their learning style.

[0043] The sleep learning system can further include a reward system to increase the user's motivation to learn. For example, badges or points can be awarded when the user achieves a specific learning goal. The reward system can also create an individual reward plan based on the user's learning data. For example, a special reward can be provided when the user overcomes a weak area. The reward system can also provide rewards in stages according to the user's learning progress. For example, a small reward can be provided when the user achieves a certain amount of study time to maintain motivation. This can increase the user's motivation to learn.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The audio generator generates an audio course based on a topic specified by the user. For example, the generator generates an audio course, such as a history lecture or a foreign language audiobook, based on prompts containing instructions about what the user wants to learn. The generator can also adjust the content of the audio course to allow the user to learn in a relaxed state. Step 2: The question-answering unit provides a function that allows users to ask questions while they sleep. For example, if a user asks a question about the pronunciation of a foreign word or a historical event, the generation AI will provide an answer to that question. The generation AI generates an appropriate answer based on a prompt that includes an indication of the question the user wants to ask. Step 3: The dream generator generates dream episodes and scenarios related to a specific topic. For example, the generator generates dream scenarios about basic mathematical concepts based on prompts containing instructions about what the user wants to learn. This allows the user to learn and retain the information in their dreams.

[0046] (Example 2) The sleep learning system according to the embodiment of the present invention utilizes a generation AI to enable a user to efficiently learn while sleeping. This allows the sleep learning system to efficiently learn while the user is sleeping.

[0047] A sleep learning system according to an embodiment includes an audio generation unit, a question-and-answer unit, and a dream generation unit. The audio generation unit generates an audio course based on a topic specified by a user. For example, the generation AI generates an audio course, such as a history lecture or a foreign language audiobook, based on prompts containing instructions about what the user wants to learn. The generation AI can also adjust the content of the audio course so that the user can learn in a relaxed state. The question-and-answer unit provides a function that allows the user to ask questions while sleeping. For example, when a user asks a question about the pronunciation of a foreign word or a historical event, the generation AI provides an answer to the question. The generation AI generates an appropriate answer based on prompts containing instructions about what the user wants to ask. The dream generation unit generates dream episodes and scenarios related to a specific topic. For example, the generation AI generates a dream scenario about basic mathematical concepts based on prompts containing instructions about what the user wants to learn. This allows the user to learn while dreaming and solidify the information in their memory. This allows the sleep learning system according to an embodiment to efficiently learn while sleeping. For example, a user can learn while relaxing by listening to an audio course and deepening their learning by asking questions and receiving answers while sleeping. Learning in dreams also helps to solidify the information in your memory.

[0048] The audio generation unit can analyze the user's past learning history and generate an individually customized audio course. In the audio generation unit, for example, the generation AI analyzes the user's past learning history to understand the user's level of understanding and interest in a specific topic. For example, an individually customized audio course is generated based on the user's past learning content and test results. In addition, the audio generation unit uses the generation AI to create an audio course tailored to the user's individual learning needs based on the user's learning history. For example, an audio course focusing on areas in which the user is weak is provided. In addition, the audio generation unit uses the generation AI to analyze the user's learning history and generate an audio course including new topics related to past learning content. For example, a new episode related to a history topic the user has learned is added. This makes it possible to provide an audio course customized based on the user's learning history.

[0049] The audio generation unit can add background sounds and sound effects to audio courses to provide a more immersive learning experience. For example, the generation AI of the audio generation unit can add natural or environmental sounds to audio courses to provide a relaxing learning environment. For example, an audio course can be generated with the sounds of a forest or waves playing in the background. The audio generation unit can also add sound effects to audio courses to emphasize the learning content. For example, in a history lecture, adding sound effects of battle scenes can enhance the sense of realism. The generation AI of the audio generation unit can also combine music with audio courses to improve concentration during learning. For example, an audio course can be provided with classical music playing in the background. This can provide a more immersive learning experience.

[0050] The voice generation unit can use the emotion estimation function to adjust the voice tone and pace according to the user's emotional state. For example, the generation AI of the voice generation unit analyzes the user's emotional state in real time and adjusts the tone and pace of the voice course. For example, if the user is relaxed, the voice generation unit provides voice in a gentle tone. The voice generation unit also uses the emotion estimation function to detect the user's stress level and generate a voice course that has a relaxing effect. For example, if the user is nervous, the voice generation unit provides voice at a slow pace. The generation AI of the voice generation unit also selects the optimal voice tone and pace based on the user's emotional data. For example, if the user is concentrating, the voice generation unit provides a fast-paced voice course. This makes it possible to provide a voice tone and pace according to the user's emotional state.

[0051] The question answering unit can analyze the user's past question history and provide related additional information and supplemental explanations. In the question answering unit, for example, the generation AI analyzes the user's past question history and provides related additional information. For example, it provides new information related to questions the user has asked in the past. In addition, the question answering unit has the generation AI provide supplemental explanations based on the user's question history. For example, it adds detailed explanations to questions the user has asked in the past. In addition, the question answering unit has the generation AI analyze the user's question history and provide additional information on related topics. For example, if the user asks a question about history, it explains the relevant historical background. This makes it possible to provide related information based on the user's past question history.

[0052] The question-answering unit can evaluate the user's pronunciation and intonation and provide feedback on areas for improvement. In the question-answering unit, for example, the generation AI analyzes and evaluates the user's pronunciation and intonation. For example, when a user pronounces a word in a foreign language, the accuracy of the pronunciation is evaluated. The question-answering unit also provides feedback on the user's pronunciation and intonation. For example, it specifically points out areas for improvement in the words pronounced by the user. The question-answering unit also suggests ways to improve pronunciation and intonation based on the user's pronunciation data using the generation AI. For example, it provides practice methods to help the user master correct pronunciation. This can support the improvement of the user's pronunciation and intonation.

[0053] The question answering unit can use the emotion estimation function to analyze the emotional response to the user's question and provide an appropriate answer. For example, the generation AI in the question answering unit analyzes the emotional response to the user's question and provides an appropriate answer. For example, if the user is feeling anxious, it provides an answer that gives a sense of security. The question answering unit also uses the emotion estimation function to analyze the user's emotional state in response to the question and provides an answer in an appropriate tone. For example, if the user is relaxed, it answers in a calm tone. The question answering unit also selects the optimal answer based on the user's emotion data. For example, if the user is excited, it provides an answer in a calm tone. This makes it possible to provide an appropriate answer according to the user's emotions.

[0054] The dream generation unit can analyze the content of the user's past dreams and generate more effective learning scenarios. For example, the dream generation unit uses a generation AI to analyze the content of the user's past dreams and generate scenarios that are useful for learning. For example, it provides related learning content based on scenes the user saw in their dreams. The dream generation unit also uses a generation AI to create effective learning scenarios based on the content of the user's dreams. For example, it incorporates events the user experienced in their dreams into the learning content. The dream generation unit also uses a generation AI to analyze the user's dream data and generate scenarios to improve learning effectiveness. For example, it reflects places and people the user saw in their dreams in the learning scenario. This makes it possible to provide effective learning scenarios based on the content of the user's past dreams.

[0055] The dream generation unit can add interactive elements to the dream scenario, allowing the user to make choices and take action in the dream. For example, the generation AI in the dream generation unit adds interactive elements to the dream scenario, allowing the user to make choices and take action in the dream. For example, the user selects options in the dream and the story progresses. In addition, the generation AI in the dream generation unit designs a scenario so that the user can learn interactively in the dream. For example, the user deepens the learning content by solving problems in the dream. In addition, the generation AI in the dream generation unit incorporates interactive quizzes and tasks into the dream scenario, allowing the user to advance their learning in the dream. For example, the user can confirm their knowledge by answering quizzes in the dream. This allows the user to learn interactively in the dream.

[0056] The dream generation unit can use the emotion estimation function to analyze the emotional state of the user in a dream and provide a positive learning experience. For example, the dream generation unit uses the generation AI to analyze the emotional state of the user in a dream and provide a positive learning experience. For example, if the user is relaxed in a dream, it provides a calm learning scenario. The dream generation unit also uses the emotion estimation function to analyze the emotions of the user in a dream and generate a scenario that enhances learning effectiveness. For example, if the user is excited in a dream, it provides a scenario that enhances concentration. The dream generation unit also uses the generation AI to create a scenario that provides a positive learning experience based on the emotional data of the user's dream. For example, it provides a scenario that makes the user feel at ease in a dream. This makes it possible to provide a positive learning experience based on the emotional state of the user in a dream.

[0057] The dream generation unit can customize the dream scenario according to different learning topics or themes. In the dream generation unit, for example, the generation AI customizes the dream scenario according to the user's learning topic. For example, if the user wants to learn mathematics, it provides a dream scenario including mathematics problems. In addition, in the dream generation unit, the generation AI creates a dream scenario according to different themes. For example, if the user wants to learn history, it provides a dream scenario that recreates historical events. In addition, in the dream generation unit, the generation AI customizes the dream scenario according to the user's learning needs. For example, if the user wants to learn science, it provides a dream scenario that includes scientific experiments. In this way, it is possible to provide dream scenarios according to different learning topics or themes.

[0058] The dream generation unit can add a function that allows dream scenarios to be shared with other users and for collaborative learning. The dream generation unit, for example, allows the generation AI to share dream scenarios with other users, providing a collaborative learning function. For example, a user experiences the same dream scenario with a friend and shares the learning content. Furthermore, by sharing dream scenarios, the dream generation unit provides an environment in which users can learn collaboratively with other users. For example, users solve problems in a dream as a group. Furthermore, the dream generation unit adds a function that allows the generation AI to share dream scenarios and for collaborative learning with other users. For example, a user can have a discussion with other users in a dream. This allows collaborative learning with other users.

[0059] The dream generation unit can use the emotion estimation function to automatically generate environments and scenarios that make it easy for the user to relax in their dreams. For example, the dream generation unit uses the generation AI to analyze the user's emotional state and generate dream scenarios that make it easy for the user to relax. For example, if the user is feeling stressed, it provides a calm scenario. The dream generation unit also uses the emotion estimation function to provide an environment in the dream that makes it easy for the user to relax. For example, it incorporates natural scenery that makes the user feel relaxed into the dream scenario. The dream generation unit also uses the generation AI to automatically generate dream scenarios that have a relaxing effect based on the user's emotional data. For example, it provides a scenario that makes the user feel at ease. This makes it possible to provide environments and scenarios that make it easy for the user to relax in their dreams.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The sleep learning system may further include a timing analysis unit that analyzes the user's sleep patterns and provides optimal learning timing. For example, it may detect when the user enters deep sleep and provide learning content at that timing. The timing analysis unit may also analyze the user's sleep cycle and identify the most effective learning timing. For example, providing learning content during REM sleep may promote memory consolidation. The timing analysis unit may also create an individually customized learning schedule based on the user's sleep data. For example, it may provide learning content during the time period when the user is most relaxed. This allows the system to provide optimal learning timing based on the user's sleep patterns.

[0062] The sleep learning system may further include a health monitoring unit that monitors the user's health condition and adjusts the learning content. For example, the health monitoring unit may measure the user's heart rate and blood pressure and evaluate the user's stress level. The health monitoring unit may also provide learning content with a relaxing effect based on the user's health data. For example, if the user is feeling high stress, the health monitoring unit may provide an audio course with a relaxing effect. The health monitoring unit may also adjust the difficulty of the learning content according to the user's health condition. For example, if the user is tired, the health monitoring unit may provide an easy learning course. This allows the system to provide optimal learning content based on the user's health condition.

[0063] The sleep learning system may further include a progress visualization unit that visualizes the user's learning progress. For example, it may display the content the user has learned and the goals they have achieved in graphs and charts. The progress visualization unit may also create an individual learning plan based on the user's learning data. For example, it may provide a learning plan that focuses on areas in which the user is weak. The progress visualization unit may also monitor the user's learning progress in real time and provide feedback. For example, when the user achieves a goal, it may provide feedback that gives the user a sense of accomplishment. This makes it possible to visualize the user's learning progress and increase motivation.

[0064] The sleep learning system may further include a learning style adaptation unit that customizes the content according to the user's learning style. For example, if the user prefers visual learning, an audio course including visual content may be provided. The learning style adaptation unit may also analyze the user's learning style and suggest the optimal learning method. For example, if the user prefers auditory learning, an audio-based learning course may be provided. The learning style adaptation unit may also adjust the format of the learning content according to the user's learning style. For example, if the user prefers hands-on learning, a learning course including interactive quizzes and assignments may be provided. This allows the user to have an optimal learning experience according to their learning style.

[0065] The sleep learning system can further include a reward system to increase the user's motivation to learn. For example, badges or points can be awarded when the user achieves a specific learning goal. The reward system can also create an individual reward plan based on the user's learning data. For example, a special reward can be provided when the user overcomes a weak area. The reward system can also provide rewards in stages according to the user's learning progress. For example, a small reward can be provided when the user achieves a certain amount of study time to maintain motivation. This can increase the user's motivation to learn.

[0066] The sleep learning system may further include an emotion adaptation unit that adjusts the learning content based on the user's emotional state. For example, if the user is feeling stressed, learning content with a relaxing effect is provided. The emotion adaptation unit may also adjust the tone and pace of the learning content based on the user's emotional data. For example, if the user is relaxed, learning content is provided in a gentle tone. The emotion adaptation unit may also adjust the difficulty of the learning content according to the user's emotional state. For example, if the user is concentrating, learning content with a high level of difficulty is provided. In this way, optimal learning content can be provided based on the user's emotional state.

[0067] The sleep learning system may further include an environmental adaptation unit that adjusts the learning environment based on the user's emotional state. For example, if the user is relaxed, the system may provide a learning environment with calm music or natural sounds as a background. The environmental adaptation unit may also adjust the lighting and temperature of the learning environment based on the user's emotional data. For example, if the user is feeling stressed, the system may provide soft lighting and a comfortable temperature. The environmental adaptation unit may also adjust the interface of the learning environment according to the user's emotional state. For example, if the user is concentrating, the system may provide a simple and intuitive interface. This allows the system to provide an optimal learning environment based on the user's emotional state.

[0068] The sleep learning system may further include a goal setting unit that sets learning goals based on the user's emotional state. For example, if the user is relaxed, a long-term learning goal is set. The goal setting unit may also suggest realistic and achievable learning goals based on the user's emotional data. For example, if the user is feeling stressed, a short-term learning goal is set. The goal setting unit may also monitor the progress of the learning goals and provide feedback according to the user's emotional state. For example, when the user achieves a goal, feedback is provided that gives the user a sense of accomplishment. This allows optimal learning goals to be set based on the user's emotional state.

[0069] The sleep learning system may further include a content selection unit that selects learning content based on the user's emotional state. For example, if the user is relaxed, learning content with calming content is provided. The content selection unit can also suggest optimal learning content based on the user's emotional data. For example, if the user is feeling stressed, learning content with a relaxing effect is provided. The content selection unit can also adjust the format of the learning content according to the user's emotional state. For example, if the user is concentrating, learning content with detailed explanations is provided. In this way, optimal learning content can be provided based on the user's emotional state.

[0070] The sleep learning system may further include a feedback unit that provides learning feedback based on the user's emotional state. For example, if the user is relaxed, the feedback unit provides feedback in a gentle tone. The feedback unit may also suggest an optimal feedback method based on the user's emotional data. For example, if the user is feeling stressed, the feedback unit may provide feedback including encouraging words. The feedback unit may also adjust the content of the feedback according to the user's emotional state. For example, if the user is concentrating, the feedback unit may provide feedback including specific points for improvement. This allows optimal learning feedback to be provided based on the user's emotional state.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The audio generator generates an audio course based on a topic specified by the user. For example, the generator generates an audio course, such as a history lecture or a foreign language audiobook, based on prompts containing instructions about what the user wants to learn. The generator can also adjust the content of the audio course to allow the user to learn in a relaxed state. Step 2: The question-answering unit provides a function that allows users to ask questions while they sleep. For example, if a user asks a question about the pronunciation of a foreign word or a historical event, the generation AI will provide an answer to that question. The generation AI generates an appropriate answer based on a prompt that includes an indication of the question the user wants to ask. Step 3: The dream generator generates dream episodes and scenarios related to a specific topic. For example, the generator generates dream scenarios about basic mathematical concepts based on prompts containing instructions about what the user wants to learn. This allows the user to learn and retain the information in their dreams.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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]

[0140] 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. Equipped with generative AI, The generated AI is an audio generator that generates an audio course based on a user-specified topic; a question answering unit that allows the user to ask questions while sleeping; A dream generation unit that generates dream episodes and scenarios related to a specific topic. A system characterized by:

2. The voice generation unit Analyzing the user's past learning history and generating an individually customized audio course 2. The system of claim 1.

3. The voice generation unit Add background sounds and sound effects to the audio courses to create a more immersive learning experience 2. The system of claim 1.

4. The voice generation unit Adjusting the tone and pace of the voice depending on the emotional state of the user 2. The system of claim 1.

5. The question answering unit Analyze the user's past question history and provide related additional information and supplementary explanations 2. The system of claim 1.

6. The question answering unit Evaluate the user's pronunciation and intonation and provide feedback on areas for improvement 2. The system of claim 1.

7. The question answering unit Analyzing the user's emotional response to the question and providing an appropriate answer 2. The system of claim 1.

8. The dream generation unit Analyzing the content of the user's past dreams to generate more effective learning scenarios 2. The system of claim 1.

Images