system

Abstract

The system according to the embodiment aims to provide learners with individualized learning plans and to support them efficiently. [Solution] A system according to an embodiment includes a reception unit, a generation unit, and a support unit. The reception unit receives input of study content from a user. The generation unit analyzes the information received by the reception unit and generates a study plan. The support unit provides interactive support based on the study plan generated by the generation unit.

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 provide learners with personalized learning plans and provide efficient support.

[0005] The system according to the embodiment aims to provide learners with individualized learning plans and to support them efficiently. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a support unit. The reception unit receives input of study content from a user. The generation unit analyzes the information received by the reception unit and generates a study plan. The support unit provides interactive support based on the study plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a learner with an individualized learning plan and provide efficient support. [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) A support-based online education system according to an embodiment of the present invention utilizes a generative AI to solve various problems associated with traditional cram schools and online education. In this system, a user inputs the content they want to learn, and the generative AI analyzes the content to generate an optimal study plan. The system efficiently provides optimal information while engaging the user through interactive support. For example, if a user inputs "I want to learn calculus," this information is input to the generative AI. The generative AI analyzes the input information and generates an optimal study plan based on the user's learning history and level of understanding. For example, if the user understands the basics of calculus, the generative AI generates a study plan that includes more advanced content. Based on the generated study plan, the generative AI efficiently provides optimal information while engaging the user through interactive support. For example, the generative AI poses questions to the user and suggests next steps based on the user's answers. In this way, users can progress through their studies at their own pace. This system solves the problems associated with traditional cram schools and online education. For example, even in large classes where individual instruction is difficult, the generative AI can provide optimal study plans for each user, enabling effective learning. In addition, the generative AI provides efficient information while engaging the user's interest through interactive support, thereby increasing motivation to study. Furthermore, the generative AI constantly monitors the user's learning history and level of understanding, revising the learning plan as necessary. This allows the user to always proceed with their studies based on the optimal learning plan. For example, if the user struggles with a particular topic, the generative AI provides additional learning materials on that topic. In this way, the user can study effectively at their own pace. This system is a groundbreaking mechanism for solving the challenges of traditional cram schools and online education and supporting effective learning. This enables the supportive online education system to efficiently accept and analyze the user's learning content and provide the optimal learning plan.

[0029] A support-type online education system according to an embodiment includes a reception unit, a generation unit, and a support unit. The reception unit receives input of learning content from a user. For example, the user may input, "I want to learn differential and integral calculus in mathematics." This information is then input to a generation AI. The generation unit uses the generation AI to analyze the information received by the reception unit and generate an optimal learning plan. The generation AI considers the user's learning history and level of understanding and proposes an optimal learning plan for each individual user. For example, if a user has a basic understanding of differential and integral calculus, the generation AI generates a learning plan that includes more advanced content. The generation unit can also use the generation AI to constantly monitor the user's learning history and level of understanding and modify the learning plan as needed. For example, if a user has difficulty with a particular topic, the generation AI provides additional learning materials related to that topic. The support unit provides interactive support based on the learning plan generated by the generation unit. For example, the generation AI poses questions to the user and suggests next steps based on the user's answers. In this way, the user can progress through their learning at their own pace. The support unit can also use the generation AI to efficiently provide optimal information while engaging the user's interest. For example, the generation AI suggests interesting topics to the user and provides information about those topics. This allows the support-based online education system to efficiently accept and analyze the user's learning content and provide an optimal learning plan. Furthermore, the support department can use the generation AI to constantly monitor the user's learning history and level of understanding and modify the learning plan as necessary. For example, if the user has difficulty with a particular topic, the generation AI will provide additional learning materials on that topic. This allows the support-based online education system to efficiently accept and analyze the user's learning content and provide an optimal learning plan.

[0030] The generation unit can generate a study plan based on the user's learning history and level of understanding. The generation unit generates an optimal study plan based on, for example, the user's past learning records and test results. For example, the generation unit considers the user's past learning content and level of understanding and suggests the next content to study. The generation unit can also analyze the user's learning history and dynamically modify the study plan. For example, if the user has difficulty with a particular topic, the generation unit can provide additional learning materials related to that topic. Furthermore, the generation unit can evaluate the user's level of understanding and adjust the study plan. For example, the generation unit tests whether the user understands a particular topic and modifies the study plan based on the results. In this way, the generation unit can provide an individually optimized study plan based on the user's learning history and level of understanding.

[0031] The support unit can analyze the user's answers and suggest the next step. For example, the support unit can analyze the content of the user's answers and suggest the next content to be studied. For example, when the user answers a multiple-choice question, the support unit can suggest the next step based on the answer. Also, when the user answers a written question, the support unit can analyze the content of the answer and suggest the next content to be studied. Furthermore, the support unit can analyze the user's answer history and suggest the next step. For example, the support unit can suggest the next content to be studied based on the content of the user's past answers. In this way, the support unit can support the progress of learning by suggesting an appropriate next step based on the user's answers.

[0032] The support unit can monitor the user's learning history and level of understanding and revise the learning plan. For example, the support unit monitors the user's learning history in real time and dynamically revise the learning plan. For example, if the user has difficulty with a particular topic, the support unit can provide additional learning materials on that topic. The support unit can also evaluate the user's level of understanding and adjust the learning plan. For example, the support unit can test whether the user understands a particular topic and revise the learning plan based on the results. Furthermore, the support unit can analyze the user's learning history and optimize the learning plan. For example, the support unit can take into account the content the user has previously studied and their level of understanding and suggest what content they should study next. In this way, the support unit can always provide optimal learning by dynamically revising the learning plan according to the user's learning history and level of understanding.

[0033] The support unit can ask the user questions and suggest the next step based on the user's answer. For example, the support unit can ask the user open-ended questions and suggest the next step based on the answer. For example, the support unit can ask the user, "Do you understand the basics of differential calculus?" and suggest the next learning content based on the user's answer. The support unit can also ask the user closed-ended questions and suggest the next step based on the answer. For example, if the user answers "yes" or "no" to the question, "Do you understand the basics of differential calculus?", the support unit can suggest the next learning content based on the answer. Furthermore, the support unit can analyze the user's answer history and suggest the next step. For example, the support unit can suggest the next learning content based on the user's past answers. In this way, the support unit can effectively support the progress of learning by suggesting the next step based on the user's answers.

[0034] The support unit can efficiently provide information while attracting the user's interest. For example, the support unit suggests interesting topics to the user and provides information related to those topics. For example, if the user inputs, "I'm interested in differential calculus in mathematics," the support unit provides information related to that topic. The support unit can also provide interactive quizzes to the user, efficiently providing information while attracting the user's interest. For example, the support unit can provide a "quiz on the basics of differential calculus" to the user and provide information through the quiz. Furthermore, the support unit can provide personalized feedback by taking into account the user's learning history and level of understanding. For example, the support unit can suggest the next content to study based on the user's past learning and level of understanding. In this way, the support unit can efficiently provide information while attracting the user's interest, thereby improving learning motivation.

[0035] The reception unit can analyze the user's past learning history and select the optimal input method. For example, the reception unit preferentially suggests an input method (voice, text, etc.) that the user has used preferentially in the past. For example, if the user has used voice input preferentially in the past, the reception unit preferentially suggests voice input. The reception unit can also select the most efficient input method based on the user's past learning history. For example, if the user has achieved high learning results with text input in the past, the reception unit preferentially suggests text input. Furthermore, the reception unit can also suggest the optimal input method based on the user's successful learning methods in the past. For example, the reception unit suggests voice input based on the user's successful learning methods in the past with voice input. In this way, the reception unit can improve learning efficiency by providing the optimal input method based on the user's past learning history.

[0036] When inputting learning content, the reception unit can filter the learning content based on the user's current learning status and areas of interest. For example, the reception unit prioritizes displaying content related to the topic the user is currently studying. For example, if the user is currently studying "calculus," the reception unit prioritizes displaying content related to that topic. The reception unit can also filter related learning content based on the user's areas of interest. For example, if the user is interested in "physics," the reception unit prioritizes displaying learning content related to that field. Furthermore, the reception unit can suggest learning content of an appropriate level of difficulty depending on the user's learning progress. For example, if the user understands the basics of "calculus," the reception unit suggests a learning plan including more advanced content. In this way, the reception unit can provide appropriate learning content by filtering based on the user's current learning status and areas of interest.

[0037] When inputting study content, the reception unit can prioritize inputting related content taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes inputting study content related to that area. For example, if the user is in "Tokyo," the reception unit prioritizes inputting study content related to that area. Furthermore, if the user is traveling, the reception unit can prioritize inputting study content related to the travel destination. For example, if the user is traveling to "Kyoto," the reception unit prioritizes inputting study content related to that area. Furthermore, if the user is in a specific school or facility, the reception unit can prioritize inputting study content related to that location. For example, if the user is in a "university library," the reception unit prioritizes inputting study content related to that location. In this way, the reception unit can improve the effectiveness of learning by providing highly relevant study content based on the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity and input related content when inputting study content. For example, the reception unit inputs related study content based on content shared by the user on social media. For example, if the user shares an article about calculus, the reception unit inputs study content related to that content. The reception unit can also input related study content based on accounts the user follows on social media. For example, if the user follows a math expert, the reception unit inputs study content related to that content. The reception unit can also input related study content based on groups the user joins on social media. For example, if the user joins a group of math enthusiasts, the reception unit inputs study content related to that content. In this way, the reception unit can improve the effectiveness of learning by providing related study content based on the user's social media activity.

[0039] When generating a study plan, the generation unit can adjust the content of the plan based on the user's study history and level of understanding. The generation unit, for example, generates a detailed study plan based on content that the user has studied in the past. For example, the generation unit generates a detailed study plan based on content that the user has studied in the past. The generation unit can also generate a concise study plan according to the user's level of understanding. For example, the generation unit generates a concise study plan according to the user's level of understanding. Furthermore, the generation unit can analyze the user's study history and generate a study plan with an optimal level of detail. For example, the generation unit analyzes the user's study history and generates a study plan with an optimal level of detail. In this way, the generation unit can support more effective study by adjusting the level of detail of the plan based on the user's study history and level of understanding.

[0040] When generating a study plan, the generation unit can apply different generation algorithms depending on the category of the study content. For example, the generation unit applies a mathematical expression analysis algorithm to a mathematics study plan. For example, the generation unit applies a mathematical expression analysis algorithm to a mathematics study plan. The generation unit can also apply a time series analysis algorithm to a history study plan. For example, the generation unit applies a time series analysis algorithm to a history study plan. Furthermore, the generation unit can also apply a natural language processing algorithm to a language study plan. For example, the generation unit applies a natural language processing algorithm to a language study plan. In this way, the generation unit can provide a more effective study plan by applying an appropriate generation algorithm depending on the category of the study content.

[0041] When generating a learning plan, the generation unit can determine the order of the plans based on the submission dates of the learning content. For example, the generation unit prioritizes incorporating learning content with an upcoming submission deadline into the plan. For example, the generation unit prioritizes incorporating learning content with an upcoming submission deadline into the plan. The generation unit can also postpone learning content with a distant submission deadline. For example, the generation unit postpones learning content with a distant submission deadline. Furthermore, the generation unit can also adjust the schedule of the learning plan according to the submission deadline. For example, the generation unit adjusts the schedule of the learning plan according to the submission deadline. In this way, the generation unit can support efficient learning by determining the priority of the plans based on the submission dates of the learning content.

[0042] When generating a learning plan, the generation unit can adjust the order of the plan based on the relevance of the learning content. The generation unit, for example, incorporates highly relevant learning content into the plan consecutively. For example, the generation unit incorporates highly relevant learning content into the plan consecutively. The generation unit can also postpone less relevant learning content. For example, the generation unit postpones less relevant learning content. Furthermore, the generation unit can also adjust the order of the plan according to the relevance of the learning content. For example, the generation unit adjusts the order of the plan according to the relevance of the learning content. In this way, the generation unit can support efficient learning by adjusting the order of the plan based on the relevance of the learning content.

[0043] During interactive support, the support unit can select a support method by referring to the user's past response history. For example, the support unit prioritizes providing support methods that the user has previously preferred. For example, the support unit prioritizes providing support methods that the user has previously preferred. The support unit can also select an optimal support method from the user's past response history. For example, the support unit selects an optimal support method from the user's past response history. Furthermore, the support unit can also suggest an optimal support method based on support methods that the user has previously used successfully. For example, the support unit suggests an optimal support method based on support methods that the user has previously used successfully. In this way, the support unit can improve the effectiveness of learning by providing an optimal support method based on the user's past response history.

[0044] During interactive support, the support unit can customize support means based on the user's current learning situation. The support unit, for example, provides support related to a topic the user is currently learning. For example, if the user is currently learning "calculus," the support unit provides support related to that topic. The support unit can also provide appropriate support according to the user's learning progress. For example, the support unit provides appropriate support according to the user's learning progress. Furthermore, the support unit can also provide customized support based on the user's learning situation. For example, the support unit provides customized support based on the user's learning situation. In this way, the support unit can improve the effectiveness of learning by providing customized support according to the user's current learning situation.

[0045] During interactive support, the support unit can select a support method taking into account the user's geographical location information. For example, if the user is in a specific area, the support unit provides support related to that area. For example, if the user is in "Tokyo," the support unit provides support related to that area. Furthermore, if the user is traveling, the support unit can provide support related to the travel destination. For example, if the user is traveling to "Kyoto," the support unit provides support related to that area. Furthermore, if the user is in a specific school or facility, the support unit can provide support related to that location. For example, if the user is in the "university library," the support unit provides support related to that location. In this way, the support unit can improve learning effectiveness by providing the optimal support method based on the user's geographical location information.

[0046] During interactive support, the support unit can analyze the user's social media activity and suggest support methods. For example, the support unit provides relevant support based on the content shared by the user on social media. For example, if the user shares an article about calculus, the support unit provides support related to that content. The support unit can also provide relevant support based on accounts the user follows on social media. For example, if the user follows a math expert, the support unit provides support related to that content. Furthermore, the support unit can provide relevant support based on groups the user joins on social media. For example, if the user joins a group of math enthusiasts, the support unit provides support related to that content. In this way, the support unit can improve learning effectiveness by providing relevant support based on the user's social media activity.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] When the user inputs the learning content, the receiving unit can analyze the user's past learning patterns and suggest the optimal input method. For example, if the user has preferred voice input in the past, the receiving unit can preferentially suggest voice input. Also, if the user has previously achieved high learning results with text input, the receiving unit can preferentially suggest text input. Furthermore, the receiving unit can suggest the optimal input method based on the learning methods that the user has had success with in the past. In this way, the receiving unit can improve learning efficiency by providing the optimal input method based on the user's past learning patterns.

[0049] The generation unit can adjust the difficulty of the study plan based on the user's study history and level of comprehension. For example, the generation unit can adjust the difficulty of the content to be studied next based on the content the user has studied in the past. The generation unit can also dynamically change the difficulty of the study plan according to the user's level of comprehension. Furthermore, if the user has difficulty with a particular topic, the generation unit can adjust the difficulty of the study plan by providing additional study materials related to that topic. This allows the generation unit to provide an individually optimized study plan based on the user's study history and level of comprehension.

[0050] The support unit can take the user's learning style into consideration when analyzing the user's answers and suggesting the next step. For example, if the user prefers visual learning, visual learning materials can be suggested. If the user prefers auditory learning, audio learning materials can be suggested. Furthermore, if the user prefers practical learning, practical assignments can be suggested. In this way, the support unit can support the progress of learning by suggesting an appropriate next step according to the user's learning style.

[0051] The support unit can monitor the user's learning history and level of understanding, and take the user's learning goals into consideration when modifying the learning plan. For example, if the user's goal is to pass a specific exam, the support unit can provide a learning plan related to that exam. Also, if the user's goal is to acquire a specific skill, the support unit can provide a learning plan related to that skill. Furthermore, if the user is interested in a specific topic, the support unit can provide a learning plan related to that topic. In this way, the support unit can always provide optimal learning by dynamically modifying the learning plan according to the user's learning goals.

[0052] The support unit can take the user's learning pace into consideration when asking the user questions and suggesting the next step based on the user's answers. For example, if the user wants to study at a fast pace, the support unit can quickly suggest the next step. Conversely, if the user wants to study at a slower pace, the support unit can slowly suggest the next step. Furthermore, if the user gets stuck on a particular topic, the support unit can suggest the next step by providing additional learning materials on that topic. This allows the support unit to effectively support the progress of the user's learning by suggesting an appropriate next step according to the user's learning pace.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The reception unit accepts input of the user's learning content. For example, the user might input, "I want to learn differential and integral calculus in mathematics." This information is input into the generation AI. Step 2: The generation unit uses the generation AI to analyze the information received by the reception unit and generate an optimal study plan. The generation AI considers the user's learning history and level of understanding and proposes the optimal study plan for each individual user. For example, if the user has a basic understanding of calculus, it will generate a study plan that includes more advanced content. The generation unit can also use the generation AI to constantly monitor the user's learning history and level of understanding and revise the study plan as necessary. For example, if the user is struggling with a particular topic, the generation AI will provide additional study materials on that topic. Step 3: The support unit provides interactive support based on the learning plan generated by the generation unit. For example, the generation AI poses questions to the user and suggests the next step based on the user's answers. In this way, the user can progress through their learning at their own pace. The support unit can also use the generation AI to efficiently provide optimal information while attracting the user's interest. For example, the generation AI may suggest an interesting topic to the user and provide information on that topic.

[0055] (Example 2) A support-based online education system according to an embodiment of the present invention utilizes a generative AI to solve various problems associated with traditional cram schools and online education. In this system, a user inputs the content they want to learn, and the generative AI analyzes the content to generate an optimal study plan. The system efficiently provides optimal information while engaging the user through interactive support. For example, if a user inputs "I want to learn calculus," this information is input to the generative AI. The generative AI analyzes the input information and generates an optimal study plan based on the user's learning history and level of understanding. For example, if the user understands the basics of calculus, the generative AI generates a study plan that includes more advanced content. Based on the generated study plan, the generative AI efficiently provides optimal information while engaging the user through interactive support. For example, the generative AI poses questions to the user and suggests next steps based on the user's answers. In this way, users can progress through their studies at their own pace. This system solves the problems associated with traditional cram schools and online education. For example, even in large classes where individual instruction is difficult, the generative AI can provide optimal study plans for each user, enabling effective learning. In addition, the generative AI provides efficient information while engaging the user's interest through interactive support, thereby increasing motivation to study. Furthermore, the generative AI constantly monitors the user's learning history and level of understanding, revising the learning plan as necessary. This allows the user to always proceed with their studies based on the optimal learning plan. For example, if the user struggles with a particular topic, the generative AI provides additional learning materials on that topic. In this way, the user can study effectively at their own pace. This system is a groundbreaking mechanism for solving the challenges of traditional cram schools and online education and supporting effective learning. This enables the supportive online education system to efficiently accept and analyze the user's learning content and provide the optimal learning plan.

[0056] A support-type online education system according to an embodiment includes a reception unit, a generation unit, and a support unit. The reception unit receives input of learning content from a user. For example, the user may input, "I want to learn differential and integral calculus in mathematics." This information is then input to a generation AI. The generation unit uses the generation AI to analyze the information received by the reception unit and generate an optimal learning plan. The generation AI considers the user's learning history and level of understanding and proposes an optimal learning plan for each individual user. For example, if a user has a basic understanding of differential and integral calculus, the generation AI generates a learning plan that includes more advanced content. The generation unit can also use the generation AI to constantly monitor the user's learning history and level of understanding and modify the learning plan as needed. For example, if a user has difficulty with a particular topic, the generation AI provides additional learning materials related to that topic. The support unit provides interactive support based on the learning plan generated by the generation unit. For example, the generation AI poses questions to the user and suggests next steps based on the user's answers. In this way, the user can progress through their learning at their own pace. The support unit can also use the generation AI to efficiently provide optimal information while engaging the user's interest. For example, the generation AI suggests interesting topics to the user and provides information about those topics. This allows the support-based online education system to efficiently accept and analyze the user's learning content and provide an optimal learning plan. Furthermore, the support department can use the generation AI to constantly monitor the user's learning history and level of understanding and modify the learning plan as necessary. For example, if the user has difficulty with a particular topic, the generation AI will provide additional learning materials on that topic. This allows the support-based online education system to efficiently accept and analyze the user's learning content and provide an optimal learning plan.

[0057] The generation unit can generate a study plan based on the user's learning history and level of understanding. The generation unit generates an optimal study plan based on, for example, the user's past learning records and test results. For example, the generation unit considers the user's past learning content and level of understanding and suggests the next content to study. The generation unit can also analyze the user's learning history and dynamically modify the study plan. For example, if the user has difficulty with a particular topic, the generation unit can provide additional learning materials related to that topic. Furthermore, the generation unit can evaluate the user's level of understanding and adjust the study plan. For example, the generation unit tests whether the user understands a particular topic and modifies the study plan based on the results. In this way, the generation unit can provide an individually optimized study plan based on the user's learning history and level of understanding.

[0058] The support unit can analyze the user's answers and suggest the next step. For example, the support unit can analyze the content of the user's answers and suggest the next content to be studied. For example, when the user answers a multiple-choice question, the support unit can suggest the next step based on the answer. Also, when the user answers a written question, the support unit can analyze the content of the answer and suggest the next content to be studied. Furthermore, the support unit can analyze the user's answer history and suggest the next step. For example, the support unit can suggest the next content to be studied based on the content of the user's past answers. In this way, the support unit can support the progress of learning by suggesting an appropriate next step based on the user's answers.

[0059] The support unit can monitor the user's learning history and level of understanding and revise the learning plan. For example, the support unit monitors the user's learning history in real time and dynamically revise the learning plan. For example, if the user has difficulty with a particular topic, the support unit can provide additional learning materials on that topic. The support unit can also evaluate the user's level of understanding and adjust the learning plan. For example, the support unit can test whether the user understands a particular topic and revise the learning plan based on the results. Furthermore, the support unit can analyze the user's learning history and optimize the learning plan. For example, the support unit can take into account the content the user has previously studied and their level of understanding and suggest what content they should study next. In this way, the support unit can always provide optimal learning by dynamically revising the learning plan according to the user's learning history and level of understanding.

[0060] The support unit can ask the user questions and suggest the next step based on the user's answer. For example, the support unit can ask the user open-ended questions and suggest the next step based on the answer. For example, the support unit can ask the user, "Do you understand the basics of differential calculus?" and suggest the next learning content based on the user's answer. The support unit can also ask the user closed-ended questions and suggest the next step based on the answer. For example, if the user answers "yes" or "no" to the question, "Do you understand the basics of differential calculus?", the support unit can suggest the next learning content based on the answer. Furthermore, the support unit can analyze the user's answer history and suggest the next step. For example, the support unit can suggest the next learning content based on the user's past answers. In this way, the support unit can effectively support the progress of learning by suggesting the next step based on the user's answers.

[0061] The support unit can efficiently provide information while attracting the user's interest. For example, the support unit suggests interesting topics to the user and provides information related to those topics. For example, if the user inputs, "I'm interested in differential calculus in mathematics," the support unit provides information related to that topic. The support unit can also provide interactive quizzes to the user, efficiently providing information while attracting the user's interest. For example, the support unit can provide a "quiz on the basics of differential calculus" to the user and provide information through the quiz. Furthermore, the support unit can provide personalized feedback by taking into account the user's learning history and level of understanding. For example, the support unit can suggest the next content to study based on the user's past learning and level of understanding. In this way, the support unit can efficiently provide information while attracting the user's interest, thereby improving learning motivation.

[0062] The reception unit can estimate the user's emotions and adjust the timing of inputting study content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit prompts the user to input study content during a time when the user can relax. For example, if the reception unit estimates that the user is feeling stressed, the reception unit prompts the user to input "I want to learn differential and integral calculus" during a time when the user can relax. The reception unit can also prompt the user to input study content at a time when the user is concentrating. For example, if the reception unit estimates that the user is concentrating, the reception unit prompts the user to input "I want to learn differential and integral calculus" during that time. Furthermore, if the user is tired, the reception unit can prompt the user to input study content after a break. For example, if the reception unit estimates that the user is tired, the reception unit prompts the user to input "I want to learn differential and integral calculus" after a break. In this way, the reception unit can support more effective learning by adjusting the timing of inputting study content according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0063] The reception unit can analyze the user's past learning history and select the optimal input method. For example, the reception unit preferentially suggests an input method (voice, text, etc.) that the user has used preferentially in the past. For example, if the user has used voice input preferentially in the past, the reception unit preferentially suggests voice input. The reception unit can also select the most efficient input method based on the user's past learning history. For example, if the user has achieved high learning results with text input in the past, the reception unit preferentially suggests text input. Furthermore, the reception unit can also suggest the optimal input method based on the user's successful learning methods in the past. For example, the reception unit suggests voice input based on the user's successful learning methods in the past with voice input. In this way, the reception unit can improve learning efficiency by providing the optimal input method based on the user's past learning history.

[0064] When inputting learning content, the reception unit can filter the learning content based on the user's current learning status and areas of interest. For example, the reception unit prioritizes displaying content related to the topic the user is currently studying. For example, if the user is currently studying "calculus," the reception unit prioritizes displaying content related to that topic. The reception unit can also filter related learning content based on the user's areas of interest. For example, if the user is interested in "physics," the reception unit prioritizes displaying learning content related to that field. Furthermore, the reception unit can suggest learning content of an appropriate level of difficulty depending on the user's learning progress. For example, if the user understands the basics of "calculus," the reception unit suggests a learning plan including more advanced content. In this way, the reception unit can provide appropriate learning content by filtering based on the user's current learning status and areas of interest.

[0065] The reception unit can estimate the user's emotions and prioritize the learning content to be input based on the estimated user emotions. For example, if the user is excited, the reception unit prioritizes input of more difficult learning content. For example, if the reception unit estimates that the user is excited, the reception unit prioritizes input of "Applications of Calculus." Furthermore, if the user is relaxed, the reception unit can prioritize input of basic learning content. For example, if the reception unit estimates that the user is relaxed, the reception unit prioritizes input of "Fundamentals of Calculus." Furthermore, if the user is tired, the reception unit can prioritize input of easier learning content. For example, if the reception unit estimates that the user is tired, the reception unit prioritizes input of "Basic Concepts of Calculus." This allows the reception unit to prioritize learning content according to the user's emotions, thereby supporting more effective learning. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0066] When inputting study content, the reception unit can prioritize inputting related content taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes inputting study content related to that area. For example, if the user is in "Tokyo," the reception unit prioritizes inputting study content related to that area. Furthermore, if the user is traveling, the reception unit can prioritize inputting study content related to the travel destination. For example, if the user is traveling to "Kyoto," the reception unit prioritizes inputting study content related to that area. Furthermore, if the user is in a specific school or facility, the reception unit can prioritize inputting study content related to that location. For example, if the user is in a "university library," the reception unit prioritizes inputting study content related to that location. In this way, the reception unit can improve the effectiveness of learning by providing highly relevant study content based on the user's geographical location information.

[0067] The reception unit can analyze the user's social media activity and input related content when inputting study content. For example, the reception unit inputs related study content based on content shared by the user on social media. For example, if the user shares an article about calculus, the reception unit inputs study content related to that content. The reception unit can also input related study content based on accounts the user follows on social media. For example, if the user follows a math expert, the reception unit inputs study content related to that content. The reception unit can also input related study content based on groups the user joins on social media. For example, if the user joins a group of math enthusiasts, the reception unit inputs study content related to that content. In this way, the reception unit can improve the effectiveness of learning by providing related study content based on the user's social media activity.

[0068] The generation unit can estimate the user's emotions and adjust the presentation method of the study plan based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates a study plan with a visually relaxing design. For example, if the generation unit estimates that the user is relaxed, the generation unit generates a study plan with a visually relaxing design. Furthermore, if the user is excited, the generation unit can generate a study plan with a visually stimulating design. For example, if the generation unit estimates that the user is excited, the generation unit can generate a study plan with a visually stimulating design. Furthermore, if the user is tired, the generation unit can generate a study plan with a simple, highly visible design. For example, if the generation unit estimates that the user is tired, the generation unit generates a study plan with a simple, highly visible design. This allows the generation unit to support more effective learning by adjusting the presentation method of the study plan according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input user emotion data to the generation AI and have the generation AI estimate the emotion.

[0069] When generating a study plan, the generation unit can adjust the content of the plan based on the user's study history and level of understanding. The generation unit, for example, generates a detailed study plan based on content that the user has studied in the past. For example, the generation unit generates a detailed study plan based on content that the user has studied in the past. The generation unit can also generate a concise study plan according to the user's level of understanding. For example, the generation unit generates a concise study plan according to the user's level of understanding. Furthermore, the generation unit can analyze the user's study history and generate a study plan with an optimal level of detail. For example, the generation unit analyzes the user's study history and generates a study plan with an optimal level of detail. In this way, the generation unit can support more effective study by adjusting the level of detail of the plan based on the user's study history and level of understanding.

[0070] When generating a study plan, the generation unit can apply different generation algorithms depending on the category of the study content. For example, the generation unit applies a mathematical expression analysis algorithm to a mathematics study plan. For example, the generation unit applies a mathematical expression analysis algorithm to a mathematics study plan. The generation unit can also apply a time series analysis algorithm to a history study plan. For example, the generation unit applies a time series analysis algorithm to a history study plan. Furthermore, the generation unit can also apply a natural language processing algorithm to a language study plan. For example, the generation unit applies a natural language processing algorithm to a language study plan. In this way, the generation unit can provide a more effective study plan by applying an appropriate generation algorithm depending on the category of the study content.

[0071] The generation unit can estimate the user's emotions and adjust the length of the study plan based on the estimated user emotions. For example, if the user is in a hurry, the generation unit generates a short study plan. For example, if the generation unit estimates that the user is in a hurry, the generation unit generates a short study plan. The generation unit can also generate a longer study plan if the user is relaxed. For example, if the generation unit estimates that the user is relaxed, the generation unit generates a longer study plan. Furthermore, if the user is tired, the generation unit can also generate a short and concise study plan. For example, if the generation unit estimates that the user is tired, the generation unit generates a short and concise study plan. This allows the generation unit to support more effective learning by adjusting the length of the study plan according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0072] When generating a learning plan, the generation unit can determine the order of the plans based on the submission dates of the learning content. For example, the generation unit prioritizes incorporating learning content with an upcoming submission deadline into the plan. For example, the generation unit prioritizes incorporating learning content with an upcoming submission deadline into the plan. The generation unit can also postpone learning content with a distant submission deadline. For example, the generation unit postpones learning content with a distant submission deadline. Furthermore, the generation unit can also adjust the schedule of the learning plan according to the submission deadline. For example, the generation unit adjusts the schedule of the learning plan according to the submission deadline. In this way, the generation unit can support efficient learning by determining the priority of the plans based on the submission dates of the learning content.

[0073] When generating a learning plan, the generation unit can adjust the order of the plan based on the relevance of the learning content. The generation unit, for example, incorporates highly relevant learning content into the plan consecutively. For example, the generation unit incorporates highly relevant learning content into the plan consecutively. The generation unit can also postpone less relevant learning content. For example, the generation unit postpones less relevant learning content. Furthermore, the generation unit can also adjust the order of the plan according to the relevance of the learning content. For example, the generation unit adjusts the order of the plan according to the relevance of the learning content. In this way, the generation unit can support efficient learning by adjusting the order of the plan based on the relevance of the learning content.

[0074] The support unit can estimate the user's emotions and adjust the interactive support method based on the estimated user's emotions. For example, if the user is nervous, the support unit may use a calm tone when speaking. For example, if the support unit estimates that the user is nervous, the support unit may use a calm tone when speaking. Furthermore, if the user is relaxed, the support unit may use a friendly tone when speaking. For example, if the support unit estimates that the user is relaxed, the support unit may use a friendly tone when speaking. Furthermore, if the user is in a hurry, the support unit may use a quick and concise conversation. For example, if the support unit estimates that the user is in a hurry, the support unit may use a quick and concise conversation. This allows the support unit to adjust the interactive support method according to the user's emotions and provide more effective support. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the support department can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0075] During interactive support, the support unit can select a support method by referring to the user's past response history. For example, the support unit prioritizes providing support methods that the user has previously preferred. For example, the support unit prioritizes providing support methods that the user has previously preferred. The support unit can also select an optimal support method from the user's past response history. For example, the support unit selects an optimal support method from the user's past response history. Furthermore, the support unit can also suggest an optimal support method based on support methods that the user has previously used successfully. For example, the support unit suggests an optimal support method based on support methods that the user has previously used successfully. In this way, the support unit can improve the effectiveness of learning by providing an optimal support method based on the user's past response history.

[0076] During interactive support, the support unit can customize support means based on the user's current learning situation. The support unit, for example, provides support related to a topic the user is currently learning. For example, if the user is currently learning "calculus," the support unit provides support related to that topic. The support unit can also provide appropriate support according to the user's learning progress. For example, the support unit provides appropriate support according to the user's learning progress. Furthermore, the support unit can also provide customized support based on the user's learning situation. For example, the support unit provides customized support based on the user's learning situation. In this way, the support unit can improve the effectiveness of learning by providing customized support according to the user's current learning situation.

[0077] The support unit can estimate the user's emotions and determine the priority of interactive support based on the estimated user's emotions. For example, if the user is excited, the support unit can prioritize providing important support. For example, if the support unit estimates that the user is excited, the support unit can prioritize providing important support. The support unit can also prioritize providing basic support if the user is relaxed. For example, if the support unit estimates that the user is relaxed, the support unit can prioritize providing basic support. Furthermore, the support unit can prioritize providing simple support if the user is tired. For example, if the support unit estimates that the user is tired, the support unit can prioritize providing simple support. In this way, the support unit can determine the priority of interactive support according to the user's emotions, thereby providing more effective support. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the support unit can be performed, for example, using AI or without AI. For example, the support department can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0078] During interactive support, the support unit can select a support method taking into account the user's geographical location information. For example, if the user is in a specific area, the support unit provides support related to that area. For example, if the user is in "Tokyo," the support unit provides support related to that area. Furthermore, if the user is traveling, the support unit can provide support related to the travel destination. For example, if the user is traveling to "Kyoto," the support unit provides support related to that area. Furthermore, if the user is in a specific school or facility, the support unit can provide support related to that location. For example, if the user is in the "university library," the support unit provides support related to that location. In this way, the support unit can improve learning effectiveness by providing the optimal support method based on the user's geographical location information.

[0079] During interactive support, the support unit can analyze the user's social media activity and suggest support methods. For example, the support unit provides relevant support based on the content shared by the user on social media. For example, if the user shares an article about calculus, the support unit provides support related to that content. The support unit can also provide relevant support based on accounts the user follows on social media. For example, if the user follows a math expert, the support unit provides support related to that content. Furthermore, the support unit can provide relevant support based on groups the user joins on social media. For example, if the user joins a group of math enthusiasts, the support unit provides support related to that content. In this way, the support unit can improve learning effectiveness by providing relevant support based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and support unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives input of study content from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal study plan using a generation AI. The support unit is realized, for example, by the control unit 46A of the smart device 14 and provides interactive support based on the generated study plan. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and support unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives input of study content from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal study plan using a generation AI. The support unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides interactive support based on the generated study plan. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and support unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives input of study content from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal study plan using a generation AI. The support unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides interactive support based on the generated study plan. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and support unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives input of learning content from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal learning plan using a generation AI. The support unit is realized, for example, by the control unit 46A of the robot 414 and provides interactive support based on the generated learning plan.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] When the user inputs the learning content, the receiving unit can analyze the user's past learning patterns and suggest the optimal input method. For example, if the user has preferred voice input in the past, the receiving unit can preferentially suggest voice input. Also, if the user has previously achieved high learning results with text input, the receiving unit can preferentially suggest text input. Furthermore, the receiving unit can suggest the optimal input method based on the learning methods that the user has had success with in the past. In this way, the receiving unit can improve learning efficiency by providing the optimal input method based on the user's past learning patterns.

[0082] The generation unit can adjust the difficulty of the study plan based on the user's study history and level of comprehension. For example, the generation unit can adjust the difficulty of the content to be studied next based on the content the user has studied in the past. The generation unit can also dynamically change the difficulty of the study plan according to the user's level of comprehension. Furthermore, if the user has difficulty with a particular topic, the generation unit can adjust the difficulty of the study plan by providing additional study materials related to that topic. This allows the generation unit to provide an individually optimized study plan based on the user's study history and level of comprehension.

[0083] The support unit can take the user's learning style into consideration when analyzing the user's answers and suggesting the next step. For example, if the user prefers visual learning, visual learning materials can be suggested. If the user prefers auditory learning, audio learning materials can be suggested. Furthermore, if the user prefers practical learning, practical assignments can be suggested. In this way, the support unit can support the progress of learning by suggesting an appropriate next step according to the user's learning style.

[0084] The support unit can monitor the user's learning history and level of understanding, and take the user's learning goals into consideration when modifying the learning plan. For example, if the user's goal is to pass a specific exam, the support unit can provide a learning plan related to that exam. Also, if the user's goal is to acquire a specific skill, the support unit can provide a learning plan related to that skill. Furthermore, if the user is interested in a specific topic, the support unit can provide a learning plan related to that topic. In this way, the support unit can always provide optimal learning by dynamically modifying the learning plan according to the user's learning goals.

[0085] The support unit can take the user's learning pace into consideration when asking the user questions and suggesting the next step based on the user's answers. For example, if the user wants to study at a fast pace, the support unit can quickly suggest the next step. Conversely, if the user wants to study at a slower pace, the support unit can slowly suggest the next step. Furthermore, if the user gets stuck on a particular topic, the support unit can suggest the next step by providing additional learning materials on that topic. This allows the support unit to effectively support the progress of the user's learning by suggesting an appropriate next step according to the user's learning pace.

[0086] When providing information efficiently while attracting the user's interest, the support unit can estimate the user's emotions and adjust the method of providing information based on the estimated emotions. For example, if the user is excited, visually stimulating information can be provided. Also, if the user is relaxed, visually relaxing information can be provided. Furthermore, if the user is tired, simple, highly visible information can be provided. In this way, the support unit can improve learning motivation by adjusting the method of providing information according to the user's emotions.

[0087] The reception unit can estimate the user's emotions and adjust the timing of inputting study content based on the estimated emotions. For example, if the user is feeling stressed, the reception unit can encourage the user to input study content at a time when the user is able to relax. Also, if the user is concentrating, the reception unit can encourage the user to input study content at that time. Furthermore, if the user is tired, the reception unit can encourage the user to input study content after a break. In this way, the reception unit can support more effective learning by adjusting the timing of inputting study content according to the user's emotions.

[0088] The generation unit can estimate the user's emotions and adjust the presentation method of the study plan based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a study plan with a visually relaxing design. If the user is excited, the generation unit can generate a study plan with a visually stimulating design. Furthermore, if the user is tired, the generation unit can generate a study plan with a simple, highly visible design. In this way, the generation unit can support more effective study by adjusting the presentation method of the study plan according to the user's emotions.

[0089] The generation unit can estimate the user's emotions and adjust the length of the study plan based on the estimated emotions. For example, if the user is in a hurry, a short study plan can be generated. Also, if the user is relaxed, a longer study plan can be generated. Furthermore, if the user is tired, a short study plan that focuses on the main points can be generated. In this way, the generation unit can support more effective study by adjusting the length of the study plan according to the user's emotions.

[0090] The support unit can estimate the user's emotions and adjust the interactive support method based on the estimated emotions. For example, if the user is nervous, the conversation can be conducted in a calm tone. If the user is relaxed, the conversation can be conducted in a friendly tone. Furthermore, if the user is in a hurry, the conversation can be conducted quickly and concisely. In this way, the support unit can provide more effective support by adjusting the interactive support method according to the user's emotions.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The reception unit accepts input of the user's learning content. For example, the user might input, "I want to learn differential and integral calculus in mathematics." This information is input into the generation AI. Step 2: The generation unit uses the generation AI to analyze the information received by the reception unit and generate an optimal study plan. The generation AI considers the user's learning history and level of understanding and proposes the optimal study plan for each individual user. For example, if the user has a basic understanding of calculus, it will generate a study plan that includes more advanced content. The generation unit can also use the generation AI to constantly monitor the user's learning history and level of understanding and revise the study plan as necessary. For example, if the user is struggling with a particular topic, the generation AI will provide additional study materials on that topic. Step 3: The support unit provides interactive support based on the learning plan generated by the generation unit. For example, the generation AI poses questions to the user and suggests the next step based on the user's answers. In this way, the user can progress through their learning at their own pace. The support unit can also use the generation AI to efficiently provide optimal information while attracting the user's interest. For example, the generation AI may suggest an interesting topic to the user and provide information on that topic.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0095] 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The 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.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 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.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0107] 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.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0123] 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 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 identification processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0141] 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.

[0142] 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.

[0143] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 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 reception unit that receives input of learning content from a user; a generation unit that analyzes the information received by the reception unit and generates a study plan; a support unit that provides interactive support based on the learning plan generated by the generation unit. A system characterized by:

2. The generation unit Generate a learning plan based on the user's learning history and level of understanding 2. The system of claim 1.

3. The support portion is Analyze user responses and suggest next steps 2. The system of claim 1.

4. The support portion is Monitor users' learning history and comprehension and adjust their learning plans 2. The system of claim 1.

5. The support portion is Ask the user questions and suggest next steps based on their answers 2. The system of claim 1.

6. The support portion is Providing information efficiently while capturing the user's interest 2. The system of claim 1.

7. The reception unit Estimates the user's emotions and adjusts the timing of learning content input based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past learning history and select the input method 2. The system of claim 1.

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