system

Abstract

The system according to the embodiment aims to reduce the personnel costs of cram schools. [Solution] A system according to an embodiment includes an input unit, an analysis unit, a generation unit, and a provision unit. The input unit inputs lesson content. The analysis unit analyzes the lesson content input by the input unit. The generation unit generates a lesson video based on the content analyzed by the analysis unit. The provision unit provides the lesson video generated by the generation unit.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, the personnel costs of cram schools are high, and there is room for cost reduction.

[0005] The system according to the embodiment aims to reduce the personnel costs of cram schools. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a generation unit, and a provision unit. The input unit inputs lesson content. The analysis unit analyzes the lesson content input by the input unit. The generation unit generates a lesson video based on the content analyzed by the analysis unit. The provision unit provides the lesson video generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the personnel costs of cram schools. [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 system according to an embodiment of the present invention incorporates a video generation AI to reduce the labor costs of cram schools nationwide. This system inputs and analyzes lesson content, generates, and provides lesson videos. For example, lesson content from each cram school is input into the video generation AI, which analyzes the content and generates optimal lesson videos. The generated lesson videos are provided to students at each cram school. This reduces the labor costs of cram school instructors, aiming to achieve annual cost savings of 150 billion yen. For example, instead of having instructors teach one hour of lessons, instructors can provide students with lesson videos generated by the video generation AI, reducing the instructor's working hours. Furthermore, the generated lesson videos can be shared among multiple cram schools. This eliminates the need for each cram school to individually create lesson videos, thereby reducing costs. For example, using common lesson videos across cram schools nationwide can standardize the quality of lessons and promote efficient learning.

[0029] The lesson video generation system according to the embodiment includes an input unit, an analysis unit, a generation unit, and a provision unit. The input unit inputs lesson content. The lesson content includes, for example, but is not limited to, subject content such as mathematics, science, and history. The input unit can input the lesson content using methods such as text input, audio input, and image input. The analysis unit analyzes the lesson content input by the input unit. The analysis can be performed using methods such as, for example, text analysis, audio analysis, and image analysis, but is not limited to these examples. The generation unit generates lesson videos based on the content analyzed by the analysis unit. The lesson videos can include, for example, video length, resolution, and level of detail, but are not limited to these examples. The generation unit generates lesson videos that include, for example, specific problem explanations and solution explanations. The provision unit provides the lesson videos generated by the generation unit. The lesson videos can be provided using methods such as, for example, online distribution, downloading, and streaming, but are not limited to these examples. As a result, the lesson video generation system according to the embodiment can consistently perform processes from inputting lesson content to analyzing, generating, and providing it.

[0030] The input unit can specifically input the lesson content of each subject. Specific input methods include, but are not limited to, text input, audio input, and image input. For example, the input unit inputs the math lesson content as text. The input unit can also input the English lesson content as audio. The input unit can also input the science lesson content as images. This allows for detailed input of the lesson content of each subject, making it possible to generate highly accurate lesson videos.

[0031] The analysis unit can analyze the input lesson content and generate appropriate lesson videos. Criteria for appropriate lesson videos include, but are not limited to, accuracy of the content and adjustment according to the viewer's level of understanding. For example, the analysis unit can analyze the input mathematics lesson content and generate lesson videos that include specific problem explanations and solution explanations. The analysis unit can also analyze the input English lesson content and generate lesson videos that include grammar and pronunciation points. The analysis unit can also analyze the input science lesson content and generate lesson videos that include experimental procedures and results. In this way, optimal lesson videos can be generated by analyzing the input lesson content.

[0032] The generation unit can generate lesson videos that include problem explanations and solution explanations. Specific content of the problem explanations and solutions includes, but is not limited to, specific problem examples and solution steps. For example, the generation unit explains specific problem examples in a mathematics lesson video. The generation unit can also provide grammar explanations in an English lesson video. The generation unit can also explain experimental procedures in a science lesson video. This improves learning effectiveness by generating lesson videos that include specific problem explanations and solution explanations.

[0033] The providing unit can provide the generated lesson videos to students of each cram school. The specific range of students of each cram school includes, for example, a specific grade, a specific region, etc., but is not limited to these examples. The providing unit can provide the lesson videos, for example, through online distribution. The providing unit can also provide the lesson videos in a downloadable format. The providing unit can also provide the lesson videos in a streaming format. By providing the generated lesson videos to students of each cram school, learning efficiency is improved.

[0034] The providing unit can share the generated lesson video with several cram schools. Specific scope of several cram schools includes, for example, a specific region, a specific affiliated cram school, etc., but is not limited to such examples. The providing unit, for example, shares the generated lesson video with several cram schools. The providing unit can also share the generated lesson video with cram schools across the country. The providing unit can also share the generated lesson video with a specific affiliated cram school. This allows the generated lesson video to be shared with several cram schools, thereby reducing costs.

[0035] When inputting lesson content for each subject, the input unit can select the optimal input method by referring to the history of past lesson content. Specific examples of the history of past lesson content include, but are not limited to, past lesson records and learning history data. The input unit selects the most effective input method based on the history of past lesson content, for example. The input unit can also analyze the history of past lesson content and customize the input method. The input unit can also optimize the input method by referring to the history of past lesson content. This allows the optimal input method to be selected by referring to the history of past lesson content.

[0036] When inputting lesson content, the input unit can filter the lesson content based on the user's current learning progress and level of understanding. Specific methods for evaluating learning progress and level of understanding include, but are not limited to, test results and learning logs. The input unit selects appropriate lesson content based on, for example, the user's learning progress. The input unit can also filter the lesson content based on the user's level of understanding. The input unit can also provide optimal lesson content taking into account the user's learning progress and level of understanding. This makes it possible to provide appropriate lesson content by filtering the lesson content based on the user's learning progress and level of understanding.

[0037] When inputting lesson content, the input unit can select the optimal input means depending on the user's input method. Specific types of input methods include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the input unit can prioritize voice input. Also, if the user prefers text input, the input unit can prioritize text input. Also, if the user prefers image input, the input unit can prioritize image input. This allows for efficient input of lesson content by selecting the optimal input means depending on the user's input method.

[0038] When inputting lesson content, the input unit can prioritize inputting highly relevant content by taking into account the user's geographical location information. Specific methods for acquiring geographical location information include, but are not limited to, GPS data, address information, etc. The input unit, for example, prioritizes lesson content related to a region based on the user's geographical location information. The input unit can also prioritize lesson content related to the culture or history of a region by taking into account the user's geographical location information. The input unit can also prioritize lesson content tailored to the characteristics of a region based on the user's geographical location information. This allows highly relevant lesson content to be prioritized by taking into account the user's geographical location information.

[0039] When inputting lesson content, the input unit can analyze the user's social media activity and input related content. Specific methods for analyzing social media activity include, but are not limited to, for example, the content of posts and the reactions of followers. The input unit, for example, analyzes the user's social media activity and reflects the content of interest in the lesson. The input unit can also input related lesson content based on the content of the user's social media posts. The input unit can also input related lesson content by referring to the activities of the user's friends on social media. In this way, related lesson content can be input by analyzing the user's social media activity.

[0040] The input unit can customize the input method by reflecting the user's past feedback when inputting lesson content. Specific methods for obtaining past feedback include, but are not limited to, survey results, comments, etc. The input unit can improve the input method, for example, based on the user's past feedback. The input unit can also customize the input method by reflecting the user's past feedback. The input unit can also provide an optimal input method by referring to the user's past feedback. In this way, the optimal input method can be provided by reflecting the user's past feedback.

[0041] When analyzing lesson content, the analysis unit can adjust the level of detail of the analysis based on the importance of the content. Specific methods for evaluating the importance of content include, but are not limited to, learning goals, exam scope, etc. For example, the analysis unit performs a detailed analysis of content with high importance. The analysis unit can also perform a simplified analysis of content with low importance. The analysis unit can also adjust the level of detail of the analysis depending on the importance of the content. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the content.

[0042] The analysis unit can apply different analysis algorithms to each subject when analyzing the lesson content. Specific types of analysis algorithms include, but are not limited to, natural language processing algorithms and image analysis algorithms. For example, the analysis unit can apply a mathematical expression analysis algorithm to mathematics lesson content. The analysis unit can also apply a grammar analysis algorithm to English lesson content. The analysis unit can also apply an experimental data analysis algorithm to science lesson content. This allows for efficient analysis by applying different analysis algorithms to each subject.

[0043] When analyzing lesson content, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Specific methods for referring to past analysis results include, but are not limited to, past analysis data, learning history, etc. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and provide an optimal analysis method. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0044] When analyzing the content of a lesson, the analysis unit can determine the priority of analysis based on the submission time of the content. Specific methods for obtaining the submission time include, but are not limited to, for example, a submission deadline or a submission date and time. For example, the analysis unit prioritizes the analysis of content with an upcoming submission deadline. The analysis unit can also postpone content with a distant submission deadline. The analysis unit can also determine the priority of analysis based on the submission time of the content. This enables efficient analysis by determining the priority of analysis based on the submission time of the content.

[0045] When analyzing lesson content, the analysis unit can adjust the order of analysis based on the relevance of the content. Specific methods for evaluating the relevance of the content include, but are not limited to, for example, topic similarity and matching of learning goals. For example, the analysis unit prioritizes the analysis of highly relevant content. The analysis unit can also postpone the analysis of less relevant content. The analysis unit can also adjust the order of analysis based on the relevance of the content. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the content.

[0046] When analyzing lesson content, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Specific methods for evaluating the level of expertise include, but are not limited to, test results, learning history, etc. For example, the analysis unit uses a lot of technical terms when the user's level of expertise is high. The analysis unit can also avoid technical terms when the user's level of expertise is low. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. This allows for efficient analysis by adjusting the use of technical terms according to the user's level of expertise.

[0047] When generating a lesson video, the generation unit can adjust the level of detail of the generated lesson video based on the importance of the content. Specific methods for adjusting the level of detail of the generated lesson video include, but are not limited to, for example, the resolution of the video and the level of detail of the content. For example, the generation unit generates a detailed lesson video for content with high importance. The generation unit can also generate a simplified lesson video for content with low importance. The generation unit can also adjust the level of detail of the generated lesson video based on the importance of the content. This allows for efficient generation of lesson videos by adjusting the level of detail of the generated lesson video based on the importance of the content.

[0048] The generation unit can apply different generation algorithms to each subject when generating lesson videos. Specific types of generation algorithms include, but are not limited to, video generation algorithms and voice synthesis algorithms. For example, the generation unit can apply a mathematical expression analysis algorithm to a math lesson video. The generation unit can also apply a grammar analysis algorithm to an English lesson video. The generation unit can also apply an experimental data analysis algorithm to a science lesson video. This allows for efficient generation of lesson videos by applying different generation algorithms to each subject.

[0049] When generating a lesson video, the generation unit can improve the accuracy of generation by referring to the user's past generation results. Specific methods for referring to past generation results include, but are not limited to, past video generation data, viewing history, etc. For example, the generation unit adjusts the generation algorithm based on the user's past generation results. The generation unit can also improve the accuracy of generation by referring to the user's past generation results. The generation unit can also analyze the user's past generation results and provide an optimal generation method. In this way, the accuracy of generation can be improved by referring to the user's past generation results.

[0050] When generating lesson videos, the generation unit can determine the generation priority based on the submission time of the content. Specific methods for determining the generation priority include, but are not limited to, for example, the submission deadline and importance. For example, the generation unit prioritizes the generation of content with an upcoming submission deadline. The generation unit can also postpone content with a distant submission deadline. The generation unit can also determine the generation priority based on the submission time of the content. This enables efficient generation of lesson videos by determining the generation priority based on the submission time of the content.

[0051] When generating lesson videos, the generation unit can adjust the generation order based on the relevance of the content. Specific methods for adjusting the generation order include, but are not limited to, for example, content relevance, matching of learning goals, etc. The generation unit, for example, generates highly relevant content with priority. The generation unit can also postpone less relevant content. The generation unit can also adjust the generation order based on the relevance of the content. This allows for efficient generation of lesson videos by adjusting the generation order based on the relevance of the content.

[0052] When generating a lesson video, the generation unit can adjust the use of technical terms in the generation according to the user's level of expertise. Specific methods for adjusting the use of technical terms include, but are not limited to, definitions and frequency of use of technical terms. For example, if the user's level of expertise is high, the generation unit uses a lot of technical terms. Also, if the user's level of expertise is low, the generation unit can avoid technical terms. Also, the generation unit can adjust the use of technical terms in the generation according to the user's level of expertise. This allows for efficient generation of lesson videos by adjusting the use of technical terms according to the user's level of expertise.

[0053] When providing the lesson video, the providing unit can select the optimal providing method by referring to the user's past viewing history. Specific methods for acquiring the viewing history include, but are not limited to, for example, viewing time and number of views. The providing unit selects the optimal providing method based on, for example, the user's past viewing history. The providing unit can also analyze the user's past viewing history and customize the providing method. The providing unit can also optimize the providing method by referring to the user's past viewing history. In this way, the optimal providing method can be selected by referring to the user's past viewing history.

[0054] When providing the lesson video, the providing unit can customize the content to be provided based on the user's current learning progress and level of understanding. Specific methods for evaluating the learning progress and level of understanding include, but are not limited to, test results and learning logs. The providing unit provides appropriate lesson videos, for example, based on the user's learning progress. The providing unit can also customize the lesson videos based on the user's level of understanding. The providing unit can also provide optimal lesson videos taking into account the user's learning progress and level of understanding. This enables efficient provision of lesson videos by customizing the content to be provided based on the user's learning progress and level of understanding.

[0055] The providing unit can improve the providing method by reflecting user feedback when providing the lesson video. Specific methods of obtaining feedback include, but are not limited to, survey results, comments, etc. For example, the providing unit improves the providing method based on the user feedback. The providing unit can also customize the providing method by reflecting the user feedback. The providing unit can also provide the optimal providing method by referring to the user feedback. In this way, the optimal providing method can be provided by reflecting the user feedback.

[0056] When providing lesson videos, the providing unit can select the optimal providing method by taking into account the user's geographical location information. Specific methods for acquiring geographical location information include, but are not limited to, GPS data, address information, etc. For example, the providing unit prioritizes lesson videos related to a region based on the user's geographical location information. The providing unit can also prioritize lesson videos related to the culture or history of a region by taking into account the user's geographical location information. The providing unit can also prioritize lesson videos tailored to the characteristics of a region based on the user's geographical location information. This allows the optimal providing method to be selected by taking into account the user's geographical location information.

[0057] When providing lesson videos, the providing unit can analyze the user's social media activity to customize the content to be provided. Specific methods for analyzing social media activity include, but are not limited to, for example, the content of posts and the reactions of followers. For example, the providing unit analyzes the user's social media activity and reflects the user's interests in the lessons. The providing unit can also provide related lesson videos based on the content of the user's social media posts. The providing unit can also provide related lesson videos by referring to the activities of the user's friends on social media. In this way, related lesson videos can be provided by analyzing the user's social media activity.

[0058] When providing the lesson video, the providing unit can customize the providing method by reflecting the user's past feedback. Specific methods for acquiring past feedback include, but are not limited to, survey results, comments, etc. For example, the providing unit improves the providing method based on the user's past feedback. The providing unit can also customize the providing method by reflecting the user's past feedback. The providing unit can also provide the optimal providing method by referring to the user's past feedback. In this way, the optimal providing method can be provided by reflecting the user's past feedback.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When analyzing lesson content, the analysis unit can improve the accuracy of the analysis by referring to the user's learning history. For example, it can identify areas in which the user is weak based on past learning history and perform analysis that focuses on those areas. It can also adjust the level of detail of the analysis taking into account the user's areas of strength. It can also analyze past learning history and provide the optimal analysis method. In this way, by referring to the user's learning history, it is possible to improve the accuracy of the analysis.

[0061] When generating lesson videos, the generation unit can customize the content of the generated lesson videos based on the user's learning goals. For example, if the user is studying for a specific exam, the generation unit can generate lesson videos that are specialized for the scope of that exam. Also, if the user wants to acquire a specific skill, the generation unit can generate lesson videos that emphasize content related to that skill. The generation unit can also adjust the difficulty level of the lesson videos to be generated according to the user's learning goals. This allows for efficient learning by customizing the content of lesson videos based on the user's learning goals.

[0062] When providing lesson videos, the providing unit can select the optimal providing method taking into account the user's device environment. For example, if the user is using a smartphone, it can provide lesson videos optimized for mobile devices. Also, if the user is using a tablet, it can provide lesson videos suitable for large screens. Also, if the user is using a PC, it can provide high-resolution lesson videos. This allows for efficient learning by selecting the optimal providing method according to the user's device environment.

[0063] When analyzing lesson content, the analysis unit can customize the analysis method by taking into account the user's learning style. For example, for visual learners, the analysis can make extensive use of graphs and diagrams. For auditory learners, the analysis can also include audio commentary. It can also provide interactive analysis methods for tactile learners. This allows the analysis method to be customized according to the user's learning style, enabling efficient analysis.

[0064] When providing the lesson videos, the providing unit can select the optimal providing method taking into account the user's internet connection status. For example, if the user's internet connection is unstable, the lesson videos can be provided in download format. Alternatively, if the user's internet connection is fast, the lesson videos can be provided in streaming format. Alternatively, if the user's internet connection is limited, the lesson videos can be provided in low resolution. This allows for efficient learning by selecting the optimal providing method depending on the user's internet connection status.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The input unit inputs lesson content. The lesson content includes, but is not limited to, subject content such as mathematics, science, and history. The input unit can input the lesson content by, for example, text input, voice input, image input, or the like. Step 2: The analysis unit analyzes the lesson content input by the input unit. The analysis is performed by, for example, text analysis, audio analysis, image analysis, or the like, but is not limited to these examples. Step 3: The generation unit generates a lesson video based on the content analyzed by the analysis unit. The lesson video includes, but is not limited to, the length, resolution, and level of detail of the content. The generation unit generates a lesson video that includes, for example, specific problem explanations and explanations of solutions. Step 4: The providing unit provides the lesson video generated by the generating unit, for example, by online distribution, downloading, streaming, or the like, but is not limited to these examples.

[0067] (Example 2) A system according to an embodiment of the present invention incorporates a video generation AI to reduce the labor costs of cram schools nationwide. This system inputs and analyzes lesson content, generates, and provides lesson videos. For example, lesson content from each cram school is input into the video generation AI, which analyzes the content and generates optimal lesson videos. The generated lesson videos are provided to students at each cram school. This reduces the labor costs of cram school instructors, aiming to achieve annual cost savings of 150 billion yen. For example, instead of having instructors teach one hour of lessons, instructors can provide students with lesson videos generated by the video generation AI, reducing the instructor's working hours. Furthermore, the generated lesson videos can be shared among multiple cram schools. This eliminates the need for each cram school to individually create lesson videos, thereby reducing costs. For example, using common lesson videos across cram schools nationwide can standardize the quality of lessons and promote efficient learning.

[0068] The lesson video generation system according to the embodiment includes an input unit, an analysis unit, a generation unit, and a provision unit. The input unit inputs lesson content. The lesson content includes, for example, but is not limited to, subject content such as mathematics, science, and history. The input unit can input the lesson content using methods such as text input, audio input, and image input. The analysis unit analyzes the lesson content input by the input unit. The analysis can be performed using methods such as, for example, text analysis, audio analysis, and image analysis, but is not limited to these examples. The generation unit generates lesson videos based on the content analyzed by the analysis unit. The lesson videos can include, for example, video length, resolution, and level of detail, but are not limited to these examples. The generation unit generates lesson videos that include, for example, specific problem explanations and solution explanations. The provision unit provides the lesson videos generated by the generation unit. The lesson videos can be provided using methods such as, for example, online distribution, downloading, and streaming, but are not limited to these examples. As a result, the lesson video generation system according to the embodiment can consistently perform processes from inputting lesson content to analyzing, generating, and providing it.

[0069] The input unit can specifically input the lesson content of each subject. Specific input methods include, but are not limited to, text input, audio input, and image input. For example, the input unit inputs the math lesson content as text. The input unit can also input the English lesson content as audio. The input unit can also input the science lesson content as images. This allows for detailed input of the lesson content of each subject, making it possible to generate highly accurate lesson videos.

[0070] The analysis unit can analyze the input lesson content and generate appropriate lesson videos. Criteria for appropriate lesson videos include, but are not limited to, accuracy of the content and adjustment according to the viewer's level of understanding. For example, the analysis unit can analyze the input mathematics lesson content and generate lesson videos that include specific problem explanations and solution explanations. The analysis unit can also analyze the input English lesson content and generate lesson videos that include grammar and pronunciation points. The analysis unit can also analyze the input science lesson content and generate lesson videos that include experimental procedures and results. In this way, optimal lesson videos can be generated by analyzing the input lesson content.

[0071] The generation unit can generate lesson videos that include problem explanations and solution explanations. Specific content of the problem explanations and solutions includes, but is not limited to, specific problem examples and solution steps. For example, the generation unit explains specific problem examples in a mathematics lesson video. The generation unit can also provide grammar explanations in an English lesson video. The generation unit can also explain experimental procedures in a science lesson video. This improves learning effectiveness by generating lesson videos that include specific problem explanations and solution explanations.

[0072] The providing unit can provide the generated lesson videos to students of each cram school. The specific range of students of each cram school includes, for example, a specific grade, a specific region, etc., but is not limited to these examples. The providing unit can provide the lesson videos, for example, through online distribution. The providing unit can also provide the lesson videos in a downloadable format. The providing unit can also provide the lesson videos in a streaming format. By providing the generated lesson videos to students of each cram school, learning efficiency is improved.

[0073] The providing unit can share the generated lesson video with several cram schools. Specific scope of several cram schools includes, for example, a specific region, a specific affiliated cram school, etc., but is not limited to such examples. The providing unit, for example, shares the generated lesson video with several cram schools. The providing unit can also share the generated lesson video with cram schools across the country. The providing unit can also share the generated lesson video with a specific affiliated cram school. This allows the generated lesson video to be shared with several cram schools, thereby reducing costs.

[0074] The input unit can estimate the user's emotions and adjust the timing of inputting lesson content based on the estimated user emotions. Specific methods for estimating the user's emotions include, but are not limited to, facial expression recognition, voice analysis, and questionnaire results. For example, if the user is feeling stressed, the input unit can delay the input timing to relax the user. Furthermore, if the user is concentrating, the input unit can also advance the input timing to efficiently input lesson content. Furthermore, if the user is tired, the input unit can adjust the input timing to allow the user to take a break. This allows for efficient input of lesson content by adjusting the input timing according to the user's emotions.

[0075] When inputting lesson content for each subject, the input unit can select the optimal input method by referring to the history of past lesson content. Specific examples of the history of past lesson content include, but are not limited to, past lesson records and learning history data. The input unit selects the most effective input method based on the history of past lesson content, for example. The input unit can also analyze the history of past lesson content and customize the input method. The input unit can also optimize the input method by referring to the history of past lesson content. This allows the optimal input method to be selected by referring to the history of past lesson content.

[0076] When inputting lesson content, the input unit can filter the lesson content based on the user's current learning progress and level of understanding. Specific methods for evaluating learning progress and level of understanding include, but are not limited to, test results and learning logs. The input unit selects appropriate lesson content based on, for example, the user's learning progress. The input unit can also filter the lesson content based on the user's level of understanding. The input unit can also provide optimal lesson content taking into account the user's learning progress and level of understanding. This makes it possible to provide appropriate lesson content by filtering the lesson content based on the user's learning progress and level of understanding.

[0077] When inputting lesson content, the input unit can select the optimal input means depending on the user's input method. Specific types of input methods include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the input unit can prioritize voice input. Also, if the user prefers text input, the input unit can prioritize text input. Also, if the user prefers image input, the input unit can prioritize image input. This allows for efficient input of lesson content by selecting the optimal input means depending on the user's input method.

[0078] The input unit can estimate the user's emotions and determine the priority of lesson content to be input based on the estimated user's emotions. Specific methods for determining the priority of lesson content include, but are not limited to, learning goals, importance, etc. For example, the input unit can prioritize more difficult lesson content when the user is excited. The input unit can also prioritize more basic lesson content when the user is relaxed. The input unit can also prioritize easier lesson content when the user is tired. This allows for efficient input of lesson content by determining the priority of lesson content according to the user's emotions.

[0079] When inputting lesson content, the input unit can prioritize inputting highly relevant content by taking into account the user's geographical location information. Specific methods for acquiring geographical location information include, but are not limited to, GPS data, address information, etc. The input unit, for example, prioritizes lesson content related to a region based on the user's geographical location information. The input unit can also prioritize lesson content related to the culture or history of a region by taking into account the user's geographical location information. The input unit can also prioritize lesson content tailored to the characteristics of a region based on the user's geographical location information. This allows highly relevant lesson content to be prioritized by taking into account the user's geographical location information.

[0080] When inputting lesson content, the input unit can analyze the user's social media activity and input related content. Specific methods for analyzing social media activity include, but are not limited to, for example, the content of posts and the reactions of followers. The input unit, for example, analyzes the user's social media activity and reflects the content of interest in the lesson. The input unit can also input related lesson content based on the content of the user's social media posts. The input unit can also input related lesson content by referring to the activities of the user's friends on social media. In this way, related lesson content can be input by analyzing the user's social media activity.

[0081] The input unit can customize the input method by reflecting the user's past feedback when inputting lesson content. Specific methods for obtaining past feedback include, but are not limited to, survey results, comments, etc. The input unit can improve the input method, for example, based on the user's past feedback. The input unit can also customize the input method by reflecting the user's past feedback. The input unit can also provide an optimal input method by referring to the user's past feedback. In this way, the optimal input method can be provided by reflecting the user's past feedback.

[0082] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. Specific methods for adjusting the way the analysis is presented include, but are not limited to, graph display, text display, and the like. For example, the analysis unit can provide detailed analysis results when the user is relaxed. Furthermore, the analysis unit can also provide simple analysis results when the user is nervous. Furthermore, the analysis unit can also provide visually stimulating analysis results when the user is excited. This allows for efficient analysis by adjusting the way the analysis is presented based on the user's emotions.

[0083] When analyzing lesson content, the analysis unit can adjust the level of detail of the analysis based on the importance of the content. Specific methods for evaluating the importance of content include, but are not limited to, learning goals, exam scope, etc. For example, the analysis unit performs a detailed analysis of content with high importance. The analysis unit can also perform a simplified analysis of content with low importance. The analysis unit can also adjust the level of detail of the analysis depending on the importance of the content. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the content.

[0084] The analysis unit can apply different analysis algorithms to each subject when analyzing the lesson content. Specific types of analysis algorithms include, but are not limited to, natural language processing algorithms and image analysis algorithms. For example, the analysis unit can apply a mathematical expression analysis algorithm to mathematics lesson content. The analysis unit can also apply a grammar analysis algorithm to English lesson content. The analysis unit can also apply an experimental data analysis algorithm to science lesson content. This allows for efficient analysis by applying different analysis algorithms to each subject.

[0085] When analyzing lesson content, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Specific methods for referring to past analysis results include, but are not limited to, past analysis data, learning history, etc. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and provide an optimal analysis method. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0086] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. Specific methods for adjusting the length of the analysis include, but are not limited to, the analysis time and the level of detail of the analysis content. For example, the analysis unit can provide a short analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. The analysis unit can also provide a visually stimulating analysis result when the user is excited. This allows for efficient analysis by adjusting the length of the analysis according to the user's emotions.

[0087] When analyzing the content of a lesson, the analysis unit can determine the priority of analysis based on the submission time of the content. Specific methods for obtaining the submission time include, but are not limited to, for example, a submission deadline or a submission date and time. For example, the analysis unit prioritizes the analysis of content with an upcoming submission deadline. The analysis unit can also postpone content with a distant submission deadline. The analysis unit can also determine the priority of analysis based on the submission time of the content. This enables efficient analysis by determining the priority of analysis based on the submission time of the content.

[0088] When analyzing lesson content, the analysis unit can adjust the order of analysis based on the relevance of the content. Specific methods for evaluating the relevance of the content include, but are not limited to, for example, topic similarity and matching of learning goals. For example, the analysis unit prioritizes the analysis of highly relevant content. The analysis unit can also postpone the analysis of less relevant content. The analysis unit can also adjust the order of analysis based on the relevance of the content. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the content.

[0089] When analyzing lesson content, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Specific methods for evaluating the level of expertise include, but are not limited to, test results, learning history, etc. For example, the analysis unit uses a lot of technical terms when the user's level of expertise is high. The analysis unit can also avoid technical terms when the user's level of expertise is low. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. This allows for efficient analysis by adjusting the use of technical terms according to the user's level of expertise.

[0090] The generation unit can estimate the user's emotions and adjust the generation method of the lesson video based on the estimated user's emotions. Specific methods for adjusting the generation method of the lesson video include, but are not limited to, video editing techniques, generation algorithms, etc. For example, if the user is relaxed, the generation unit can generate a lesson video that progresses at a leisurely pace. Furthermore, if the user is in a hurry, the generation unit can generate a lesson video that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a lesson video that adds visually stimulating effects. This allows for efficient generation of lesson videos by adjusting the generation method of the lesson video according to the user's emotions.

[0091] When generating a lesson video, the generation unit can adjust the level of detail of the generated lesson video based on the importance of the content. Specific methods for adjusting the level of detail of the generated lesson video include, but are not limited to, for example, the resolution of the video and the level of detail of the content. For example, the generation unit generates a detailed lesson video for content with high importance. The generation unit can also generate a simplified lesson video for content with low importance. The generation unit can also adjust the level of detail of the generated lesson video based on the importance of the content. This allows for efficient generation of lesson videos by adjusting the level of detail of the generated lesson video based on the importance of the content.

[0092] The generation unit can apply different generation algorithms to each subject when generating lesson videos. Specific types of generation algorithms include, but are not limited to, video generation algorithms and voice synthesis algorithms. For example, the generation unit can apply a mathematical expression analysis algorithm to a math lesson video. The generation unit can also apply a grammar analysis algorithm to an English lesson video. The generation unit can also apply an experimental data analysis algorithm to a science lesson video. This allows for efficient generation of lesson videos by applying different generation algorithms to each subject.

[0093] When generating a lesson video, the generation unit can improve the accuracy of generation by referring to the user's past generation results. Specific methods for referring to past generation results include, but are not limited to, past video generation data, viewing history, etc. For example, the generation unit adjusts the generation algorithm based on the user's past generation results. The generation unit can also improve the accuracy of generation by referring to the user's past generation results. The generation unit can also analyze the user's past generation results and provide an optimal generation method. In this way, the accuracy of generation can be improved by referring to the user's past generation results.

[0094] The generation unit can estimate the user's emotions and adjust the length of the lesson video based on the estimated user's emotions. Specific methods for adjusting the length of the lesson video include, but are not limited to, the video playback time and the level of detail of the content. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point lesson video. Furthermore, if the user is relaxed, the generation unit can generate a longer lesson video with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a lesson video with visually stimulating effects. This allows for efficient generation of lesson videos by adjusting the length of the lesson video according to the user's emotions.

[0095] When generating lesson videos, the generation unit can determine the generation priority based on the submission time of the content. Specific methods for determining the generation priority include, but are not limited to, for example, the submission deadline and importance. For example, the generation unit prioritizes the generation of content with an upcoming submission deadline. The generation unit can also postpone content with a distant submission deadline. The generation unit can also determine the generation priority based on the submission time of the content. This enables efficient generation of lesson videos by determining the generation priority based on the submission time of the content.

[0096] When generating lesson videos, the generation unit can adjust the generation order based on the relevance of the content. Specific methods for adjusting the generation order include, but are not limited to, for example, content relevance, matching of learning goals, etc. The generation unit, for example, generates highly relevant content with priority. The generation unit can also postpone less relevant content. The generation unit can also adjust the generation order based on the relevance of the content. This allows for efficient generation of lesson videos by adjusting the generation order based on the relevance of the content.

[0097] When generating a lesson video, the generation unit can adjust the use of technical terms in the generation according to the user's level of expertise. Specific methods for adjusting the use of technical terms include, but are not limited to, definitions and frequency of use of technical terms. For example, if the user's level of expertise is high, the generation unit uses a lot of technical terms. Also, if the user's level of expertise is low, the generation unit can avoid technical terms. Also, the generation unit can adjust the use of technical terms in the generation according to the user's level of expertise. This allows for efficient generation of lesson videos by adjusting the use of technical terms according to the user's level of expertise.

[0098] The providing unit can estimate the user's emotions and adjust the method of providing the lesson videos based on the estimated user's emotions. Specific methods for adjusting the method of providing the lesson videos include, but are not limited to, online distribution, downloading, streaming, etc. For example, if the user is relaxed, the providing unit can provide a lesson video that progresses at a leisurely pace. Furthermore, if the user is in a hurry, the providing unit can provide a lesson video that emphasizes the shortest route. Furthermore, if the user is excited, the providing unit can provide a lesson video with visually stimulating effects. This allows for efficient provision of lesson videos by adjusting the method of providing the lesson videos according to the user's emotions.

[0099] When providing the lesson video, the providing unit can select the optimal providing method by referring to the user's past viewing history. Specific methods for acquiring the viewing history include, but are not limited to, for example, viewing time and number of views. The providing unit selects the optimal providing method based on, for example, the user's past viewing history. The providing unit can also analyze the user's past viewing history and customize the providing method. The providing unit can also optimize the providing method by referring to the user's past viewing history. In this way, the optimal providing method can be selected by referring to the user's past viewing history.

[0100] When providing the lesson video, the providing unit can customize the content to be provided based on the user's current learning progress and level of understanding. Specific methods for evaluating the learning progress and level of understanding include, but are not limited to, test results and learning logs. The providing unit provides appropriate lesson videos, for example, based on the user's learning progress. The providing unit can also customize the lesson videos based on the user's level of understanding. The providing unit can also provide optimal lesson videos taking into account the user's learning progress and level of understanding. This enables efficient provision of lesson videos by customizing the content to be provided based on the user's learning progress and level of understanding.

[0101] The providing unit can improve the providing method by reflecting user feedback when providing the lesson video. Specific methods of obtaining feedback include, but are not limited to, survey results, comments, etc. For example, the providing unit improves the providing method based on the user feedback. The providing unit can also customize the providing method by reflecting the user feedback. The providing unit can also provide the optimal providing method by referring to the user feedback. In this way, the optimal providing method can be provided by reflecting the user feedback.

[0102] The providing unit can estimate the user's emotions and determine the order in which the lesson videos are provided based on the estimated user's emotions. Specific methods for determining the order of provision include, but are not limited to, learning goals, importance, etc. For example, if the user is excited, the providing unit can prioritize more difficult lesson videos. Furthermore, if the user is relaxed, the providing unit can prioritize more basic lesson videos. Furthermore, if the user is tired, the providing unit can prioritize easier lesson videos. This allows for efficient provision of lesson videos by determining the order in which the lesson videos are provided based on the user's emotions.

[0103] When providing lesson videos, the providing unit can select the optimal providing method by taking into account the user's geographical location information. Specific methods for acquiring geographical location information include, but are not limited to, GPS data, address information, etc. For example, the providing unit prioritizes lesson videos related to a region based on the user's geographical location information. The providing unit can also prioritize lesson videos related to the culture or history of a region by taking into account the user's geographical location information. The providing unit can also prioritize lesson videos tailored to the characteristics of a region based on the user's geographical location information. This allows the optimal providing method to be selected by taking into account the user's geographical location information.

[0104] When providing lesson videos, the providing unit can analyze the user's social media activity to customize the content to be provided. Specific methods for analyzing social media activity include, but are not limited to, for example, the content of posts and the reactions of followers. For example, the providing unit analyzes the user's social media activity and reflects the user's interests in the lessons. The providing unit can also provide related lesson videos based on the content of the user's social media posts. The providing unit can also provide related lesson videos by referring to the activities of the user's friends on social media. In this way, related lesson videos can be provided by analyzing the user's social media activity.

[0105] When providing the lesson video, the providing unit can customize the providing method by reflecting the user's past feedback. Specific methods for acquiring past feedback include, but are not limited to, survey results, comments, etc. For example, the providing unit improves the providing method based on the user's past feedback. The providing unit can also customize the providing method by reflecting the user's past feedback. The providing unit can also provide the optimal providing method by referring to the user's past feedback. In this way, the optimal providing method can be provided by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by the reception device 38 of the smart device 14 and receives lesson content via text input, voice input, or image input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input lesson content using text analysis, voice analysis, or image analysis. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates lesson videos based on the analyzed content. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides the generated lesson videos via online distribution, download, or streaming. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned input unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the smart glasses 214 and receives voice input of the lesson content. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input lesson content through voice analysis. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a lesson video based on the analyzed content. The provision unit is realized by the speaker 240 of the smart glasses 214 and provides the generated lesson video via online distribution, download, or streaming. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the headset-type terminal 314 and receives voice input of the lesson content. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input lesson content by voice analysis. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a lesson video based on the analyzed content. The provision unit is realized, for example, by the display 343 of the headset-type terminal 314 and provides the generated lesson video via online distribution, download, or streaming. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the robot 414 and receives voice input of the lesson content. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input lesson content by voice analysis. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a lesson video based on the analyzed content. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the generated lesson video by online distribution, download, or streaming.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The input unit can estimate the user's learning style and customize the lesson content input method based on the estimated learning style. For example, if the user is a visual learner, an input method that makes heavy use of images and videos can be selected. If the user is an auditory learner, voice input can be prioritized. If the user is a tactile learner, an interactive input method can be provided. This allows for efficient lesson content input by providing the optimal input method according to the user's learning style.

[0108] When analyzing lesson content, the analysis unit can improve the accuracy of the analysis by referring to the user's learning history. For example, it can identify areas in which the user is weak based on past learning history and perform analysis that focuses on those areas. It can also adjust the level of detail of the analysis taking into account the user's areas of strength. It can also analyze past learning history and provide the optimal analysis method. In this way, by referring to the user's learning history, it is possible to improve the accuracy of the analysis.

[0109] When generating lesson videos, the generation unit can customize the content of the generated lesson videos based on the user's learning goals. For example, if the user is studying for a specific exam, the generation unit can generate lesson videos that are specialized for the scope of that exam. Also, if the user wants to acquire a specific skill, the generation unit can generate lesson videos that emphasize content related to that skill. The generation unit can also adjust the difficulty level of the lesson videos to be generated according to the user's learning goals. This allows for efficient learning by customizing the content of lesson videos based on the user's learning goals.

[0110] The providing unit can estimate the user's emotions and adjust the timing of providing the lesson videos based on the estimated user emotions. For example, if the user is feeling stressed, the lesson videos can be provided at a time when the user can relax. Also, if the user is concentrating, the lesson videos can be provided at a time when the user can study efficiently. Also, if the user is tired, the lesson videos can be provided after a break. In this way, efficient learning is possible by adjusting the timing of provision according to the user's emotions.

[0111] When providing lesson videos, the providing unit can select the optimal providing method taking into account the user's device environment. For example, if the user is using a smartphone, it can provide lesson videos optimized for mobile devices. Also, if the user is using a tablet, it can provide lesson videos suitable for large screens. Also, if the user is using a PC, it can provide high-resolution lesson videos. This allows for efficient learning by selecting the optimal providing method according to the user's device environment.

[0112] The input unit can estimate the user's emotions and adjust the difficulty of the lesson content to be input based on the estimated user's emotions. For example, if the user is relaxed, the input unit can input more difficult lesson content. If the user is nervous, the input unit can input basic lesson content. If the user is excited, the input unit can input challenging lesson content. This allows for efficient learning by adjusting the difficulty of the lesson content according to the user's emotions.

[0113] When analyzing lesson content, the analysis unit can customize the analysis method by taking into account the user's learning style. For example, for visual learners, the analysis can make extensive use of graphs and diagrams. For auditory learners, the analysis can also include audio commentary. It can also provide interactive analysis methods for tactile learners. This allows the analysis method to be customized according to the user's learning style, enabling efficient analysis.

[0114] The generation unit can estimate the user's emotions and adjust the effects of the lesson video based on the estimated user's emotions. For example, if the user is relaxed, a calm effect can be used. If the user is excited, a visually stimulating effect can be used. If the user is focused, a simple effect can be used. This allows for efficient learning by adjusting the effects of the lesson video according to the user's emotions.

[0115] When providing the lesson videos, the providing unit can select the optimal providing method taking into account the user's internet connection status. For example, if the user's internet connection is unstable, the lesson videos can be provided in download format. Alternatively, if the user's internet connection is fast, the lesson videos can be provided in streaming format. Alternatively, if the user's internet connection is limited, the lesson videos can be provided in low resolution. This allows for efficient learning by selecting the optimal providing method depending on the user's internet connection status.

[0116] The providing unit can estimate the user's emotions and adjust the frequency of providing lesson videos based on the estimated user emotions. For example, if the user is relaxed, lesson videos can be provided more frequently. If the user is feeling stressed, the providing frequency can be reduced. If the user is concentrating, lesson videos can be provided at an appropriate frequency. This allows for efficient learning by adjusting the providing frequency according to the user's emotions.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The input unit inputs lesson content. The lesson content includes, but is not limited to, subject content such as mathematics, science, and history. The input unit can input the lesson content by, for example, text input, voice input, image input, or the like. Step 2: The analysis unit analyzes the lesson content input by the input unit. The analysis is performed by, for example, text analysis, audio analysis, image analysis, or the like, but is not limited to these examples. Step 3: The generation unit generates a lesson video based on the content analyzed by the analysis unit. The lesson video includes, but is not limited to, the length, resolution, and level of detail of the content. The generation unit generates a lesson video that includes, for example, specific problem explanations and explanations of solutions. Step 4: The providing unit provides the lesson video generated by the generating unit, for example, by online distribution, downloading, streaming, or the like, but is not limited to these examples.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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. an input section for inputting lesson contents; an analysis unit that analyzes the lesson content input by the input unit; a generation unit that generates a lesson video based on the content analyzed by the analysis unit; a providing unit that provides the lesson video generated by the generating unit; Equipped with A system characterized by:

2. The input unit Enter specific lesson content for each subject 2. The system of claim 1.

3. The analysis unit Analyzes input lesson content and generates appropriate lesson videos 2. The system of claim 1.

4. The generation unit Generate lesson videos that include problem explanations and solution explanations 2. The system of claim 1.

5. The providing unit The generated lesson videos are provided to students at each cram school.

2. The system of claim 1.

6. The providing unit Share the generated lesson videos with several cram schools 2. The system of claim 1.

7. The input unit Estimate the user's emotions and adjust the timing of lesson content input based on the estimated user emotions 2. The system of claim 1.

8. The input unit When entering lesson content for each subject, the system selects the optimal input method by referring to the history of past lesson content.

2. The system of claim 1.

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