System

Abstract

An object of a system according to an embodiment is to propose an optimal learning method or teaching material in real time on the basis of a user's learning progress or understanding level.SOLUTION: A system includes a communication unit, an analysis unit, and a proposal unit. The communication unit communicates with a user through a talk function. The analysis unit analyzes the learning progress and the degree of understanding of the user acquired by the communication unit. The proposal unit proposes an optimum learning method or teaching material based on the result analyzed by the analysis unit.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 of not being able to adequately suggest optimal learning methods and learning materials in real time based on a user's learning progress and level of understanding.

[0005] The system according to the embodiment aims to propose optimal learning methods and learning materials in real time based on the user's learning progress and level of understanding. [Means for solving the problem]

[0006] The system according to the embodiment includes a communication unit, an analysis unit, and a suggestion unit. The communication unit communicates with the user through a talk function. The analysis unit analyzes the user's learning progress and level of understanding acquired by the communication unit. The suggestion unit suggests optimal learning methods and learning materials based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest optimal learning methods and learning materials in real time based on the user's learning progress and level of understanding. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) An AI system according to an embodiment of the present invention is a system that provides individualized learning plans and learning materials via LINE. In this system, the AI ​​and the user communicate using the LINE chat function, and the AI ​​analyzes the user's learning progress and level of understanding, and suggests optimal learning methods and learning materials in real time. This allows the AI ​​system to suggest optimal learning methods and learning materials in real time based on the user's learning progress and level of understanding.

[0029] The AI ​​system according to the embodiment includes a communication unit, an analysis unit, and a suggestion unit. The communication unit communicates with a user through the LINE chat function. For example, if a user sends a message saying, "I'm going to start studying math today," the communication unit responds with, "Which unit will you study?" The communication unit can also analyze the user's past conversation history and generate personalized responses based on the user's individual learning style and preferences. The analysis unit analyzes the user's learning progress and comprehension level acquired by the communication unit. For example, if a user reports, "I couldn't solve yesterday's problem," the analysis unit checks the content of the problem and identifies where the user is struggling. The analysis unit can also analyze the user's learning data chronologically to track changes in learning progress and comprehension in detail. The suggestion unit suggests optimal learning methods and learning materials based on the results of the analysis by the analysis unit. For example, if a user is struggling with a particular math unit, the suggestion unit suggests supplementary materials and practice problems related to that unit. The suggestion unit can also dynamically adjust the optimal learning method and learning materials based on the user's learning history and level of understanding, and always provide the latest, optimized suggestions. This allows the AI ​​system according to the embodiment to suggest optimal learning methods and learning materials based on the user's learning progress and level of understanding. For example, the user can proceed with learning at their own pace and use the optimal learning materials according to their level of understanding. Furthermore, real-time suggestions can prevent learning delays and provide support for achieving goals.

[0030] The communication unit can analyze the user's past conversation history and generate personalized responses based on the user's learning style and preferences. For example, the communication unit can analyze the user's past conversation history and generate responses based on the user's learning style and preferences. For example, based on the learning materials and learning methods that the user has preferred in the past, the communication unit can suggest similar learning materials and methods for the next learning session. The communication unit can also identify the user's preferred learning style based on the user's conversation history and generate responses accordingly. This makes it possible to generate responses based on the user's learning style and preferences.

[0031] The communication unit can analyze the user's voice input, estimate the user's motivation and concentration level for learning from the voice tone and speed, and respond accordingly. The communication unit, for example, analyzes the user's voice input, estimates the user's motivation for learning from the voice tone and speed. For example, if the user speaks in a lively tone, it responds proactively to promote learning. The communication unit can also estimate the user's concentration level based on the user's voice tone and speed, and respond accordingly. For example, if the user speaks slowly, it responds in a relaxed manner to reduce the burden of learning. In this way, it is possible to estimate the user's motivation and concentration level for learning from the user's voice tone and speed, and respond accordingly.

[0032] In addition to the chat function, the communication unit can provide users with interactive learning sessions using video calls and AR. For example, in addition to the LINE chat function, the communication unit can provide interactive learning sessions using video calls. For example, when a user asks a question, the AI ​​provides an explanation in real time via video call. The communication unit can also provide learning sessions using AR. For example, the user can use an AR device to conduct virtual experiments. This makes it possible to provide interactive learning sessions using video calls and AR.

[0033] The communication unit supports communication in different languages ​​and can provide an international multilingual system for users. The communication unit, for example, builds a multilingual system that supports communication in different languages. For example, responses are provided in multiple languages, such as English, French, and Chinese. The communication unit can also provide responses in an appropriate language depending on the language selected by the user. For example, if the user selects English, responses are provided in English. This makes it possible to provide a multilingual system that supports communication in different languages ​​and can accommodate international users.

[0034] The analysis unit can analyze the user's learning data in chronological order and track the learning progress and changes in the level of understanding in detail. The analysis unit, for example, analyzes the user's learning data in chronological order and tracks the learning progress and changes in the level of understanding in detail. For example, based on past learning history, it identifies in which unit the progress is lagging. The analysis unit can also analyze changes in the level of understanding based on the user's learning data. For example, if the level of understanding is declining in a particular unit, it identifies the cause. This makes it possible to analyze the user's learning data in chronological order and track the learning progress and changes in the level of understanding in detail.

[0035] The analysis unit can cluster the user's learning patterns and provide advice based on success stories of other users with the same patterns. The analysis unit, for example, clusters the user's learning patterns and provides advice based on success stories of other users with the same patterns. For example, it suggests an effective learning method based on success stories of users with the same learning style. The analysis unit can also suggest a learning method suitable for the user based on the clustering results. For example, it suggests a learning method that was effective for users belonging to the same cluster. This makes it possible to cluster the user's learning patterns and provide advice based on success stories of other users with the same patterns.

[0036] The analysis unit can share the analysis results of the learning progress and level of comprehension with parents and teachers and provide feedback to strengthen learning support. The analysis unit, for example, can share the analysis results of the learning progress and level of comprehension with parents and teachers and provide feedback to strengthen learning support. For example, the analysis unit can periodically report the user's learning status so that parents and teachers can provide appropriate support. The analysis unit can also provide parents and teachers with specific advice based on the user's learning progress and level of comprehension. For example, if the user's level of comprehension in a particular unit is low, the analysis unit can suggest supplementary learning materials for that unit. In this way, the analysis results of the learning progress and level of comprehension can be shared with parents and teachers and feedback can be provided to strengthen learning support.

[0037] The suggestion unit dynamically adjusts the optimal learning method and learning materials based on the user's learning history and level of understanding, and can always provide the latest, optimized suggestions. The suggestion unit dynamically adjusts the optimal learning method and learning materials based on the user's learning history and level of understanding. For example, the suggestion unit may suggest a similar learning method for the next learning session based on a learning method that was effective for the user in the past. The suggestion unit may also adjust the learning method and learning materials in real time according to the user's level of understanding. For example, if the user is struggling with a particular unit, the suggestion unit may suggest supplementary learning materials related to that unit. In this way, the suggestion unit dynamically adjusts the optimal learning method and learning materials based on the user's learning history and level of understanding, and can always provide the latest, optimized suggestions.

[0038] The suggestion unit can provide customized learning materials according to the user's learning style. For example, the suggestion unit provides customized learning materials according to the user's learning style (visual, auditory, tactile). For example, a user who prefers visual learning can be provided with learning materials that make extensive use of diagrams and graphs. Furthermore, audio learning materials and podcasts can be provided to a user who prefers auditory learning. Furthermore, hands-on experiments and interactive simulations can be provided to a user who prefers tactile learning. In this way, customized learning materials can be provided according to the user's learning style.

[0039] The suggestion unit can optimize the suggested study methods and learning materials to suit the user's lifestyle and schedule, thereby maximizing learning efficiency. The suggestion unit, for example, optimizes the suggested study methods and learning materials to suit the user's lifestyle and schedule. For example, it suggests studying during times when the user can concentrate best. The suggestion unit can also adjust the study plan based on the user's schedule. For example, when the user is busy, it suggests a short and effective study method. In this way, it is possible to optimize the suggested study methods and learning materials to suit the user's lifestyle and schedule, thereby maximizing learning efficiency.

[0040] The suggestion unit can cooperate with other learning platforms and educational institutions to provide a variety of learning resources to users. The suggestion unit, for example, cooperates with other learning platforms and educational institutions to provide a variety of learning resources to users. For example, it integrates learning materials from different platforms and provides them to users. The suggestion unit can also cooperate with educational institutions to suggest specific courses or programs to users. For example, it can suggest online courses from universities or vocational schools. This allows cooperation with other learning platforms and educational institutions to provide a variety of learning resources to users.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The communication unit can monitor the user's learning environment and suggest the optimal learning environment. For example, if the user is studying in a noisy environment, it can suggest a quiet place. The communication unit can also suggest appropriate learning tools and apps depending on the user's learning environment. For example, it can suggest a music app to improve concentration or a time management app. This can optimize the user's learning environment and improve learning efficiency.

[0043] The communication department can propose a customized learning schedule according to the user's learning style. For example, for a user who prefers visual learning, it can propose a schedule that makes extensive use of diagrams and graphs. For a user who prefers auditory learning, it can also propose a schedule that incorporates audio materials. Furthermore, for a user who prefers tactile learning, it can propose a schedule that includes hands-on experiments and interactive simulations. This makes it possible to provide an optimal learning schedule according to the user's learning style.

[0044] The communication unit can provide a dashboard that visually displays the user's learning progress. For example, it can display learning progress in graphs and charts, allowing the user to check their progress at a glance. The communication unit can also display achieved goals and future tasks based on the user's learning history. For example, it can review past learning results and suggest the next task to tackle. This allows the user to visually grasp their learning progress and maintain motivation.

[0045] The communication unit can provide a function that compares a user's learning progress with that of other users based on the user's learning history. For example, users with the same learning goals can compete with each other to see who can improve their progress. The communication unit can also share success stories so that users can learn from the success stories of other users. For example, it can introduce the learning methods of users who have achieved specific goals. This allows users to study while competing with other users, helping to maintain motivation.

[0046] The communication unit can provide a function for sharing learning progress with parents and teachers based on the user's learning history. For example, it can periodically report the user's learning status so that parents and teachers can provide appropriate support. The communication unit can also provide parents and teachers with specific advice based on the user's learning progress and level of understanding. For example, if the user's level of understanding of a particular unit is low, it can suggest supplementary materials for that unit. This makes it easier for parents and teachers to support the user's learning.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The communication department communicates with the user through LINE's chat function. For example, if a user sends a message saying, "Let's start studying math today," the communication department responds with, "Which unit will you study?" The communication department can also analyze the user's past conversation history and generate personalized responses based on individual learning styles and preferences. Step 2: The analysis unit analyzes the user's learning progress and level of understanding obtained by the communication unit. For example, if a user reports that they "couldn't solve yesterday's problem," the analysis unit checks the content of the problem and identifies the part where the user is struggling. The analysis unit can also analyze the user's learning data over time to track in detail the changes in learning progress and level of understanding. Step 3: The suggestion unit suggests optimal learning methods and learning materials based on the results of the analysis by the analysis unit. For example, if a user is struggling with a particular math unit, the suggestion unit will suggest supplementary learning materials and practice problems related to that unit. The suggestion unit can also dynamically adjust the optimal learning methods and learning materials based on the user's learning history and level of understanding, always providing the latest, optimized suggestions.

[0049] (Example 2) An AI system according to an embodiment of the present invention is a system that provides individualized learning plans and learning materials via LINE. In this system, the AI ​​and the user communicate using the LINE chat function, and the AI ​​analyzes the user's learning progress and level of understanding, and suggests optimal learning methods and learning materials in real time. This allows the AI ​​system to suggest optimal learning methods and learning materials in real time based on the user's learning progress and level of understanding.

[0050] The AI ​​system according to the embodiment includes a communication unit, an analysis unit, and a suggestion unit. The communication unit communicates with a user through the LINE chat function. For example, if a user sends a message saying, "I'm going to start studying math today," the communication unit responds with, "Which unit will you study?" The communication unit can also analyze the user's past conversation history and generate personalized responses based on the user's individual learning style and preferences. The analysis unit analyzes the user's learning progress and comprehension level acquired by the communication unit. For example, if a user reports, "I couldn't solve yesterday's problem," the analysis unit checks the content of the problem and identifies where the user is struggling. The analysis unit can also analyze the user's learning data chronologically to track changes in learning progress and comprehension in detail. The suggestion unit suggests optimal learning methods and learning materials based on the results of the analysis by the analysis unit. For example, if a user is struggling with a particular math unit, the suggestion unit suggests supplementary materials and practice problems related to that unit. The suggestion unit can also dynamically adjust the optimal learning method and learning materials based on the user's learning history and level of understanding, and always provide the latest, optimized suggestions. This allows the AI ​​system according to the embodiment to suggest optimal learning methods and learning materials based on the user's learning progress and level of understanding. For example, the user can proceed with learning at their own pace and use the optimal learning materials according to their level of understanding. Furthermore, real-time suggestions can prevent learning delays and provide support for achieving goals.

[0051] The communication unit can analyze the user's past conversation history and generate personalized responses based on the user's learning style and preferences. For example, the communication unit can analyze the user's past conversation history and generate responses based on the user's learning style and preferences. For example, based on the learning materials and learning methods that the user has preferred in the past, the communication unit can suggest similar learning materials and methods for the next learning session. The communication unit can also identify the user's preferred learning style based on the user's conversation history and generate responses accordingly. This makes it possible to generate responses based on the user's learning style and preferences.

[0052] The communication unit can analyze the user's voice input, estimate the user's motivation and concentration level for learning from the voice tone and speed, and respond accordingly. The communication unit, for example, analyzes the user's voice input, estimates the user's motivation for learning from the voice tone and speed. For example, if the user speaks in a lively tone, it responds proactively to promote learning. The communication unit can also estimate the user's concentration level based on the user's voice tone and speed, and respond accordingly. For example, if the user speaks slowly, it responds in a relaxed manner to reduce the burden of learning. In this way, it is possible to estimate the user's motivation and concentration level for learning from the user's voice tone and speed, and respond accordingly.

[0053] The communication unit uses the emotion estimation function to grasp the user's emotional state in real time, and can provide advice to relax if the user is feeling stressed or tired. The communication unit, for example, uses the emotion estimation function to grasp the user's emotional state in real time. For example, if the user is feeling stressed, it provides advice to relax. The communication unit can also suggest taking a break if the user is feeling tired based on the user's emotional state. For example, if the user is tired, it provides advice such as "Take a short break." In this way, it is possible to grasp the user's emotional state in real time, and provide advice to relax if the user is feeling stressed or tired.

[0054] In addition to the chat function, the communication unit can provide users with interactive learning sessions using video calls and AR. For example, in addition to the LINE chat function, the communication unit can provide interactive learning sessions using video calls. For example, when a user asks a question, the AI ​​provides an explanation in real time via video call. The communication unit can also provide learning sessions using AR. For example, the user can use an AR device to conduct virtual experiments. This makes it possible to provide interactive learning sessions using video calls and AR.

[0055] The communication unit supports communication in different languages ​​and can provide an international multilingual system for users. The communication unit, for example, builds a multilingual system that supports communication in different languages. For example, responses are provided in multiple languages, such as English, French, and Chinese. The communication unit can also provide responses in an appropriate language depending on the language selected by the user. For example, if the user selects English, responses are provided in English. This makes it possible to provide a multilingual system that supports communication in different languages ​​and can accommodate international users.

[0056] The communication unit can use the emotion estimation function to automatically generate motivational messages to reinforce the positive emotions felt by the user while studying. The communication unit, for example, uses the emotion estimation function to automatically generate messages to reinforce the positive emotions felt by the user while studying. For example, it sends a message such as "Great! Keep it up!" The communication unit can also provide an encouraging message when the user feels a sense of accomplishment. For example, it sends a message such as "You did a great job!" In this way, it is possible to automatically generate motivational messages to reinforce the positive emotions felt by the user while studying.

[0057] The analysis unit can analyze the user's learning data in chronological order and track the learning progress and changes in the level of understanding in detail. The analysis unit, for example, analyzes the user's learning data in chronological order and tracks the learning progress and changes in the level of understanding in detail. For example, based on past learning history, it identifies in which unit the progress is lagging. The analysis unit can also analyze changes in the level of understanding based on the user's learning data. For example, if the level of understanding is declining in a particular unit, it identifies the cause. This makes it possible to analyze the user's learning data in chronological order and track the learning progress and changes in the level of understanding in detail.

[0058] The analysis unit can cluster the user's learning patterns and provide advice based on success stories of other users with the same patterns. The analysis unit, for example, clusters the user's learning patterns and provides advice based on success stories of other users with the same patterns. For example, it suggests an effective learning method based on success stories of users with the same learning style. The analysis unit can also suggest a learning method suitable for the user based on the clustering results. For example, it suggests a learning method that was effective for users belonging to the same cluster. This makes it possible to cluster the user's learning patterns and provide advice based on success stories of other users with the same patterns.

[0059] The analysis unit can use the emotion estimation function to identify the frustration or difficulty the user feels when solving a specific problem and propose a solution to that. The analysis unit, for example, uses the emotion estimation function to identify the frustration the user feels when solving a specific problem. For example, it analyzes the stress the user feels when solving a problem and identifies the cause of that stress. The analysis unit can also propose solutions to problems that the user is having difficulty with. For example, it provides a step-by-step guide to assist in problem solving. This makes it possible to identify the frustration or difficulty the user feels when solving a specific problem and propose a solution to that problem.

[0060] The analysis unit can share the analysis results of the learning progress and level of comprehension with parents and teachers and provide feedback to strengthen learning support. The analysis unit, for example, can share the analysis results of the learning progress and level of comprehension with parents and teachers and provide feedback to strengthen learning support. For example, the analysis unit can periodically report the user's learning status so that parents and teachers can provide appropriate support. The analysis unit can also provide parents and teachers with specific advice based on the user's learning progress and level of comprehension. For example, if the user's level of comprehension in a particular unit is low, the analysis unit can suggest supplementary learning materials for that unit. In this way, the analysis results of the learning progress and level of comprehension can be shared with parents and teachers and feedback can be provided to strengthen learning support.

[0061] The analysis unit can use the emotion estimation function to introduce a reward system for reinforcing the positive emotions that the user feels about learning. The analysis unit, for example, uses the emotion estimation function to introduce a reward system for reinforcing the positive emotions that the user feels about learning. For example, the analysis unit can allow the user to earn points by continuing to study and exchange those points for rewards. The analysis unit can also award badges according to the user's learning results. For example, an achievement badge can be awarded to a user who achieves a specific goal. In this way, a reward system can be introduced for reinforcing the positive emotions that the user feels about learning.

[0062] The suggestion unit dynamically adjusts the optimal learning method and learning materials based on the user's learning history and level of understanding, and can always provide the latest, optimized suggestions. The suggestion unit dynamically adjusts the optimal learning method and learning materials based on the user's learning history and level of understanding. For example, the suggestion unit may suggest a similar learning method for the next learning session based on a learning method that was effective for the user in the past. The suggestion unit may also adjust the learning method and learning materials in real time according to the user's level of understanding. For example, if the user is struggling with a particular unit, the suggestion unit may suggest supplementary learning materials related to that unit. In this way, the suggestion unit dynamically adjusts the optimal learning method and learning materials based on the user's learning history and level of understanding, and can always provide the latest, optimized suggestions.

[0063] The suggestion unit can provide customized learning materials according to the user's learning style. For example, the suggestion unit provides customized learning materials according to the user's learning style (visual, auditory, tactile). For example, a user who prefers visual learning can be provided with learning materials that make extensive use of diagrams and graphs. Furthermore, audio learning materials and podcasts can be provided to a user who prefers auditory learning. Furthermore, hands-on experiments and interactive simulations can be provided to a user who prefers tactile learning. In this way, customized learning materials can be provided according to the user's learning style.

[0064] The suggestion unit can use the emotion estimation function to identify the learning content in which the user is most interested and preferentially suggest learning materials related to that content. The suggestion unit, for example, uses the emotion estimation function to identify the learning content in which the user is most interested. For example, it preferentially suggests learning materials related to topics in which the user has shown interest. The suggestion unit can also adjust a study plan based on the user's interests. For example, if the user is interested in a particular field, it proposes a study plan related to that field. This makes it possible to identify the learning content in which the user is most interested and preferentially suggest learning materials related to that content.

[0065] The suggestion unit can optimize the suggested study methods and learning materials to suit the user's lifestyle and schedule, thereby maximizing learning efficiency. The suggestion unit, for example, optimizes the suggested study methods and learning materials to suit the user's lifestyle and schedule. For example, it suggests studying during times when the user can concentrate best. The suggestion unit can also adjust the study plan based on the user's schedule. For example, when the user is busy, it suggests a short and effective study method. In this way, it is possible to optimize the suggested study methods and learning materials to suit the user's lifestyle and schedule, thereby maximizing learning efficiency.

[0066] The suggestion unit can cooperate with other learning platforms and educational institutions to provide a variety of learning resources to users. The suggestion unit, for example, cooperates with other learning platforms and educational institutions to provide a variety of learning resources to users. For example, it integrates learning materials from different platforms and provides them to users. The suggestion unit can also cooperate with educational institutions to suggest specific courses or programs to users. For example, it can suggest online courses from universities or vocational schools. This allows cooperation with other learning platforms and educational institutions to provide a variety of learning resources to users.

[0067] The suggestion unit can use the emotion estimation function to introduce an incentive program to reinforce the positive emotions that the user feels about learning. The suggestion unit, for example, uses the emotion estimation function to introduce an incentive program to reinforce the positive emotions that the user feels about learning. For example, the suggestion unit allows the user to earn points by continuing to study and exchange the points for rewards. The suggestion unit can also provide rewards according to the user's learning results. For example, a reward is provided to a user who achieves a specific goal. In this way, an incentive program can be introduced to reinforce the positive emotions that the user feels about learning.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The communication unit can monitor the user's learning environment and suggest the optimal learning environment. For example, if the user is studying in a noisy environment, it can suggest a quiet place. The communication unit can also suggest appropriate learning tools and apps depending on the user's learning environment. For example, it can suggest a music app to improve concentration or a time management app. This can optimize the user's learning environment and improve learning efficiency.

[0070] The communication unit can support goal setting to maintain motivation to study based on the user's learning history. For example, the unit can help the user set short-term goals and suggest steps to achieve them. The communication unit can also track the user's progress toward the goals they have set and provide feedback according to their level of achievement. For example, it can send an encouraging message when a goal is achieved. This helps maintain the user's motivation to study and support them in achieving their goals.

[0071] The communication department can propose a customized learning schedule according to the user's learning style. For example, for a user who prefers visual learning, it can propose a schedule that makes extensive use of diagrams and graphs. For a user who prefers auditory learning, it can also propose a schedule that incorporates audio materials. Furthermore, for a user who prefers tactile learning, it can propose a schedule that includes hands-on experiments and interactive simulations. This makes it possible to provide an optimal learning schedule according to the user's learning style.

[0072] The communication unit can use the emotion estimation function to suggest relaxation techniques to reduce negative emotions the user feels about learning. For example, if the user is feeling stressed, it can suggest deep breathing or meditation. The communication unit can also suggest taking a short break if the user feels tired. For example, it can provide advice such as "Take a five-minute break." This can reduce negative emotions the user feels about learning and support continued learning.

[0073] The communication unit can provide a dashboard that visually displays the user's learning progress. For example, it can display learning progress in graphs and charts, allowing the user to check their progress at a glance. The communication unit can also display achieved goals and future tasks based on the user's learning history. For example, it can review past learning results and suggest the next task to tackle. This allows the user to visually grasp their learning progress and maintain motivation.

[0074] The communication department can use the emotion estimation function to introduce gamification elements to reinforce the positive emotions users feel toward learning. For example, users can earn points as they progress through their studies and use those points to purchase virtual items. The communication department can also award badges or titles when users achieve specific goals. For example, a user can earn a title such as "Math Master." This can reinforce the positive emotions users feel toward learning and increase their motivation to study.

[0075] The communication unit can provide a function that compares a user's learning progress with that of other users based on the user's learning history. For example, users with the same learning goals can compete with each other to see who can improve their progress. The communication unit can also share success stories so that users can learn from the success stories of other users. For example, it can introduce the learning methods of users who have achieved specific goals. This allows users to study while competing with other users, helping to maintain motivation.

[0076] The communication unit can use the emotion estimation function to provide a counseling function to reduce negative emotions the user feels about studying. For example, if the user feels stressed, it can provide counseling advice. The communication unit can also suggest ways to relax if the user feels anxious about studying. For example, it can provide advice such as "Take a deep breath and relax." This can reduce negative emotions the user feels about studying and support the user in continuing to study.

[0077] The communication unit can provide a function for sharing learning progress with parents and teachers based on the user's learning history. For example, it can periodically report the user's learning status so that parents and teachers can provide appropriate support. The communication unit can also provide parents and teachers with specific advice based on the user's learning progress and level of understanding. For example, if the user's level of understanding of a particular unit is low, it can suggest supplementary materials for that unit. This makes it easier for parents and teachers to support the user's learning.

[0078] The communication unit can use the emotion estimation function to provide a feedback function to reinforce the positive emotions the user feels about learning. For example, the user can receive positive feedback as they progress in their studies. The communication unit can also provide encouraging messages when the user achieves a specific goal. For example, it can send a message such as "Great! Keep it up!" This can reinforce the positive emotions the user feels about learning and increase their motivation to study.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The communication department communicates with the user through LINE's chat function. For example, if a user sends a message saying, "Let's start studying math today," the communication department responds with, "Which unit will you study?" The communication department can also analyze the user's past conversation history and generate personalized responses based on individual learning styles and preferences. Step 2: The analysis unit analyzes the user's learning progress and level of understanding obtained by the communication unit. For example, if a user reports that they "couldn't solve yesterday's problem," the analysis unit checks the content of the problem and identifies the part where the user is struggling. The analysis unit can also analyze the user's learning data over time to track in detail the changes in learning progress and level of understanding. Step 3: The suggestion unit suggests optimal learning methods and learning materials based on the results of the analysis by the analysis unit. For example, if a user is struggling with a particular math unit, the suggestion unit will suggest supplementary learning materials and practice problems related to that unit. The suggestion unit can also dynamically adjust the optimal learning methods and learning materials based on the user's learning history and level of understanding, always providing the latest, optimized suggestions.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0094] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 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 communication department that communicates with users through the chat function; an analysis unit that analyzes the user's learning progress and understanding level acquired by the communication unit; a suggestion unit that suggests optimal learning methods and learning materials based on the results of the analysis by the analysis unit. A system characterized by:

2. The communication unit In addition to the chat function, the app will also provide users with interactive learning sessions using video calls and AR.

2. The system of claim 1.

3. The analysis unit The user's learning data is analyzed over time to track in detail the learning progress and changes in understanding.

2. The system of claim 1.

4. The proposal unit Based on the user's learning history and level of understanding, the optimal learning method and learning materials are dynamically adjusted, and the latest, optimized suggestions are always made.

2. The system of claim 1.

5. The communication unit Understanding the user's emotional state in real time and providing advice to relax if the user is feeling stressed or fatigued 2. The system of claim 1.

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