system

Abstract

The system according to the embodiment aims to provide learning content that is suited to the cognitive characteristics of the user. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects a user's learning history, answer patterns, and reaction speed. The analysis unit analyzes the data collected by the collection unit and determines the user's cognitive characteristics. The generation unit generates study content based on the cognitive characteristics determined by the analysis 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 not adequately provided learning content tailored to the cognitive characteristics of individual users, and there is room for improvement.

[0005] The system according to the embodiment aims to provide learning content that is suited to the cognitive characteristics of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects a user's learning history, answer patterns, and reaction speed. The analysis unit analyzes the data collected by the collection unit and determines the user's cognitive characteristics. The generation unit generates study content based on the cognitive characteristics determined by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide learning content according to the cognitive characteristics of the user. [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 learning content generation system according to an embodiment of the present invention identifies a user's cognitive characteristics and generates learning content tailored to those characteristics. The learning content generation system collects a user's learning history, answer patterns, and reaction speed, and uses AI to analyze the user's cognitive characteristics and generate optimal learning content. For example, the learning content generation system collects detailed data, such as a user's learning history, answer patterns, and reaction speed. The learning content generation system then uses AI to analyze the collected data and identify the user's cognitive characteristics. For example, the system determines whether the user prefers visual information, auditory information, or tactile information. Based on the identified cognitive characteristics, the learning content generation system then generates optimal learning content. For example, for a user who prefers visual information, the system generates learning content that makes extensive use of diagrams and graphs, while for a user who prefers auditory information, the system generates learning content that makes extensive use of audio and music. Furthermore, for a user who prefers tactile information, the system generates learning content that includes many interactive elements. This allows the learning content generation system to provide an optimal learning experience for each individual user. This allows the learning content generation system to provide optimal learning content based on the user's cognitive characteristics. For example, users who prefer visual information will find it easier to understand learning content that makes extensive use of diagrams and graphs, while users who prefer auditory information can concentrate better through audio and music, and users who prefer tactile information can learn more hands-on through interactive elements.

[0029] The learning content generation system according to the embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects a user's learning history, answer patterns, and reaction speed. The user's learning history includes, but is not limited to, topics studied, study time, and study methods. The collection unit collects data such as what questions the user has answered in the past, how they answered them, and how long it took them to answer them. The collection unit can also collect the user's answer patterns. For example, the collection unit can collect data such as the correct answer rate, answer trends, and answer time. The collection unit can also collect the user's reaction speed. For example, the reaction time for a specific task can be measured in milliseconds. The analysis unit analyzes the data collected by the collection unit to determine the user's cognitive characteristics. Examples of cognitive characteristics include, but are not limited to, visual cognition, auditory cognition, and memory. The analysis unit can determine, for example, whether the user prefers visual information, auditory information, or tactile information. The analysis unit uses AI to analyze the collected data and identify the user's cognitive characteristics. For example, the AI ​​analyzes the user's learning history, answer patterns, and reaction speed to determine the user's cognitive characteristics. The generation unit generates learning content based on the cognitive characteristics determined by the analysis unit. Learning content includes, but is not limited to, text, video, and interactive quizzes. For example, the generation unit generates learning content that makes extensive use of diagrams and graphs for users who prefer visual information. The generation unit can also generate learning content that makes extensive use of audio and music for users who prefer auditory information. The generation unit can also generate learning content that includes many interactive elements for users who prefer tactile information. For example, the generation unit generates interactive quizzes and simulations to enable users to learn practically. This allows the learning content generation system according to the embodiment to provide optimal learning content based on the user's cognitive characteristics. Some or all of the above-described processing by the generation unit may be performed using AI, for example, or without AI.For example, the generation unit can generate learning content using an AI model that takes the cognitive characteristics determined by the analysis unit as input and outputs optimal learning content.

[0030] The collection unit can analyze the user's past learning history and select a data collection method. For example, the collection unit can prioritize collection of learning methods that were effective for the user in the past. The collection unit can also select an effective method by avoiding learning methods that the user was weak at in the past. The collection unit can also select a data collection method that is effective for a specific time period from the user's learning history. This enables effective data collection by selecting the optimal data collection method based on the user's past learning history. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the user's learning history data into a generation AI and have the generation AI select the optimal data collection method.

[0031] When collecting data, the collection unit can filter the data based on the user's current learning situation and areas of interest. For example, the collection unit collects only data related to the area the user is currently learning. The collection unit can also prioritize the collection of highly relevant data based on the user's areas of interest. The collection unit can also filter and collect only necessary data according to the user's learning progress. This makes it possible to collect highly relevant data by filtering data based on the user's current learning situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's learning situation data to a generation AI and have the generation AI perform filtering.

[0032] When collecting data, the collection unit can select a collection means according to the user's input method. For example, if the user prefers voice input, the collection unit can prioritize collecting voice data. Also, if the user prefers text input, the collection unit can prioritize collecting text data. Also, if the user prefers image input, the collection unit can prioritize collecting image data. This enables efficient data collection by selecting the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and have the generation AI select the optimal collection means.

[0033] When collecting data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, if the user is traveling, the collection unit can prioritize collecting data related to the user's destination. Furthermore, if the user stays in a specific location for a long time, the collection unit can prioritize collecting data related to that location. This enables more appropriate data collection by collecting highly relevant data based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location data to a generation AI and cause the generation AI to select highly relevant data.

[0034] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit collects related data based on information shared by the user on social media. The collection unit can also analyze the user's social media activity patterns and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. This enables more effective data collection by collecting related data based on the user's social media activities. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related data.

[0035] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit can adjust the collection method based on, for example, feedback provided by the user in the past. The collection unit can also select an effective collection method based on the user's past feedback. The collection unit can also improve the quality of collected data by reflecting the user's feedback. This enables more effective data collection by customizing the collection method based on the user's past feedback. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's feedback data into a generation AI and cause the generation AI to customize the collection method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a concise analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply an image analysis algorithm to visual data. The analysis unit can also apply a voice analysis algorithm to auditory data. The analysis unit can also apply a natural language processing algorithm to text data. This allows for more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also select an effective analysis method from the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by reflecting the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of older data. The analysis unit can also determine the analysis priority according to the time when the data was collected. This enables efficient analysis by determining the analysis priority based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0041] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit can provide analysis results that use a lot of technical terminology to a user with high levels of expertise. The analysis unit can also provide concise and easy-to-understand analysis results to a user with low levels of expertise. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to provide more appropriate analysis results by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise into the generation AI and have the generation AI use technical terminology.

[0042] When generating learning content, the generation unit can adjust the level of detail of the content based on the user's cognitive characteristics. For example, the generation unit can generate content that makes extensive use of detailed diagrams and graphs for a user who prefers visual information. The generation unit can also generate content that makes extensive use of detailed audio explanations for a user who prefers auditory information. The generation unit can also generate content that includes many interactive elements for a user who prefers tactile information. This allows for more effective learning content to be provided by adjusting the level of detail of the content based on the user's cognitive characteristics. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's cognitive characteristic data into the generation AI and cause the generation AI to adjust the level of detail of the content.

[0043] When generating study content, the generation unit can improve the accuracy of the content by referring to the user's learning history. The generation unit generates effective content based on, for example, the user's past learning history. The generation unit can also generate content that reinforces weak areas based on the user's past learning history. The generation unit can also improve the accuracy of the content by reflecting the user's past learning history. In this way, the accuracy of the content is improved by referring to the user's learning history. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's learning history data into the generation AI and have the generation AI improve the accuracy of the content.

[0044] When generating learning content, the generation unit can improve the content by reflecting user feedback. For example, the generation unit adjusts the content based on user feedback. The generation unit can also generate effective content from user feedback. The generation unit can also improve the quality of the content by reflecting user feedback. In this way, the quality of the content is improved by reflecting user feedback. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user feedback data into the generation AI and have the generation AI improve the content.

[0045] When generating study content, the generation unit can determine the priority of content based on the user's study progress. For example, the generation unit can prioritize generating content related to areas in which the user is making progress. The generation unit can also prioritize generating content related to areas in which the user is lagging behind. The generation unit can also prioritize generating necessary content according to the user's study progress. This allows for more effective study content to be provided by prioritizing content based on the user's study progress. Some or all of the above-described processing by the generation unit can be performed using, or without, AI. For example, the generation unit can input the user's study progress data into the generation AI and have the generation AI determine the priority of content.

[0046] When generating study content, the generation unit can adjust the order of content based on the user's areas of interest. For example, the generation unit can prioritize generating content related to the user's areas of interest. The generation unit can also prioritize generating highly relevant content based on the user's areas of interest. The generation unit can also prioritize generating necessary content according to the user's areas of interest. This allows for more effective study content to be provided by adjusting the order of content based on the user's areas of interest. Some or all of the above-described processing by the generation unit may be performed using, or without, AI. For example, the generation unit can input user's area of ​​interest data into the generation AI and have the generation AI adjust the order of the content.

[0047] When generating learning content, the generation unit can adjust the use of technical terms in the content according to the user's level of expertise. For example, the generation unit can generate content that uses a lot of technical terms for a user with high levels of expertise. The generation unit can also generate concise, easy-to-understand content for a user with low levels of expertise. The generation unit can also adjust the way the content is expressed according to the user's level of expertise. This allows for more appropriate learning content to be provided by adjusting the use of technical terms in the content according to the user's level of expertise. Some or all of the above-described processing by the generation unit can be performed using, or without, AI. For example, the generation unit can input the user's level of expertise data into the generation AI and have the generation AI execute the use of technical terms.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The analysis unit can estimate the user's learning style and adjust the analysis method based on the estimated learning style. For example, if the user prefers repeated learning, the analysis unit can analyze the same content multiple times from different angles. If the user prefers to process a lot of information at once, the analysis unit can provide a detailed analysis all at once. Furthermore, if the user prefers to learn in stages, the analysis unit can provide analysis results in stages. In this way, by adjusting the analysis method based on the user's learning style, more effective analysis results can be provided.

[0050] The generation unit can analyze the user's learning history and adjust the difficulty of the content based on the user's past learning results. For example, it can generate content with a high level of difficulty in areas in which the user has achieved good results in the past. It can also generate content that starts with basic content in areas in which the user is weak. It can also generate content that gradually increases in difficulty according to the user's learning progress. This allows the user to provide a more effective learning experience by adjusting the difficulty of the content based on the user's learning history.

[0051] The collection unit can detect the user's learning environment and adjust the data collection method according to the environment. For example, if the user is in a quiet environment, priority can be given to collecting voice data. Also, if the user is on the move, priority can be given to collecting text data. Furthermore, if the user is in a public place, a data collection method that takes privacy into consideration can be selected. This allows for more appropriate data collection by adjusting the data collection method according to the user's learning environment.

[0052] The analysis unit can set the user's learning goals and adjust the direction of analysis based on the goals. For example, if the user's goal is to pass a specific exam, the analysis can focus on content related to the exam. Also, if the user's goal is to acquire a specific skill, the analysis can also analyze content related to that skill. Furthermore, if the user's goal is self-improvement, the analysis can also analyze a wide range of content. In this way, by adjusting the direction of analysis based on the user's learning goals, more effective analysis results can be provided.

[0053] The generation unit can estimate the user's learning style and adjust the format of the content based on the estimated learning style. For example, if the user prefers visual learning, the generation unit can generate content that makes extensive use of diagrams and graphs. If the user prefers auditory learning, the generation unit can generate content that makes extensive use of audio and music. Furthermore, if the user prefers tactile learning, the generation unit can generate content that includes many interactive elements. In this way, by adjusting the format of the content based on the user's learning style, a more effective learning experience can be provided.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The collection unit collects the user's learning history, answer patterns, and reaction speed. The user's learning history includes the topics studied, study time, and study method. The collection unit collects data such as what questions the user has answered in the past, how they answered them, and how long it took them to answer them. The collection unit also collects answer patterns such as the accuracy rate, answer trends, and response time, as well as reaction speed, which measures the reaction time to a specific task in milliseconds. Step 2: The analysis unit analyzes the data collected by the collection unit and determines the user's cognitive characteristics. Cognitive characteristics include visual cognition, auditory cognition, memory, etc. The analysis unit uses AI to determine whether the user prefers visual information, auditory information, or tactile information. Step 3: The generator generates learning content based on the cognitive characteristics identified by the analyzer. The learning content may include text, video, interactive quizzes, etc. The generator generates learning content that makes extensive use of diagrams and graphs for users who prefer visual information, learning content that makes extensive use of audio and music for users who prefer auditory information, and learning content that includes many interactive elements for users who prefer tactile information.

[0056] (Example 2) A learning content generation system according to an embodiment of the present invention identifies a user's cognitive characteristics and generates learning content tailored to those characteristics. The learning content generation system collects a user's learning history, answer patterns, and reaction speed, and uses AI to analyze the user's cognitive characteristics and generate optimal learning content. For example, the learning content generation system collects detailed data, such as a user's learning history, answer patterns, and reaction speed. The learning content generation system then uses AI to analyze the collected data and identify the user's cognitive characteristics. For example, the system determines whether the user prefers visual information, auditory information, or tactile information. Based on the identified cognitive characteristics, the learning content generation system then generates optimal learning content. For example, for a user who prefers visual information, the system generates learning content that makes extensive use of diagrams and graphs, while for a user who prefers auditory information, the system generates learning content that makes extensive use of audio and music. Furthermore, for a user who prefers tactile information, the system generates learning content that includes many interactive elements. This allows the learning content generation system to provide an optimal learning experience for each individual user. This allows the learning content generation system to provide optimal learning content based on the user's cognitive characteristics. For example, users who prefer visual information will find it easier to understand learning content that makes extensive use of diagrams and graphs, while users who prefer auditory information can concentrate better through audio and music, and users who prefer tactile information can learn more hands-on through interactive elements.

[0057] The learning content generation system according to the embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects a user's learning history, answer patterns, and reaction speed. The user's learning history includes, but is not limited to, topics studied, study time, and study methods. The collection unit collects data such as what questions the user has answered in the past, how they answered them, and how long it took them to answer them. The collection unit can also collect the user's answer patterns. For example, the collection unit can collect data such as the correct answer rate, answer trends, and answer time. The collection unit can also collect the user's reaction speed. For example, the reaction time for a specific task can be measured in milliseconds. The analysis unit analyzes the data collected by the collection unit to determine the user's cognitive characteristics. Examples of cognitive characteristics include, but are not limited to, visual cognition, auditory cognition, and memory. The analysis unit can determine, for example, whether the user prefers visual information, auditory information, or tactile information. The analysis unit uses AI to analyze the collected data and identify the user's cognitive characteristics. For example, the AI ​​analyzes the user's learning history, answer patterns, and reaction speed to determine the user's cognitive characteristics. The generation unit generates learning content based on the cognitive characteristics determined by the analysis unit. Learning content includes, but is not limited to, text, video, and interactive quizzes. For example, the generation unit generates learning content that makes extensive use of diagrams and graphs for users who prefer visual information. The generation unit can also generate learning content that makes extensive use of audio and music for users who prefer auditory information. The generation unit can also generate learning content that includes many interactive elements for users who prefer tactile information. For example, the generation unit generates interactive quizzes and simulations to enable users to learn practically. This allows the learning content generation system according to the embodiment to provide optimal learning content based on the user's cognitive characteristics. Some or all of the above-described processing by the generation unit may be performed using AI, for example, or without AI.For example, the generation unit can generate learning content using an AI model that takes the cognitive characteristics determined by the analysis unit as input and outputs optimal learning content.

[0058] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection and collect data when the user is relaxed. Furthermore, if the user is concentrating, the collection unit can increase the timing of data collection and collect more detailed data. Furthermore, if the user is tired, the collection unit can temporarily stop data collection and resume it after the user has rested. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0059] The collection unit can analyze the user's past learning history and select a data collection method. For example, the collection unit can prioritize collection of learning methods that were effective for the user in the past. The collection unit can also select an effective method by avoiding learning methods that the user was weak at in the past. The collection unit can also select a data collection method that is effective for a specific time period from the user's learning history. This enables effective data collection by selecting the optimal data collection method based on the user's past learning history. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the user's learning history data into a generation AI and have the generation AI select the optimal data collection method.

[0060] When collecting data, the collection unit can filter the data based on the user's current learning situation and areas of interest. For example, the collection unit collects only data related to the area the user is currently learning. The collection unit can also prioritize the collection of highly relevant data based on the user's areas of interest. The collection unit can also filter and collect only necessary data according to the user's learning progress. This makes it possible to collect highly relevant data by filtering data based on the user's current learning situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's learning situation data to a generation AI and have the generation AI perform filtering.

[0061] When collecting data, the collection unit can select a collection means according to the user's input method. For example, if the user prefers voice input, the collection unit can prioritize collecting voice data. Also, if the user prefers text input, the collection unit can prioritize collecting text data. Also, if the user prefers image input, the collection unit can prioritize collecting image data. This enables efficient data collection by selecting the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and have the generation AI select the optimal collection means.

[0062] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting data that helps the user relax. Furthermore, if the user is concentrating, the collection unit can prioritize collecting data that is useful for learning. Furthermore, if the user is tired, the collection unit can prioritize collecting data related to rest. This enables more effective data collection by prioritizing data based on 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, 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 collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0063] When collecting data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, if the user is traveling, the collection unit can prioritize collecting data related to the user's destination. Furthermore, if the user stays in a specific location for a long time, the collection unit can prioritize collecting data related to that location. This enables more appropriate data collection by collecting highly relevant data based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location data to a generation AI and cause the generation AI to select highly relevant data.

[0064] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit collects related data based on information shared by the user on social media. The collection unit can also analyze the user's social media activity patterns and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. This enables more effective data collection by collecting related data based on the user's social media activities. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related data.

[0065] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit can adjust the collection method based on, for example, feedback provided by the user in the past. The collection unit can also select an effective collection method based on the user's past feedback. The collection unit can also improve the quality of collected data by reflecting the user's feedback. This enables more effective data collection by customizing the collection method based on the user's past feedback. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's feedback data into a generation AI and cause the generation AI to customize the collection method.

[0066] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that focus on the main points. If the user is excited, the analysis unit can also provide visually stimulating analysis results. By adjusting the way the analysis is presented based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved 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-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0067] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a concise analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0068] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply an image analysis algorithm to visual data. The analysis unit can also apply a voice analysis algorithm to auditory data. The analysis unit can also apply a natural language processing algorithm to text data. This allows for more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0069] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also select an effective analysis method from the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by reflecting the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0070] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide a visually stimulating analysis result. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved 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-described processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0071] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of older data. The analysis unit can also determine the analysis priority according to the time when the data was collected. This enables efficient analysis by determining the analysis priority based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0072] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0073] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit can provide analysis results that use a lot of technical terminology to a user with high levels of expertise. The analysis unit can also provide concise and easy-to-understand analysis results to a user with low levels of expertise. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to provide more appropriate analysis results by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise into the generation AI and have the generation AI use technical terminology.

[0074] The generation unit can estimate the user's emotions and adjust the presentation of the learning content based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate visually calming content. If the user is in a hurry, the generation unit can also generate concise content that focuses on the main points. If the user is excited, the generation unit can also generate visually stimulating content. This allows for more appropriate learning content to be provided by adjusting the presentation of the learning content based on the user's emotions. The emotion estimation is achieved 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-described 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 facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0075] When generating learning content, the generation unit can adjust the level of detail of the content based on the user's cognitive characteristics. For example, the generation unit can generate content that makes extensive use of detailed diagrams and graphs for a user who prefers visual information. The generation unit can also generate content that makes extensive use of detailed audio explanations for a user who prefers auditory information. The generation unit can also generate content that includes many interactive elements for a user who prefers tactile information. This allows for more effective learning content to be provided by adjusting the level of detail of the content based on the user's cognitive characteristics. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's cognitive characteristic data into the generation AI and cause the generation AI to adjust the level of detail of the content.

[0076] When generating study content, the generation unit can improve the accuracy of the content by referring to the user's learning history. The generation unit generates effective content based on, for example, the user's past learning history. The generation unit can also generate content that reinforces weak areas based on the user's past learning history. The generation unit can also improve the accuracy of the content by reflecting the user's past learning history. In this way, the accuracy of the content is improved by referring to the user's learning history. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's learning history data into the generation AI and have the generation AI improve the accuracy of the content.

[0077] When generating learning content, the generation unit can improve the content by reflecting user feedback. For example, the generation unit adjusts the content based on user feedback. The generation unit can also generate effective content from user feedback. The generation unit can also improve the quality of the content by reflecting user feedback. In this way, the quality of the content is improved by reflecting user feedback. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user feedback data into the generation AI and have the generation AI improve the content.

[0078] The generation unit can estimate the user's emotions and adjust the length of the learning content based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate short, to-the-point content. If the user is relaxed, the generation unit can generate longer content with detailed explanations. If the user is excited, the generation unit can generate content with visually stimulating effects. This allows for adjusting the length of the learning content based on the user's emotions, thereby providing more appropriate learning content. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a 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 can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0079] When generating study content, the generation unit can determine the priority of content based on the user's study progress. For example, the generation unit can prioritize generating content related to areas in which the user is making progress. The generation unit can also prioritize generating content related to areas in which the user is lagging behind. The generation unit can also prioritize generating necessary content according to the user's study progress. This allows for more effective study content to be provided by prioritizing content based on the user's study progress. Some or all of the above-described processing by the generation unit can be performed using, or without, AI. For example, the generation unit can input the user's study progress data into the generation AI and have the generation AI determine the priority of content.

[0080] When generating study content, the generation unit can adjust the order of content based on the user's areas of interest. For example, the generation unit can prioritize generating content related to the user's areas of interest. The generation unit can also prioritize generating highly relevant content based on the user's areas of interest. The generation unit can also prioritize generating necessary content according to the user's areas of interest. This allows for more effective study content to be provided by adjusting the order of content based on the user's areas of interest. Some or all of the above-described processing by the generation unit may be performed using, or without, AI. For example, the generation unit can input user's area of ​​interest data into the generation AI and have the generation AI adjust the order of the content.

[0081] When generating learning content, the generation unit can adjust the use of technical terms in the content according to the user's level of expertise. For example, the generation unit can generate content that uses a lot of technical terms for a user with high levels of expertise. The generation unit can also generate concise, easy-to-understand content for a user with low levels of expertise. The generation unit can also adjust the way the content is expressed according to the user's level of expertise. This allows for more appropriate learning content to be provided by adjusting the use of technical terms in the content according to the user's level of expertise. Some or all of the above-described processing by the generation unit can be performed using, or without, AI. For example, the generation unit can input the user's level of expertise data into the generation AI and have the generation AI execute the use of technical terms. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and generation unit, described above, may be realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit may be realized by the control unit 46A of the smart device 14 and collect the user's learning history, answer patterns, and reaction speed. The analysis unit may be realized, for example, by the specific processing unit 290 of the data processing device 12 and analyze the collected data to determine the user's cognitive characteristics. The generation unit may be realized, for example, by the specific processing unit 290 of the data processing device 12 and generate optimal study content based on the determined cognitive characteristics. The generation unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and generation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 and collects the user's learning history, answer patterns, and reaction speed. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to determine the user's cognitive characteristics. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates optimal learning content based on the determined cognitive characteristics. The generation unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, and generation unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset-type terminal 314 and collects the user's learning history, answer patterns, and reaction speed. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to determine the user's cognitive characteristics. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates optimal study content based on the determined cognitive characteristics. The generation unit may be realized, for example, by the control unit 46A of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, and generation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 and collects the user's learning history, answer patterns, and reaction speed. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to determine the user's cognitive characteristics. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates optimal learning content based on the determined cognitive characteristics. The generation unit may be realized, for example, by the control unit 46A of the robot 414.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The analysis unit can estimate the user's learning style and adjust the analysis method based on the estimated learning style. For example, if the user prefers repeated learning, the analysis unit can analyze the same content multiple times from different angles. If the user prefers to process a lot of information at once, the analysis unit can provide a detailed analysis all at once. Furthermore, if the user prefers to learn in stages, the analysis unit can provide analysis results in stages. In this way, by adjusting the analysis method based on the user's learning style, more effective analysis results can be provided.

[0084] The collection unit can estimate the user's emotions and determine the type of data to collect based on the estimated user's emotions. For example, if the user is feeling stressed, data that helps the user relax can be collected. Also, if the user is concentrating, data that is useful for learning can be collected. Furthermore, if the user is excited, data that piques the user's interest can be collected. Thus, by determining the type of data to collect based on the user's emotions, more appropriate data collection becomes possible.

[0085] The generation unit can analyze the user's learning history and adjust the difficulty of the content based on the user's past learning results. For example, it can generate content with a high level of difficulty in areas in which the user has achieved good results in the past. It can also generate content that starts with basic content in areas in which the user is weak. It can also generate content that gradually increases in difficulty according to the user's learning progress. This allows the user to provide a more effective learning experience by adjusting the difficulty of the content based on the user's learning history.

[0086] The analysis unit can estimate the user's emotions and adjust the way in which the analysis results are presented based on the estimated user emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results that focus on the main points can be provided. Furthermore, if the user is excited, visually stimulating analysis results can be provided. In this way, by adjusting the way in which the analysis results are presented based on the user's emotions, more appropriate analysis results can be provided.

[0087] The collection unit can detect the user's learning environment and adjust the data collection method according to the environment. For example, if the user is in a quiet environment, priority can be given to collecting voice data. Also, if the user is on the move, priority can be given to collecting text data. Furthermore, if the user is in a public place, a data collection method that takes privacy into consideration can be selected. This allows for more appropriate data collection by adjusting the data collection method according to the user's learning environment.

[0088] The generation unit can estimate the user's emotions and adjust the interactivity of the learning content based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate content including gentle interactive elements. If the user is concentrating, the generation unit can generate content including interactive elements that encourage active participation. Furthermore, if the user is excited, the generation unit can generate content including visually stimulating interactive elements. In this way, by adjusting the interactivity of the learning content based on the user's emotions, a more appropriate learning experience can be provided.

[0089] The analysis unit can set the user's learning goals and adjust the direction of analysis based on the goals. For example, if the user's goal is to pass a specific exam, the analysis can focus on content related to the exam. Also, if the user's goal is to acquire a specific skill, the analysis can also analyze content related to that skill. Furthermore, if the user's goal is self-improvement, the analysis can also analyze a wide range of content. In this way, by adjusting the direction of analysis based on the user's learning goals, more effective analysis results can be provided.

[0090] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of data collection can be reduced. Also, if the user is relaxed, the frequency of data collection can be increased. Furthermore, if the user is concentrating, detailed data can be collected more frequently. In this way, adjusting the frequency of data collection based on the user's emotions enables more appropriate data collection.

[0091] The generation unit can estimate the user's learning style and adjust the format of the content based on the estimated learning style. For example, if the user prefers visual learning, the generation unit can generate content that makes extensive use of diagrams and graphs. If the user prefers auditory learning, the generation unit can generate content that makes extensive use of audio and music. Furthermore, if the user prefers tactile learning, the generation unit can generate content that includes many interactive elements. In this way, by adjusting the format of the content based on the user's learning style, a more effective learning experience can be provided.

[0092] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated user emotions. For example, if the user is relaxed, a detailed analysis can be performed. If the user is in a hurry, a brief analysis can be performed. Furthermore, if the user is excited, a visually stimulating analysis can be performed. In this way, by adjusting the timing of the analysis based on the user's emotions, more appropriate analysis results can be provided.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The collection unit collects the user's learning history, answer patterns, and reaction speed. The user's learning history includes the topics studied, study time, and study method. The collection unit collects data such as what questions the user has answered in the past, how they answered them, and how long it took them to answer them. The collection unit also collects answer patterns such as the accuracy rate, answer trends, and response time, as well as reaction speed, which measures the reaction time to a specific task in milliseconds. Step 2: The analysis unit analyzes the data collected by the collection unit and determines the user's cognitive characteristics. Cognitive characteristics include visual cognition, auditory cognition, memory, etc. The analysis unit uses AI to determine whether the user prefers visual information, auditory information, or tactile information. Step 3: The generator generates learning content based on the cognitive characteristics identified by the analyzer. The learning content may include text, video, interactive quizzes, etc. The generator generates learning content that makes extensive use of diagrams and graphs for users who prefer visual information, learning content that makes extensive use of audio and music for users who prefer auditory information, and learning content that includes many interactive elements for users who prefer tactile information.

[0095] 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.

[0096] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] 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.

[0098] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The 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.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 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.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0109] 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.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] The data processing system 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.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0125] 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.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] The data processing system 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] [Explanation of symbols]

[0167] 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 collection unit that collects the user's learning history, answer patterns, and reaction speed; an analysis unit that analyzes the data collected by the collection unit and determines the cognitive characteristics of the user; a generation unit that generates learning content based on the cognitive characteristics determined by the analysis unit. A system characterized by:

2. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

3. The collecting unit Analyze users' past learning history and select data collection methods 2. The system of claim 1.

4. The collecting unit When collecting data, filter it based on the user's current learning status and areas of interest.

2. The system of claim 1.

5. The collecting unit When collecting data, select the collection method according to the user's input method 2. The system of claim 1.

6. The collecting unit Estimate user emotions and prioritize data collection based on the estimated user emotions 2. The system of claim 1.

7. The collecting unit When collecting data, prioritize the collection of relevant data based on the user's geographic location 2. The system of claim 1.

8. The collecting unit When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1.

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