System

Abstract

An object of a system according to an embodiment is to improve the motivation of a learner and respond to individual learning needs.SOLUTION: A system according to an embodiment includes a quest generation unit, a persona generation unit, a feedback provision unit, and an emotion analysis unit. The quest generation unit analyzes the progress status of the learner and generates a quest to be challenged next. The persona generation unit analyzes the learner's profile or learning data based on the quest generated by the quest generation unit and generates an individual persona. The feedback providing unit visualizes the learner's performance based on the persona generated by the persona generation unit and provides feedback in real time. The emotion analysis unit analyzes the emotion of the learner based on the feedback provided by the feedback providing unit and adjusts the learning plan.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 making it difficult to maintain learners' motivation while meeting their individual learning needs.

[0005] The system according to the embodiment aims to improve the motivation of learners and to meet their individual learning needs. [Means for solving the problem]

[0006] The system according to the embodiment includes a quest generation unit, a persona generation unit, a feedback provision unit, and a sentiment analysis unit. The quest generation unit analyzes the learner's progress and generates the next quest to be attempted. The persona generation unit analyzes the learner's profile and learning data based on the quest generated by the quest generation unit and generates an individual persona. The feedback provision unit visualizes the learner's achievements based on the persona generated by the persona generation unit and provides feedback in real time. The sentiment analysis unit analyzes the learner's emotions based on the feedback provided by the feedback provision unit and adjusts the learning plan. [Effects of the Invention]

[0007] The system according to the embodiment can improve the motivation of learners and meet their individual learning needs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The edutainment system according to an embodiment of the present invention is a system that provides a learning experience for students that combines education and entertainment. This system can improve learners' motivation and respond to their individual learning needs. As a result, the edutainment system allows learners to study in an enjoyable manner and provides optimal learning plans that meet their individual learning needs.

[0029] An edutainment system according to an embodiment includes a quest generation unit, a persona generation unit, a feedback provision unit, and a sentiment analysis unit. The quest generation unit analyzes a learner's progress and generates a next quest to be attempted. For example, the quest generation unit generates a quest to solve a math problem or a quest to learn about historical events based on the learner's current progress and past learning data. The persona generation unit analyzes the learner's profile and learning data based on the quests generated by the quest generation unit and generates an individual persona. For example, the persona generation unit analyzes the learner's strong and weak subjects and learning style, and proposes an optimal study plan based on the results. The feedback provision unit visualizes the learner's achievements based on the persona generated by the persona generation unit and provides feedback in real time. For example, the feedback provision unit displays the learner's correct answer rate for questions solved and the progress of study time in graphs and charts. The sentiment analysis unit analyzes the learner's emotions based on the feedback provided by the feedback provision unit and adjusts the study plan. For example, the emotion analysis unit analyzes the learner's facial expressions and voice data, and if the learner is feeling stressed, the emotion analysis unit may lower the difficulty level or suggest taking a break. This allows the edutainment system according to the embodiment to improve the learner's motivation and respond to individual learning needs.

[0030] The quest generation unit can generate quests optimized for individual learning styles based on the learner's past learning history. For example, the quest generation unit analyzes the learner's past learning data to identify strong and weak subjects. For example, a learner who is good at mathematics can be provided with a more advanced mathematics quest. The quest generation unit also analyzes the learner's learning style (visual, auditory, experiential, etc.) and customizes the format of the quest based on that. For example, a visual learner can be provided with a quest that makes extensive use of diagrams and graphs. The quest generation unit also dynamically generates quests according to the learner's progress based on the learner's past learning history. For example, the difficulty of the next quest can be adjusted based on the correct answer rate of problems previously solved. This makes it possible to provide the learner with the optimal quest based on their past learning history.

[0031] The quest generation unit can customize rewards and incentives for learners according to their progress in the quest, thereby maintaining their motivation. For example, the quest generation unit introduces a system in which a learner can earn points or badges each time they complete a quest. For example, a special reward can be obtained when a certain number of points is reached. The quest generation unit also provides individual incentives according to the learner's progress. For example, bonus points can be awarded when quests are completed consecutively. The quest generation unit also provides feedback according to the learner's progress in the quest to maintain their motivation. For example, an encouraging message can be displayed each time a quest is completed. This makes it possible to provide rewards and incentives to maintain the learner's motivation.

[0032] The quest generation unit can promote social interaction by incorporating elements of cooperation and competition with other learners. For example, the quest generation unit introduces a team learning function in which learners cooperate with other learners to complete a quest. For example, it provides a quest in which learners solve problems in groups. The quest generation unit also introduces a ranking system in which learners can compete with each other. For example, it displays rankings based on the time it takes to complete a quest or the percentage of correct answers. The quest generation unit also adds a chat function that allows learners to communicate with other learners in real time. For example, learners can exchange advice while completing a quest. This can promote social interaction through cooperation and competition with other learners.

[0033] The quest generation unit can provide a more personalized learning experience by associating quests with the learner's interests and hobbies. For example, the quest generation unit customizes the theme of the quest based on the learner's interests and hobbies. For example, a learner who likes sports can be provided with sports-related math problems. The quest generation unit also generates quests including topics of interest based on the learner's profile information. For example, a learner who likes music can be provided with a music-related history quest. The quest generation unit also analyzes the learner's past learning history to identify topics that are likely to interest the learner. For example, a new quest can be generated based on the theme of a quest that was highly rated in the past. This makes it possible to provide a personalized learning experience based on the learner's interests and hobbies.

[0034] The persona generation unit can propose long-term learning goals and career plans based on the learner's persona. The persona generation unit, for example, sets long-term learning goals based on the learner's persona. For example, it proposes a future learning plan based on the learner's strong subjects and areas of interest. The persona generation unit also proposes a career plan based on the learner's persona. For example, it takes into account the learner's interests and areas of strength and suggests a suitable occupation or further education. The persona generation unit also specifically proposes steps for achieving learning goals based on the learner's persona. For example, it sets short-term and long-term goals and provides a learning plan based on them. In this way, it is possible to propose long-term learning goals and career plans based on the learner's persona.

[0035] When generating a persona, the persona generation unit also takes into account the learner's social background and cultural factors, making it possible to provide a more comprehensive learning plan. The persona generation unit, for example, generates a persona by taking into account the learner's social background (e.g., family environment and economic situation). For example, it proposes learning support that suits the family environment. The persona generation unit also generates a persona by taking into account the learner's cultural factors (e.g., language and religion). For example, it provides learning content that suits the cultural background. The persona generation unit also proposes a comprehensive learning plan based on the learner's social background and cultural factors. For example, it provides a learning plan that makes use of local educational resources. This makes it possible to provide a comprehensive learning plan that takes into account the learner's social background and cultural factors.

[0036] The persona generation unit can share the results of persona generation with parents and teachers, thereby strengthening the support system for learners. For example, the persona generation unit can share the results of persona generation for a learner with parents to strengthen learning support at home. For example, it can provide learning advice to parents. The persona generation unit can also share the results of persona generation for a learner with teachers to strengthen learning support at school. For example, it can propose individual teaching plans for teachers. The persona generation unit can also share the results of persona generation for a learner with both parents and teachers to build a comprehensive learning support system. For example, it can strengthen cooperation between home and school. In this way, the support system for learners can be strengthened by sharing the results of persona generation.

[0037] The persona generation unit can automatically recommend customized learning resources and teaching materials to a learner based on the persona. The persona generation unit, for example, builds a system that automatically recommends optimal learning resources and teaching materials based on the learner's persona. For example, teaching materials according to a learner's strong subjects are provided. The persona generation unit also proposes a customized learning plan based on the learner's persona. For example, resources that are useful for overcoming weak subjects are provided. The persona generation unit also recommends teaching materials that match the learner's learning style based on the learner's persona. For example, visual teaching materials are provided to a visual learner. In this way, customized learning resources and teaching materials can be automatically recommended to a learner.

[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 edutainment system can also collect learners' health data and reflect it in their study plans. For example, it can analyze a learner's sleep data and adjust the study plan if the learner is not getting enough sleep. It can also generate quests to encourage moderate exercise based on the learner's exercise data. It can also collect learners' dietary data and propose study plans that take nutritional balance into account. This makes it possible to provide the optimal study plan that takes the learner's health into account.

[0040] The edutainment system can provide a dashboard that visualizes a learner's progress based on their learning data. For example, it can display a graph of the learner's study time and correct answer rate. It can also display a learner's strong and weak subjects by color. It can also suggest what content the learner should study next based on their progress. This makes it possible to provide a dashboard that allows learners to grasp their progress at a glance.

[0041] Edutainment systems can automatically generate learning materials that match a learner's learning style based on the learner's learning data. For example, visual learning materials can be provided to visual learners. Audio learning materials can also be provided to auditory learners. Furthermore, interactive learning materials can be provided to experience-oriented learners. This makes it possible to provide optimal learning materials that match each learner's learning style.

[0042] Edutainment systems can implement a reward system based on learners' learning data according to their progress. For example, learners can earn points or badges when they achieve certain goals. Also, bonus points can be awarded if learners continue to study continuously. Furthermore, it is possible to provide special rewards when learners complete specific challenges. This makes it possible to provide a reward system that keeps learners motivated.

[0043] The edutainment system can automatically adjust the learning plan according to the learner's progress based on the learner's learning data. For example, if a learner is struggling with a particular subject, it can provide a learning plan that focuses on that subject. It can also provide more advanced content for subjects in which the learner excels. It can also dynamically change the learning plan according to the learner's progress. This makes it possible to provide the optimal learning plan according to the learner's progress.

[0044] Based on the learner's learning data, the edutainment system can provide a learning environment that suits the learner's learning style. For example, a learning environment that makes heavy use of visual elements can be provided for visual learners. Audio guides can also be provided for auditory learners. Furthermore, it is possible to provide an interactive learning environment for experience-oriented learners. This makes it possible to provide the optimal learning environment that suits each learner's learning style.

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

[0046] Step 1: The quest generator analyzes the learner's progress and generates the next quest to be attempted. For example, the quest generator generates a quest to solve a math problem or a quest to learn about a historical event based on the learner's current progress and past learning data. Step 2: The persona generation unit analyzes the learner's profile and learning data based on the quests generated by the quest generation unit, and generates an individual persona. For example, the persona generation unit analyzes the learner's strong and weak subjects and learning style, and then proposes an optimal learning plan based on that. Step 3: The feedback provider visualizes the learner's performance based on the persona generated by the persona generator and provides real-time feedback. For example, the feedback provider displays the learner's correct answer rate and progress in study time in graphs and charts. Step 4: The emotion analysis unit analyzes the learner's emotions based on the feedback provided by the feedback providing unit and adjusts the learning plan. For example, the emotion analysis unit analyzes the learner's facial expressions and voice data, and if the learner is feeling stressed, it may lower the difficulty level or suggest taking a break.

[0047] (Example 2) The edutainment system according to an embodiment of the present invention is a system that provides a learning experience for students that combines education and entertainment. This system can improve learners' motivation and respond to their individual learning needs. As a result, the edutainment system allows learners to study in an enjoyable manner and provides optimal learning plans that meet their individual learning needs.

[0048] An edutainment system according to an embodiment includes a quest generation unit, a persona generation unit, a feedback provision unit, and a sentiment analysis unit. The quest generation unit analyzes a learner's progress and generates a next quest to be attempted. For example, the quest generation unit generates a quest to solve a math problem or a quest to learn about historical events based on the learner's current progress and past learning data. The persona generation unit analyzes the learner's profile and learning data based on the quests generated by the quest generation unit and generates an individual persona. For example, the persona generation unit analyzes the learner's strong and weak subjects and learning style, and proposes an optimal study plan based on the results. The feedback provision unit visualizes the learner's achievements based on the persona generated by the persona generation unit and provides feedback in real time. For example, the feedback provision unit displays the learner's correct answer rate for questions solved and the progress of study time in graphs and charts. The sentiment analysis unit analyzes the learner's emotions based on the feedback provided by the feedback provision unit and adjusts the study plan. For example, the emotion analysis unit analyzes the learner's facial expressions and voice data, and if the learner is feeling stressed, the emotion analysis unit may lower the difficulty level or suggest taking a break. This allows the edutainment system according to the embodiment to improve the learner's motivation and respond to individual learning needs.

[0049] The quest generation unit can analyze the learner's emotions in real time and dynamically adjust the difficulty and content of the quest according to the learner's emotions. The quest generation unit, for example, analyzes the learner's facial expressions and voice data in real time to detect changes in emotions. For example, if the learner is tired, it may lower the difficulty of the quest or suggest a break. The quest generation unit also dynamically changes the content of the quest based on the learner's emotional data. For example, if the learner is excited, it may provide a more challenging quest. The quest generation unit also provides feedback to maintain the learner's motivation based on the emotion analysis results. For example, if the learner is depressed, it may display an encouraging message. This makes it possible to provide the optimal quest according to the learner's emotions.

[0050] The quest generation unit can generate quests optimized for individual learning styles based on the learner's past learning history. For example, the quest generation unit analyzes the learner's past learning data to identify strong and weak subjects. For example, a learner who is good at mathematics can be provided with a more advanced mathematics quest. The quest generation unit also analyzes the learner's learning style (visual, auditory, experiential, etc.) and customizes the format of the quest based on that. For example, a visual learner can be provided with a quest that makes extensive use of diagrams and graphs. The quest generation unit also dynamically generates quests according to the learner's progress based on the learner's past learning history. For example, the difficulty of the next quest can be adjusted based on the correct answer rate of problems previously solved. This makes it possible to provide the learner with the optimal quest based on their past learning history.

[0051] The quest generation unit can customize rewards and incentives for learners according to their progress in the quest, thereby maintaining their motivation. For example, the quest generation unit introduces a system in which a learner can earn points or badges each time they complete a quest. For example, a special reward can be obtained when a certain number of points is reached. The quest generation unit also provides individual incentives according to the learner's progress. For example, bonus points can be awarded when quests are completed consecutively. The quest generation unit also provides feedback according to the learner's progress in the quest to maintain their motivation. For example, an encouraging message can be displayed each time a quest is completed. This makes it possible to provide rewards and incentives to maintain the learner's motivation.

[0052] The quest generation unit can promote social interaction by incorporating elements of cooperation and competition with other learners. For example, the quest generation unit introduces a team learning function in which learners cooperate with other learners to complete a quest. For example, it provides a quest in which learners solve problems in groups. The quest generation unit also introduces a ranking system in which learners can compete with each other. For example, it displays rankings based on the time it takes to complete a quest or the percentage of correct answers. The quest generation unit also adds a chat function that allows learners to communicate with other learners in real time. For example, learners can exchange advice while completing a quest. This can promote social interaction through cooperation and competition with other learners.

[0053] The quest generation unit can provide a more personalized learning experience by associating quests with the learner's interests and hobbies. For example, the quest generation unit customizes the theme of the quest based on the learner's interests and hobbies. For example, a learner who likes sports can be provided with sports-related math problems. The quest generation unit also generates quests including topics of interest based on the learner's profile information. For example, a learner who likes music can be provided with a music-related history quest. The quest generation unit also analyzes the learner's past learning history to identify topics that are likely to interest the learner. For example, a new quest can be generated based on the theme of a quest that was highly rated in the past. This makes it possible to provide a personalized learning experience based on the learner's interests and hobbies.

[0054] The quest generation unit can use the emotion estimation function to analyze the emotion expressed by the learner when he or she completes a quest and provide feedback that elicits positive emotions. The quest generation unit, for example, analyzes the facial expressions and voice of the learner when he or she completes a quest to estimate the emotion. For example, if a joyful expression is detected, the quest generation unit suggests a more challenging quest. The quest generation unit also provides positive feedback to the learner based on the emotion estimation results. For example, it displays an encouraging message when the quest is completed. The quest generation unit also accumulates emotional data of the learner and analyzes patterns for eliciting positive emotions. For example, if specific feedback elicits positive emotions, it provides that feedback preferentially. This makes it possible to provide feedback that elicits positive emotions from the learner.

[0055] The persona generation unit can generate a more detailed persona by integrating various data sources, including the learner's emotional data. The persona generation unit generates a detailed persona by integrating, for example, the learner's emotional data, learning history, and profile information. For example, the learner's stress level is evaluated based on the emotional data and reflected in the persona. The persona generation unit also collects the learner's emotional data in real time and uses it to generate the persona. For example, emotional changes during learning are analyzed and reflected in the learning style. The persona generation unit also integrates various data sources (for example, the learner's social media data) to generate a more comprehensive persona. For example, the learner's interests and hobbies are reflected in the persona. In this way, a detailed persona can be generated by integrating various data sources, including the learner's emotional data.

[0056] The persona generation unit can propose long-term learning goals and career plans based on the learner's persona. The persona generation unit, for example, sets long-term learning goals based on the learner's persona. For example, it proposes a future learning plan based on the learner's strong subjects and areas of interest. The persona generation unit also proposes a career plan based on the learner's persona. For example, it takes into account the learner's interests and areas of strength and suggests a suitable occupation or further education. The persona generation unit also specifically proposes steps for achieving learning goals based on the learner's persona. For example, it sets short-term and long-term goals and provides a learning plan based on them. In this way, it is possible to propose long-term learning goals and career plans based on the learner's persona.

[0057] When generating a persona, the persona generation unit also takes into account the learner's social background and cultural factors, making it possible to provide a more comprehensive learning plan. The persona generation unit, for example, generates a persona by taking into account the learner's social background (e.g., family environment and economic situation). For example, it proposes learning support that suits the family environment. The persona generation unit also generates a persona by taking into account the learner's cultural factors (e.g., language and religion). For example, it provides learning content that suits the cultural background. The persona generation unit also proposes a comprehensive learning plan based on the learner's social background and cultural factors. For example, it provides a learning plan that makes use of local educational resources. This makes it possible to provide a comprehensive learning plan that takes into account the learner's social background and cultural factors.

[0058] The persona generation unit can share the results of persona generation with parents and teachers, thereby strengthening the support system for learners. For example, the persona generation unit can share the results of persona generation for a learner with parents to strengthen learning support at home. For example, it can provide learning advice to parents. The persona generation unit can also share the results of persona generation for a learner with teachers to strengthen learning support at school. For example, it can propose individual teaching plans for teachers. The persona generation unit can also share the results of persona generation for a learner with both parents and teachers to build a comprehensive learning support system. For example, it can strengthen cooperation between home and school. In this way, the support system for learners can be strengthened by sharing the results of persona generation.

[0059] The persona generation unit can automatically recommend customized learning resources and teaching materials to a learner based on the persona. The persona generation unit, for example, builds a system that automatically recommends optimal learning resources and teaching materials based on the learner's persona. For example, teaching materials according to a learner's strong subjects are provided. The persona generation unit also proposes a customized learning plan based on the learner's persona. For example, resources that are useful for overcoming weak subjects are provided. The persona generation unit also recommends teaching materials that match the learner's learning style based on the learner's persona. For example, visual teaching materials are provided to a visual learner. In this way, customized learning resources and teaching materials can be automatically recommended to a learner.

[0060] The persona generation unit uses the emotion estimation function to dynamically update the persona according to the learner's emotional state, thereby providing an optimal learning plan in real time. The persona generation unit, for example, uses the emotion estimation function to analyze the learner's emotional state in real time and dynamically update the persona. For example, if the learner is feeling stressed, the learning plan is adjusted. The persona generation unit also builds a system that provides an optimal learning plan in real time based on the learner's emotional data. For example, it suggests breaks and refreshments according to the learner's emotional state. The persona generation unit also continuously updates the learner's persona based on the emotion estimation data and dynamically adjusts the learning plan. For example, it changes the learning content according to changes in emotions. This makes it possible to dynamically update the persona according to the learner's emotional state and provide an optimal learning plan in real time.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The edutainment system can also collect learners' health data and reflect it in their study plans. For example, it can analyze a learner's sleep data and adjust the study plan if the learner is not getting enough sleep. It can also generate quests to encourage moderate exercise based on the learner's exercise data. It can also collect learners' dietary data and propose study plans that take nutritional balance into account. This makes it possible to provide the optimal study plan that takes the learner's health into account.

[0063] The edutainment system can estimate the learner's emotions and adjust the learning environment based on the estimated emotions. For example, if the learner is feeling stressed, it can play relaxing music. If the learner is concentrating, it can turn off notifications to provide an environment where the learner can concentrate on their studies. Furthermore, if the learner is tired, it can suggest taking a break. This makes it possible to provide the optimal learning environment according to the learner's emotions.

[0064] The edutainment system can estimate the learner's emotions and customize learning content based on the estimated emotions. For example, if the learner is excited, it can provide more challenging problems. If the learner is depressed, it can provide easier problems to restore their confidence. Furthermore, if the learner is having fun, it can provide learning content that incorporates game elements. This makes it possible to provide optimal learning content according to the learner's emotions.

[0065] The edutainment system can estimate the learner's emotions and evaluate their learning progress based on the estimated emotions. For example, if the learner is feeling stressed, the progress evaluation can be relaxed. Also, if the learner is concentrating, the progress evaluation can be made stricter. Furthermore, if the learner is enjoying themselves, the progress evaluation can be flexibly adjusted. This makes it possible to provide the optimal progress evaluation according to the learner's emotions.

[0066] The edutainment system can estimate the learner's emotions and provide learning feedback based on the estimated emotions. For example, if the learner is happy, it can provide positive feedback. If the learner is depressed, it can display an encouraging message. Furthermore, if the learner is excited, it can provide advice on how to proceed to the next step. This allows the system to provide optimal feedback according to the learner's emotions.

[0067] The edutainment system can provide a dashboard that visualizes a learner's progress based on their learning data. For example, it can display a graph of the learner's study time and correct answer rate. It can also display a learner's strong and weak subjects by color. It can also suggest what content the learner should study next based on their progress. This makes it possible to provide a dashboard that allows learners to grasp their progress at a glance.

[0068] Edutainment systems can automatically generate learning materials that match a learner's learning style based on the learner's learning data. For example, visual learning materials can be provided to visual learners. Audio learning materials can also be provided to auditory learners. Furthermore, interactive learning materials can be provided to experience-oriented learners. This makes it possible to provide optimal learning materials that match each learner's learning style.

[0069] Edutainment systems can implement a reward system based on learners' learning data according to their progress. For example, learners can earn points or badges when they achieve certain goals. Also, bonus points can be awarded if learners continue to study continuously. Furthermore, it is possible to provide special rewards when learners complete specific challenges. This makes it possible to provide a reward system that keeps learners motivated.

[0070] The edutainment system can automatically adjust the learning plan according to the learner's progress based on the learner's learning data. For example, if a learner is struggling with a particular subject, it can provide a learning plan that focuses on that subject. It can also provide more advanced content for subjects in which the learner excels. It can also dynamically change the learning plan according to the learner's progress. This makes it possible to provide the optimal learning plan according to the learner's progress.

[0071] Based on the learner's learning data, the edutainment system can provide a learning environment that suits the learner's learning style. For example, a learning environment that makes heavy use of visual elements can be provided for visual learners. Audio guides can also be provided for auditory learners. Furthermore, it is possible to provide an interactive learning environment for experience-oriented learners. This makes it possible to provide the optimal learning environment that suits each learner's learning style.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The quest generator analyzes the learner's progress and generates the next quest to be attempted. For example, the quest generator generates a quest to solve a math problem or a quest to learn about a historical event based on the learner's current progress and past learning data. Step 2: The persona generation unit analyzes the learner's profile and learning data based on the quests generated by the quest generation unit, and generates an individual persona. For example, the persona generation unit analyzes the learner's strong and weak subjects and learning style, and then proposes an optimal learning plan based on that. Step 3: The feedback provider visualizes the learner's performance based on the persona generated by the persona generator and provides real-time feedback. For example, the feedback provider displays the learner's correct answer rate and progress in study time in graphs and charts. Step 4: The emotion analysis unit analyzes the learner's emotions based on the feedback provided by the feedback providing unit and adjusts the learning plan. For example, the emotion analysis unit analyzes the learner's facial expressions and voice data, and if the learner is feeling stressed, it may lower the difficulty level or suggest taking a break.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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]

[0141] 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 quest generation unit that analyzes the learner's progress and generates the next quest to be attempted; a persona generation unit that analyzes a learner's profile and learning data based on the quest generated by the quest generation unit and generates an individual persona; a feedback providing unit that visualizes the learner's achievement based on the persona generated by the persona generating unit and provides feedback in real time; a sentiment analysis unit that analyzes the learner's sentiment based on the feedback provided by the feedback providing unit and adjusts the learning plan. A system characterized by:

2. The quest generation unit Analyze learners' emotions in real time and dynamically adjust the difficulty and content of quests according to those emotions.

2. The system of claim 1.

3. The quest generation unit Generate quests optimized for individual learning styles based on learners' past learning history 2. The system of claim 1.

4. The quest generation unit Customize rewards and incentives to keep learners motivated as they progress through their quests 2. The system of claim 1.

5. The quest generation unit Encourage social interaction by incorporating elements of cooperation and competition with other learners 2. The system of claim 1.

Images