System

Abstract

An object of the system according to the embodiment is to digitize languages and cultures that are in danger of disappearing and to promote protection and inheritance.SOLUTION: In accordance with an embodiment, a system comprises a digital archive component, a language store component, a teaching material generator component, an interactive teaching material provider component, and a cultural content generator component. The digital archiving component digitizes the cultural material. The language storage unit analyzes the data digitized by the digital archiving unit and digitizes the grammar and vocabulary of the language. The teaching material generation unit generates a teaching material for language learning based on the language data digitized by the language storage unit. The interactive teaching material providing section provides the teaching material generated by the teaching material generation section. A cultural content generator generates cultural content based on the material digitized by the digital archiver.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately protect and pass on endangered languages ​​and cultures, and there is room for improvement.

[0005] The system according to the embodiment aims to digitize languages ​​and cultures that are in danger of disappearing, and to promote their protection and succession. [Means for solving the problem]

[0006] The system according to the embodiment includes a digital archiving unit, a language storage unit, a learning material generation unit, an interactive learning material provision unit, and a cultural content generation unit. The digital archiving unit digitizes cultural materials. The language storage unit analyzes the materials digitized by the digital archiving unit and digitizes the grammar and vocabulary of the language. The learning material generation unit generates learning materials for language learning based on the language data digitized by the language storage unit. The interactive learning material provision unit provides the learning materials generated by the learning material generation unit. The cultural content generation unit generates cultural content based on the materials digitized by the digital archiving unit. [Effects of the Invention]

[0007] The system according to the embodiment can digitize languages ​​and cultures that are in danger of disappearing, and promote their protection and succession. [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 aims to protect and pass on endangered languages ​​and cultures. The system digitizes cultural materials, digitizes language grammar and vocabulary, generates language learning materials, provides interactive learning materials, and generates cultural content. For example, the system digitizes cultural materials in various formats, such as audio, video, and text. The system then analyzes audio data of a language with few speakers and digitizes the language's grammar and vocabulary. The system then generates interactive learning materials for learning the language's pronunciation and grammar, and provides them to educational institutions and communities. The system also generates cultural content, such as traditional stories, poetry, and music, and provides them in modern media formats. This allows the system to protect and pass on endangered languages ​​and cultures, thereby passing on a rich cultural heritage to future generations. This allows the system to protect and pass on endangered languages ​​and cultures, thereby passing on a rich cultural heritage to future generations. For example, the system can preserve cultural materials without degradation and pass them on to future generations. The system also automatically generates language learning materials, enabling learners to efficiently master a language. Furthermore, the system can provide cultural content in modern media formats, making culture more accessible to younger generations.

[0029] A cultural preservation system according to an embodiment includes a digital archiving unit, a language preservation unit, a teaching material generation unit, an interactive teaching material provision unit, and a cultural content generation unit. The digital archiving unit digitizes cultural materials. Examples of cultural materials include, but are not limited to, audio, video, and text. For example, the digital archiving unit digitizes audio materials as high-quality audio files. The digital archiving unit can also digitize video materials as high-resolution video files. Furthermore, the digital archiving unit can digitize text materials using scanning technology. For example, the digital archiving unit digitizes and preserves videos of traditional songs and dances. The language preservation unit analyzes the materials digitized by the digital archiving unit and digitizes the grammar and vocabulary of the language. For example, the language preservation unit analyzes audio data of a language with a small number of speakers and digitizes grammar rules and word lists. The language preservation unit can also create a pronunciation guide for the language. For example, the language preservation unit uses generative AI to extract and digitize grammar rules from audio data. The teaching material generation unit generates teaching materials for language learning based on the language data digitized by the language storage unit. The teaching material generation unit generates, for example, interactive teaching materials for learning language pronunciation and grammar. The teaching material generation unit can also automatically generate teaching materials according to the learner's level using generative AI. For example, the teaching material generation unit generates pronunciation practice materials for beginners. The interactive teaching material provision unit provides the teaching materials generated by the teaching material generation unit. The interactive teaching material provision unit provides the teaching materials to, for example, educational institutions or communities. The interactive teaching material provision unit can also provide the teaching materials through an online platform. For example, the interactive teaching material provision unit publishes the generated teaching materials on a website so that learners can access them. The cultural content generation unit generates cultural content based on the materials digitized by the digital archiving unit. The cultural content generation unit generates cultural content such as traditional stories, poems, and music.The cultural content generator can also use generative AI to provide content in modern media formats. For example, the cultural content generator can create animations based on traditional stories. This allows the cultural preservation system according to the embodiment to protect and pass on endangered languages ​​and cultures, thereby passing on a rich cultural heritage to future generations.

[0030] The digital archiving unit can digitize cultural materials in various formats, including audio, video, and text. For example, the digital archiving unit digitizes audio materials as high-quality audio files. For example, the digital archiving unit may record and digitize traditional songs and storytelling. The digital archiving unit can also digitize video materials as high-resolution video files. For example, the digital archiving unit digitizes videos of traditional dances and rituals. The digital archiving unit can also digitize text materials using scanning technology. For example, the digital archiving unit may scan and digitize ancient documents and handwritten documents. This allows for the digitization of various formats of cultural materials. Some or all of the above-mentioned processes in the digital archiving unit may be performed using or without the generation AI. For example, the digital archiving unit may input audio materials into the generation AI and have the generation AI digitize the audio data.

[0031] The language storage unit can analyze speech data of a language with a small number of speakers and digitize the grammar and vocabulary of the language. For example, the language storage unit can analyze speech data of a language with a small number of speakers and digitize grammar rules and word lists. For example, the language storage unit can use a generation AI to extract and digitize grammar rules from the speech data. The language storage unit can also create a pronunciation guide for the language. For example, the language storage unit can use a generation AI to generate a pronunciation guide from the speech data. The language storage unit can also digitize the vocabulary of the language. For example, the language storage unit can use a generation AI to generate a word list from the speech data. This makes it possible to digitize the grammar and vocabulary of a language with a small number of speakers. Some or all of the above-mentioned processing in the language storage unit may be performed using or without the generation AI. For example, the language storage unit can input speech data to the generation AI and have the generation AI digitize the grammar rules and word lists.

[0032] The teaching material generation unit can generate interactive teaching materials for learning language pronunciation and grammar. The teaching material generation unit generates, for example, interactive teaching materials for learning language pronunciation and grammar. For example, the teaching material generation unit generates interactive teaching materials for pronunciation practice using a generation AI. The teaching material generation unit can also generate interactive teaching materials for grammar learning. For example, the teaching material generation unit generates interactive teaching materials including grammar questions using a generation AI. The teaching material generation unit can also generate interactive teaching materials according to the level of the learner. For example, the teaching material generation unit generates pronunciation practice materials for beginners. This makes it possible to generate interactive teaching materials for learning language pronunciation and grammar. Some or all of the above-mentioned processes in the teaching material generation unit may be performed using or without the generation AI. For example, the teaching material generation unit can input language data to the generation AI and cause the generation AI to generate interactive teaching materials.

[0033] The interactive teaching material providing unit can provide the generated teaching materials to educational institutions or communities. For example, the interactive teaching material providing unit provides the generated teaching materials to educational institutions or communities. For example, the interactive teaching material providing unit provides the generated teaching materials to schools or universities. The interactive teaching material providing unit can also provide teaching materials to local communities. For example, the interactive teaching material providing unit provides the generated teaching materials to local cultural centers. The interactive teaching material providing unit can also provide teaching materials through an online platform. For example, the interactive teaching material providing unit publishes the generated teaching materials on a website so that learners can access them. In this way, the generated teaching materials can be provided to educational institutions or communities. Some or all of the above-mentioned processing in the interactive teaching material providing unit may be performed using or without the generation AI. For example, the interactive teaching material providing unit can input the generated teaching materials into the generation AI and cause the generation AI to optimize the provision method.

[0034] The cultural content generation unit can generate cultural content such as traditional stories, poems, or music and provide it in a modern media format. The cultural content generation unit generates cultural content such as, for example, traditional stories, poems, or music. For example, the cultural content generation unit uses a generation AI to create an animation based on a traditional story. The cultural content generation unit can also create music based on a poem. For example, the cultural content generation unit uses a generation AI to generate a song based on a poem. The cultural content generation unit can also provide content in a modern media format. For example, the cultural content generation unit uses a generation AI to create a game based on a traditional story. In this way, cultural content such as traditional stories, poems, or music can be generated and provided in a modern media format. Some or all of the above-mentioned processing in the cultural content generation unit may be performed using or without the generation AI. For example, the cultural content generation unit can input a traditional story into the generation AI and cause the generation AI to generate an animation.

[0035] The digital archiving unit can automatically detect the deterioration state of the materials during digital archiving and select an appropriate digitization method. For example, the digital archiving unit can automatically detect the deterioration state of the materials and select an appropriate digitization method. For example, if the paper quality of the materials has deteriorated, the digital archiving unit can digitize them by increasing the scan resolution. Furthermore, if the video of the materials has deteriorated, the digital archiving unit can digitize them using video correction technology. Furthermore, if the audio of the materials has deteriorated, the digital archiving unit can digitize them using noise removal technology. This allows the deterioration state of the materials to be automatically detected and the optimal digitization method to be selected. Some or all of the above-described processing in the digital archiving unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the digital archiving unit can input the deterioration state of the materials into a generation AI and have the generation AI select the optimal digitization method.

[0036] The digital archiving unit can apply different digitization algorithms based on the content of the materials during digital archiving. For example, the digital archiving unit can digitize text materials as text data using OCR technology. In addition, the digital archiving unit can digitize video materials frame by frame using video analysis technology. In addition, the digital archiving unit can digitize audio materials as audio data using voice recognition technology. This allows different digitization algorithms to be applied based on the content of the materials. Some or all of the above-described processing in the digital archiving unit can be performed using or without a generation AI. For example, the digital archiving unit can input the content of the materials into a generation AI and have the generation AI apply an appropriate digitization algorithm.

[0037] The digital archiving unit can set appropriate digitization settings based on the storage environment of the materials during digital archiving. The digital archiving unit, for example, sets optimal digitization settings taking into account the storage environment of the materials. For example, for materials stored in a hot and humid environment, the digital archiving unit performs low-temperature scanning to prevent deterioration. For materials stored in a dry environment, the digital archiving unit can also scan after taking anti-static measures. For materials sensitive to light, the digital archiving unit can also scan at low light levels. This allows optimal digitization settings to be set taking into account the storage environment of the materials. Some or all of the above-mentioned processes in the digital archiving unit may be performed using or without the generation AI. For example, the digital archiving unit can input storage environment data of the materials into the generation AI and have the generation AI execute optimal digitization settings.

[0038] When digitally archiving, the digital archiving unit can determine the priority of digitization taking into account the geographic distribution of materials. The digital archiving unit determines the priority of digitization taking into account, for example, the geographic distribution of materials. For example, the digital archiving unit prioritizes digitization of geographically important materials. The digital archiving unit can also prioritize digitization of geographically rare materials. The digital archiving unit can also prioritize digitization of materials that are geographically distributed over a wide area. This makes it possible to determine the priority of digitization taking into account the geographic distribution of materials. Some or all of the above-mentioned processing in the digital archiving unit may be performed using or without the generation AI. For example, the digital archiving unit can input geographic distribution data of materials into the generation AI and have the generation AI determine the priority of digitization.

[0039] The digital archiving unit can improve the accuracy of digitization by referring to literature related to the material during digital archiving. The digital archiving unit can, for example, improve the accuracy of digitization by referring to literature related to the material. For example, the digital archiving unit can improve the accuracy of digitization by referring to literature related to the material. The digital archiving unit can also select a digitization method by referring to literature related to the material. The digital archiving unit can also determine a priority for digitization by referring to literature related to the material. This can improve the accuracy of digitization by referring to literature related to the material. Some or all of the above-mentioned processing in the digital archiving unit can be performed using or without the generation AI. For example, the digital archiving unit can input literature data related to the material into the generation AI and have the generation AI improve the accuracy of digitization.

[0040] The digital archiving unit can select a digitization method taking into consideration the cultural value of the material when digitally archiving. The digital archiving unit selects a digitization method taking into consideration, for example, the cultural value of the material. For example, the digital archiving unit prioritizes digitizing culturally important materials. The digital archiving unit can also prioritize digitizing culturally rare materials. The digital archiving unit can also prioritize digitizing materials that are culturally widely distributed. This makes it possible to select a digitization method taking into consideration the cultural value of the material. Some or all of the above-mentioned processing in the digital archiving unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the digital archiving unit can input cultural value data of the material into the generation AI and have the generation AI select a digitization method.

[0041] The language storage unit can determine the analysis priority based on the frequency of language use when storing the language. The language storage unit determines the analysis priority based on, for example, the frequency of language use. For example, the language storage unit prioritizes analysis of frequently used languages. The language storage unit can also prioritize analysis of less frequently used languages. The language storage unit can also prioritize analysis of medium-frequency used languages. This makes it possible to determine the analysis priority based on the frequency of language use. Some or all of the above-mentioned processing in the language storage unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the language storage unit can input language frequency data into the generation AI and have the generation AI determine the analysis priority.

[0042] The language storage unit can select an analysis algorithm based on the regional characteristics of the language when storing the language. The language storage unit selects an analysis algorithm based on, for example, the regional characteristics of the language. For example, the language storage unit selects an analysis algorithm based on the regional characteristics. The language storage unit can also determine an analysis priority based on the regional characteristics. The language storage unit can also select an analysis method based on the regional characteristics. This makes it possible to select an analysis algorithm taking the regional characteristics of the language into consideration. Some or all of the above-mentioned processing in the language storage unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the language storage unit can input regional characteristic data of the language to the generation AI and cause the generation AI to select an analysis algorithm.

[0043] The language storage unit can improve the accuracy of analysis by referring to the historical evolution of the language when storing the language. The language storage unit, for example, improves the accuracy of analysis by referring to the historical evolution of the language. For example, the language storage unit improves the accuracy of analysis by referring to the historical evolution of the language. The language storage unit can also select an analysis method by referring to the historical evolution of the language. The language storage unit can also determine an analysis priority by referring to the historical evolution of the language. This makes it possible to improve the accuracy of analysis by referring to the historical evolution of the language. Some or all of the above-mentioned processing in the language storage unit may be performed using or without the generation AI. For example, the language storage unit can input language historical evolution data into the generation AI and cause the generation AI to improve the accuracy of analysis.

[0044] The language storage unit can determine the analysis priority based on the geographical distribution of languages ​​when storing languages. The language storage unit determines the analysis priority, for example, taking into account the geographical distribution of languages. For example, the language storage unit prioritizes analysis of geographically important languages. The language storage unit can also prioritize analysis of geographically rare languages. The language storage unit can also prioritize analysis of languages ​​that are geographically widely distributed. This makes it possible to determine the analysis priority taking into account the geographical distribution of languages. Some or all of the above-mentioned processing in the language storage unit may be performed using or without the generation AI. For example, the language storage unit can input language geographical distribution data to the generation AI and have the generation AI determine the analysis priority.

[0045] The language storage unit can improve the accuracy of analysis by referring to literature related to the language when storing the language. The language storage unit, for example, improves the accuracy of analysis by referring to literature related to the language. For example, the language storage unit improves the accuracy of analysis by referring to literature related to the language. The language storage unit can also select an analysis method by referring to literature related to the language. The language storage unit can also determine an analysis priority by referring to literature related to the language. This makes it possible to improve the accuracy of analysis by referring to literature related to the language. Some or all of the above-mentioned processing in the language storage unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the language storage unit can input literature data related to the language into the generation AI and cause the generation AI to improve the accuracy of analysis.

[0046] The language storage unit can select an analysis method based on the cultural background of the language when storing the language. The language storage unit selects the analysis method based on, for example, the cultural background of the language. For example, the language storage unit selects an analysis algorithm based on the cultural background. The language storage unit can also determine an analysis priority based on the cultural background. The language storage unit can also select an analysis method based on the cultural background. This makes it possible to select an analysis method taking into account the cultural background of the language. Some or all of the above-mentioned processing in the language storage unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the language storage unit can input cultural background data of the language into the generation AI and have the generation AI select an analysis method.

[0047] The teaching material generation unit can adjust the difficulty of the teaching material based on the learner's stage when generating the teaching material. The teaching material generation unit adjusts the difficulty of the teaching material based on, for example, the learner's stage. For example, the teaching material generation unit generates teaching material for beginners. The teaching material generation unit can also generate teaching material for intermediate learners. The teaching material generation unit can also generate teaching material for advanced learners. This makes it possible to adjust the difficulty of the teaching material based on the learner's stage. Some or all of the above-mentioned processing in the teaching material generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the teaching material generation unit can input learner's stage data into the generation AI and cause the generation AI to adjust the difficulty of the teaching material.

[0048] The teaching material generation unit can apply different teaching material generation algorithms depending on the characteristics of the language when generating teaching materials. The teaching material generation unit applies different teaching material generation algorithms depending on, for example, the characteristics of the language. For example, the teaching material generation unit selects a teaching material generation algorithm based on the grammatical characteristics of the language. The teaching material generation unit can also select a teaching material generation algorithm based on the pronunciation characteristics of the language. The teaching material generation unit can also select a teaching material generation algorithm based on the lexical characteristics of the language. This makes it possible to apply different teaching material generation algorithms depending on the characteristics of the language. Some or all of the above-mentioned processing in the teaching material generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the teaching material generation unit can input language characteristic data into the generation AI and cause the generation AI to apply an appropriate teaching material generation algorithm.

[0049] The teaching material generation unit can improve the accuracy of the teaching materials by referring to past learning data when generating the teaching materials. The teaching material generation unit, for example, improves the accuracy of the teaching materials by referring to past learning data. For example, the teaching material generation unit improves the accuracy of the teaching materials by referring to past learning data. The teaching material generation unit can also select a teaching material method by referring to past learning data. The teaching material generation unit can also determine the priority of the teaching materials by referring to past learning data. This makes it possible to improve the accuracy of the teaching materials by referring to past learning data. Some or all of the above-mentioned processing in the teaching material generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the teaching material generation unit can input past learning data into the generation AI and cause the generation AI to improve the accuracy of the teaching materials.

[0050] The teaching material generation unit can customize the content of the teaching material taking into account the geographical distribution of learners when generating teaching material. The teaching material generation unit customizes the content of the teaching material, for example, taking into account the geographical distribution of learners. For example, the teaching material generation unit generates teaching material specialized for the learner's region. The teaching material generation unit can also generate teaching material based on the culture of the learner's region. The teaching material generation unit can also generate teaching material based on the linguistic characteristics of the learner's region. This makes it possible to customize the content of the teaching material taking into account the geographical distribution of learners. Some or all of the above-mentioned processing in the teaching material generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the teaching material generation unit can input learners' geographical distribution data into the generation AI and cause the generation AI to customize the content of the teaching material.

[0051] The teaching material generation unit can improve the accuracy of the teaching materials by referring to the learner's related literature when generating the teaching materials. The teaching material generation unit, for example, improves the accuracy of the teaching materials by referring to the learner's related literature. For example, the teaching material generation unit improves the accuracy of the teaching materials by referring to the learner's related literature. The teaching material generation unit can also select a teaching material method by referring to the learner's related literature. The teaching material generation unit can also determine the priority of the teaching materials by referring to the learner's related literature. This makes it possible to improve the accuracy of the teaching materials by referring to the learner's related literature. Some or all of the above-mentioned processing in the teaching material generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the teaching material generation unit can input the learner's related literature data into the generation AI and cause the generation AI to improve the accuracy of the teaching materials.

[0052] The teaching material generation unit can select a teaching material technique based on the learner's cultural background when generating teaching materials. The teaching material generation unit selects a teaching material technique based on, for example, the learner's cultural background. For example, the teaching material generation unit selects a teaching material generation algorithm based on the learner's cultural background. The teaching material generation unit can also determine the priority of teaching materials based on the learner's cultural background. The teaching material generation unit can also select a teaching material technique based on the learner's cultural background. This makes it possible to select a teaching material technique taking the learner's cultural background into consideration. Some or all of the above-mentioned processing in the teaching material generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the teaching material generation unit can input the learner's cultural background data into the generation AI and cause the generation AI to select a teaching material technique.

[0053] When providing interactive teaching material, the interactive teaching material providing unit can select an appropriate delivery method by referring to the learner's past learning history. The interactive teaching material providing unit, for example, selects the optimal delivery method by referring to the learner's past learning history. For example, the interactive teaching material providing unit provides optimal interactive teaching material by referring to the learner's past learning history. The interactive teaching material providing unit can also select a delivery method for the interactive teaching material by referring to the learner's past learning history. The interactive teaching material providing unit can also determine the priority of the interactive teaching material by referring to the learner's past learning history. This makes it possible to select the optimal delivery method by referring to the learner's past learning history. Some or all of the above-mentioned processing in the interactive teaching material providing unit may be performed using or without the generation AI. For example, the interactive teaching material providing unit can input the learner's past learning history data into the generation AI and have the generation AI select the delivery method.

[0054] When providing interactive teaching materials, the interactive teaching material providing unit can customize the content to be provided based on the learner's current learning situation. The interactive teaching material providing unit customizes the content to be provided based on, for example, the learner's current learning situation. For example, the interactive teaching material providing unit refers to the learner's current learning situation and provides optimal interactive teaching materials. The interactive teaching material providing unit can also refer to the learner's current learning situation and select a method for providing the interactive teaching materials. The interactive teaching material providing unit can also refer to the learner's current learning situation and determine the priority of the interactive teaching materials. This makes it possible to customize the content to be provided based on the learner's current learning situation. Some or all of the above-mentioned processing in the interactive teaching material providing unit may be performed using or without the generation AI. For example, the interactive teaching material providing unit can input data on the learner's current learning situation into the generation AI and cause the generation AI to customize the content to be provided.

[0055] When providing interactive teaching materials, the interactive teaching material providing unit can select an appropriate delivery method based on the learner's device information. The interactive teaching material providing unit selects the optimal delivery method, for example, by taking into account the learner's device information. For example, if the learner is using a smartphone, the interactive teaching material providing unit can provide interactive teaching materials that fit the screen size. Furthermore, if the learner is using a tablet, the interactive teaching material providing unit can also provide interactive teaching materials optimized for a large screen. Furthermore, if the learner is using a smartwatch, the interactive teaching material providing unit can also provide interactive teaching materials that are concise and highly visible. This makes it possible to select the optimal delivery method by taking into account the learner's device information. Some or all of the above-mentioned processing in the interactive teaching material providing unit may be performed using or without the generation AI. For example, the interactive teaching material providing unit can input the learner's device information into the generation AI and have the generation AI select the delivery method.

[0056] When providing interactive teaching materials, the interactive teaching material providing unit can make the provided content multilingual according to the learner's language setting. The interactive teaching material providing unit makes the provided content multilingual according to, for example, the learner's language setting. For example, the interactive teaching material providing unit automatically sets the language of the interactive teaching material based on the language setting of the learner's device. The interactive teaching material providing unit can also provide a language switching function when a learner uses multiple languages. The interactive teaching material providing unit can also provide the interactive teaching material in a specific language when the learner selects that language. This makes it possible to make the provided content multilingual according to the learner's language setting. Some or all of the above-mentioned processing in the interactive teaching material providing unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the interactive teaching material providing unit can input the learner's language setting data into the generation AI and cause the generation AI to make the provided content multilingual.

[0057] When providing interactive teaching materials, the interactive teaching material providing unit can analyze the learner's social media activities and provide related information. The interactive teaching material providing unit, for example, analyzes the learner's social media activities and provides related information. For example, the interactive teaching material providing unit can provide information about places where the learner has checked in on social media. The interactive teaching material providing unit can also analyze the learner's social media posts and provide information about related tourist spots and stores. The interactive teaching material providing unit can also provide information about related places and events by referring to the activities of the learner's friends on social media. In this way, the learner's social media activities can be analyzed and related information can be provided. Some or all of the above-mentioned processing in the interactive teaching material providing unit may be performed using or without the generation AI. For example, the interactive teaching material providing unit can input the learner's social media activity data into the generation AI and cause the generation AI to provide related information.

[0058] The cultural content generation unit can adjust the level of detail of the cultural content generated based on the importance of the content when generating the cultural content. The cultural content generation unit adjusts the level of detail of the cultural content generated based on, for example, the importance of the content. For example, the cultural content generation unit generates highly important cultural content in detail. The cultural content generation unit can also generate less important cultural content in a simplified manner. The cultural content generation unit can also generate moderately important cultural content in an appropriate manner. This allows the level of detail of the cultural content generated to be adjusted based on the importance of the content. Some or all of the above-described processing in the cultural content generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the cultural content generation unit can input content importance data into the generation AI and cause the generation AI to adjust the level of detail of the content.

[0059] The cultural content generation unit can apply different generation algorithms depending on the content category when generating cultural content. The cultural content generation unit applies different generation algorithms depending on the content category, for example. For example, the cultural content generation unit selects a generation algorithm based on the story category. The cultural content generation unit can also select a generation algorithm based on the poetry category. The cultural content generation unit can also select a generation algorithm based on the music category. This makes it possible to apply different generation algorithms depending on the content category. Some or all of the above-mentioned processing in the cultural content generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the cultural content generation unit can input content category data into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0060] When generating cultural content, the cultural content generation unit can improve the accuracy of generation by referring to past content data. The cultural content generation unit, for example, improves the accuracy of generation by referring to past content data. For example, the cultural content generation unit improves the accuracy of generation by referring to past content data. The cultural content generation unit can also select a generation method by referring to past content data. The cultural content generation unit can also determine a generation priority by referring to past content data. This makes it possible to improve the accuracy of generation by referring to past content data. Some or all of the above-described processing in the cultural content generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the cultural content generation unit can input past content data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0061] When generating cultural content, the cultural content generation unit can determine generation priorities based on the geographical distribution of the content. The cultural content generation unit, for example, determines generation priorities taking into account the geographical distribution of the content. For example, the cultural content generation unit prioritizes generating geographically important cultural content. The cultural content generation unit can also prioritize generating geographically rare cultural content. The cultural content generation unit can also prioritize generating cultural content that is geographically distributed over a wide area. This makes it possible to determine generation priorities taking into account the geographical distribution of the content. Some or all of the above-described processing in the cultural content generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the cultural content generation unit can input geographical distribution data of the content into the generation AI and have the generation AI determine the generation priorities.

[0062] When generating cultural content, the cultural content generation unit can improve the accuracy of generation by referring to literature related to the content. The cultural content generation unit, for example, improves the accuracy of generation by referring to literature related to the content. For example, the cultural content generation unit improves the accuracy of generation by referring to literature related to the content. The cultural content generation unit can also select a generation method by referring to literature related to the content. The cultural content generation unit can also determine a generation priority by referring to literature related to the content. This makes it possible to improve the accuracy of generation by referring to literature related to the content. Some or all of the above-mentioned processing in the cultural content generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the cultural content generation unit can input literature data related to the content into the generation AI and cause the generation AI to improve the accuracy of generation.

[0063] When generating cultural content, the cultural content generation unit can select a generation method based on the cultural value of the content. The cultural content generation unit selects a generation method based on, for example, the cultural value of the content. For example, the cultural content generation unit selects a generation algorithm based on the cultural value. The cultural content generation unit can also determine a generation priority based on the cultural value. The cultural content generation unit can also select a generation method based on the cultural value. This makes it possible to select a generation method taking the cultural value of the content into consideration. Some or all of the above-mentioned processing in the cultural content generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the cultural content generation unit can input cultural value data of the content into the generation AI and have the generation AI select a generation method.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The Digital Archiving Department can automatically detect the deterioration state of materials and select the appropriate digitization method. For example, if the paper quality of a material has deteriorated, it can be digitized by increasing the scan resolution. Also, if the image of the material has deteriorated, it can be digitized using image correction technology. Furthermore, if the audio of the material has deteriorated, it can be digitized using noise reduction technology. This makes it possible to automatically detect the deterioration state of materials and select the optimal digitization method.

[0066] The learning material generation unit can adjust the difficulty of the learning material based on the learner's level. For example, learning material for beginners can be generated. Learning material for intermediate learners can also be generated. Learning material for advanced learners can also be generated. This makes it possible to adjust the difficulty of the learning material based on the learner's level.

[0067] The cultural content generation unit can apply different generation algorithms depending on the category of the content. For example, a generation algorithm can be selected based on the category of a story. Alternatively, a generation algorithm can be selected based on the category of a poem. Furthermore, a generation algorithm can be selected based on the category of a music. In this way, different generation algorithms can be applied depending on the category of the content.

[0068] The Digital Archiving Department can determine the priority of digitization taking into account the geographical distribution of materials. For example, geographically important materials can be digitized first. Geographically rare materials can also be digitized first. Furthermore, materials that are geographically distributed over a wide area can be digitized first. In this way, the priority of digitization can be determined taking into account the geographical distribution of materials.

[0069] The language storage unit can improve the accuracy of analysis by referring to the historical evolution of the language. For example, the accuracy of analysis can be improved by referring to the historical evolution of the language. The language storage unit can also select an analysis method by referring to the historical evolution of the language. Furthermore, the accuracy of analysis can be improved by referring to the historical evolution of the language.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The Digital Archiving Department digitizes cultural materials. Cultural materials include audio, video, and text. For example, audio materials are digitized as high-quality audio files, and video materials are digitized as high-resolution video files. Text materials can also be digitized using scanning technology. Specifically, videos of traditional songs and dances are digitized and preserved. Step 2: The Language Preservation Department analyzes the materials digitized by the Digital Archiving Department and digitizes the language's grammar and vocabulary. For example, it analyzes audio data from a language with a small number of speakers and digitizes grammar rules and word lists. It can also create a pronunciation guide for the language. Specifically, it uses generative AI to extract grammar rules from audio data and digitize them. Step 3: The teaching material generation unit generates teaching materials for language learning based on the language data digitized by the language storage unit. For example, it can generate interactive teaching materials for learning language pronunciation and grammar, and can automatically generate teaching materials appropriate for the learner's level. Specifically, it generates pronunciation practice materials for beginners. Step 4: The interactive teaching material provider provides the teaching materials generated by the teaching material generator. For example, the teaching materials can be provided to educational institutions or communities, or through an online platform. Specifically, the generated teaching materials can be published on a website so that learners can access them. Step 5: The Cultural Content Generation Unit generates cultural content based on the materials digitized by the Digital Archiving Unit. For example, cultural content such as traditional stories, poetry, and music can be generated, and the generation AI can be used to provide the content in modern media formats. Specifically, an animation based on a traditional story can be created.

[0072] (Example 2) A system according to an embodiment of the present invention aims to protect and pass on endangered languages ​​and cultures. The system digitizes cultural materials, digitizes language grammar and vocabulary, generates language learning materials, provides interactive learning materials, and generates cultural content. For example, the system digitizes cultural materials in various formats, such as audio, video, and text. The system then analyzes audio data of a language with few speakers and digitizes the language's grammar and vocabulary. The system then generates interactive learning materials for learning the language's pronunciation and grammar, and provides them to educational institutions and communities. The system also generates cultural content, such as traditional stories, poetry, and music, and provides them in modern media formats. This allows the system to protect and pass on endangered languages ​​and cultures, thereby passing on a rich cultural heritage to future generations. This allows the system to protect and pass on endangered languages ​​and cultures, thereby passing on a rich cultural heritage to future generations. For example, the system can preserve cultural materials without degradation and pass them on to future generations. The system also automatically generates language learning materials, enabling learners to efficiently master a language. Furthermore, the system can provide cultural content in modern media formats, making culture more accessible to younger generations.

[0073] A cultural preservation system according to an embodiment includes a digital archiving unit, a language preservation unit, a teaching material generation unit, an interactive teaching material provision unit, and a cultural content generation unit. The digital archiving unit digitizes cultural materials. Examples of cultural materials include, but are not limited to, audio, video, and text. For example, the digital archiving unit digitizes audio materials as high-quality audio files. The digital archiving unit can also digitize video materials as high-resolution video files. Furthermore, the digital archiving unit can digitize text materials using scanning technology. For example, the digital archiving unit digitizes and preserves videos of traditional songs and dances. The language preservation unit analyzes the materials digitized by the digital archiving unit and digitizes the grammar and vocabulary of the language. For example, the language preservation unit analyzes audio data of a language with a small number of speakers and digitizes grammar rules and word lists. The language preservation unit can also create a pronunciation guide for the language. For example, the language preservation unit uses generative AI to extract and digitize grammar rules from audio data. The teaching material generation unit generates teaching materials for language learning based on the language data digitized by the language storage unit. The teaching material generation unit generates, for example, interactive teaching materials for learning language pronunciation and grammar. The teaching material generation unit can also automatically generate teaching materials according to the learner's level using generative AI. For example, the teaching material generation unit generates pronunciation practice materials for beginners. The interactive teaching material provision unit provides the teaching materials generated by the teaching material generation unit. The interactive teaching material provision unit provides the teaching materials to, for example, educational institutions or communities. The interactive teaching material provision unit can also provide the teaching materials through an online platform. For example, the interactive teaching material provision unit publishes the generated teaching materials on a website so that learners can access them. The cultural content generation unit generates cultural content based on the materials digitized by the digital archiving unit. The cultural content generation unit generates cultural content such as traditional stories, poems, and music.The cultural content generator can also use generative AI to provide content in modern media formats. For example, the cultural content generator can create animations based on traditional stories. This allows the cultural preservation system according to the embodiment to protect and pass on endangered languages ​​and cultures, thereby passing on a rich cultural heritage to future generations.

[0074] The digital archiving unit can digitize cultural materials in various formats, including audio, video, and text. For example, the digital archiving unit digitizes audio materials as high-quality audio files. For example, the digital archiving unit may record and digitize traditional songs and storytelling. The digital archiving unit can also digitize video materials as high-resolution video files. For example, the digital archiving unit digitizes videos of traditional dances and rituals. The digital archiving unit can also digitize text materials using scanning technology. For example, the digital archiving unit may scan and digitize ancient documents and handwritten documents. This allows for the digitization of various formats of cultural materials. Some or all of the above-mentioned processes in the digital archiving unit may be performed using or without the generation AI. For example, the digital archiving unit may input audio materials into the generation AI and have the generation AI digitize the audio data.

[0075] The language storage unit can analyze speech data of a language with a small number of speakers and digitize the grammar and vocabulary of the language. For example, the language storage unit can analyze speech data of a language with a small number of speakers and digitize grammar rules and word lists. For example, the language storage unit can use a generation AI to extract and digitize grammar rules from the speech data. The language storage unit can also create a pronunciation guide for the language. For example, the language storage unit can use a generation AI to generate a pronunciation guide from the speech data. The language storage unit can also digitize the vocabulary of the language. For example, the language storage unit can use a generation AI to generate a word list from the speech data. This makes it possible to digitize the grammar and vocabulary of a language with a small number of speakers. Some or all of the above-mentioned processing in the language storage unit may be performed using or without the generation AI. For example, the language storage unit can input speech data to the generation AI and have the generation AI digitize the grammar rules and word lists.

[0076] The teaching material generation unit can generate interactive teaching materials for learning language pronunciation and grammar. The teaching material generation unit generates, for example, interactive teaching materials for learning language pronunciation and grammar. For example, the teaching material generation unit generates interactive teaching materials for pronunciation practice using a generation AI. The teaching material generation unit can also generate interactive teaching materials for grammar learning. For example, the teaching material generation unit generates interactive teaching materials including grammar questions using a generation AI. The teaching material generation unit can also generate interactive teaching materials according to the level of the learner. For example, the teaching material generation unit generates pronunciation practice materials for beginners. This makes it possible to generate interactive teaching materials for learning language pronunciation and grammar. Some or all of the above-mentioned processes in the teaching material generation unit may be performed using or without the generation AI. For example, the teaching material generation unit can input language data to the generation AI and cause the generation AI to generate interactive teaching materials.

[0077] The interactive teaching material providing unit can provide the generated teaching materials to educational institutions or communities. For example, the interactive teaching material providing unit provides the generated teaching materials to educational institutions or communities. For example, the interactive teaching material providing unit provides the generated teaching materials to schools or universities. The interactive teaching material providing unit can also provide teaching materials to local communities. For example, the interactive teaching material providing unit provides the generated teaching materials to local cultural centers. The interactive teaching material providing unit can also provide teaching materials through an online platform. For example, the interactive teaching material providing unit publishes the generated teaching materials on a website so that learners can access them. In this way, the generated teaching materials can be provided to educational institutions or communities. Some or all of the above-mentioned processing in the interactive teaching material providing unit may be performed using or without the generation AI. For example, the interactive teaching material providing unit can input the generated teaching materials into the generation AI and cause the generation AI to optimize the provision method.

[0078] The cultural content generation unit can generate cultural content such as traditional stories, poems, or music and provide it in a modern media format. The cultural content generation unit generates cultural content such as traditional stories, poems, or music. For example, the cultural content generation unit uses a generation AI to create an animation based on a traditional story. The cultural content generation unit can also create music based on a poem. For example, the cultural content generation unit uses a generation AI to generate a song based on a poem. The cultural content generation unit can also provide content in a modern media format. For example, the cultural content generation unit uses a generation AI to create a game based on a traditional story. In this way, cultural content such as traditional stories, poems, or music can be generated and provided in a modern media format. Some or all of the above-mentioned processing in the cultural content generation unit may be performed using or without the generation AI. For example, the cultural content generation unit can input a traditional story into the generation AI and cause the generation AI to generate an animation.

[0079] The digital archiving unit can estimate a user's emotions and prioritize materials to be digitized based on the estimated user emotions. The digital archiving unit, for example, estimates a user's emotions and prioritizes materials to be digitized based on the estimated user emotions. For example, if a user is emotional, the digital archiving unit can prioritize digitizing materials that are likely to evoke emotion. Furthermore, if a user is excited, the digital archiving unit can prioritize digitizing materials that are likely to evoke excitement. Furthermore, if a user is relaxed, the digital archiving unit can prioritize digitizing materials that are likely to evoke relaxation. This allows the priority of materials to be digitized to be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the digital archiving unit may be performed using a generative AI, or may be performed without a generative AI. For example, the digital archiving unit can input the user's emotional data into the generation AI and have the generation AI decide the priority of the materials to be digitized.

[0080] The digital archiving unit can automatically detect the deterioration state of the materials during digital archiving and select an appropriate digitization method. For example, the digital archiving unit can automatically detect the deterioration state of the materials and select an appropriate digitization method. For example, if the paper quality of the materials has deteriorated, the digital archiving unit can digitize them by increasing the scan resolution. Furthermore, if the video of the materials has deteriorated, the digital archiving unit can digitize them using video correction technology. Furthermore, if the audio of the materials has deteriorated, the digital archiving unit can digitize them using noise removal technology. This allows the deterioration state of the materials to be automatically detected and the optimal digitization method to be selected. Some or all of the above-described processing in the digital archiving unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the digital archiving unit can input the deterioration state of the materials into a generation AI and have the generation AI select the optimal digitization method.

[0081] The digital archiving unit can apply different digitization algorithms based on the content of the materials during digital archiving. For example, the digital archiving unit can digitize text materials as text data using OCR technology. In addition, the digital archiving unit can digitize video materials frame by frame using video analysis technology. In addition, the digital archiving unit can digitize audio materials as audio data using voice recognition technology. This allows different digitization algorithms to be applied based on the content of the materials. Some or all of the above-described processing in the digital archiving unit can be performed using or without a generation AI. For example, the digital archiving unit can input the content of the materials into a generation AI and have the generation AI apply an appropriate digitization algorithm.

[0082] The digital archiving unit can set appropriate digitization settings based on the storage environment of the materials during digital archiving. The digital archiving unit, for example, sets optimal digitization settings taking into account the storage environment of the materials. For example, for materials stored in a hot and humid environment, the digital archiving unit performs low-temperature scanning to prevent deterioration. For materials stored in a dry environment, the digital archiving unit can also scan after taking anti-static measures. For materials sensitive to light, the digital archiving unit can also scan at low light levels. This allows optimal digitization settings to be set taking into account the storage environment of the materials. Some or all of the above-mentioned processes in the digital archiving unit may be performed using or without the generation AI. For example, the digital archiving unit can input storage environment data of the materials into the generation AI and have the generation AI execute optimal digitization settings.

[0083] The digital archiving unit can estimate a user's emotions and adjust the display method of digitized materials based on the estimated user emotions. For example, the digital archiving unit can estimate a user's emotions and adjust the display method of digitized materials based on the estimated user emotions. For example, if a user is emotional, the digital archiving unit can enlarge materials that are likely to evoke emotion. Furthermore, if a user is excited, the digital archiving unit can dynamically display materials that are likely to evoke excitement. Furthermore, if a user is relaxed, the digital archiving unit can statically display materials that are likely to evoke relaxation. This allows the display method of digitized materials to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the digital archiving unit may be performed using the generation AI, or may be performed without the generation AI. For example, the digital archiving unit can input user emotion data into the generation AI and have the generation AI adjust the display method of the materials.

[0084] When digitally archiving, the digital archiving unit can determine the priority of digitization taking into account the geographic distribution of materials. The digital archiving unit determines the priority of digitization taking into account, for example, the geographic distribution of materials. For example, the digital archiving unit prioritizes digitization of geographically important materials. The digital archiving unit can also prioritize digitization of geographically rare materials. The digital archiving unit can also prioritize digitization of materials that are geographically distributed over a wide area. This makes it possible to determine the priority of digitization taking into account the geographic distribution of materials. Some or all of the above-mentioned processing in the digital archiving unit may be performed using or without the generation AI. For example, the digital archiving unit can input geographic distribution data of materials into the generation AI and have the generation AI determine the priority of digitization.

[0085] The digital archiving unit can improve the accuracy of digitization by referring to literature related to the material during digital archiving. The digital archiving unit can, for example, improve the accuracy of digitization by referring to literature related to the material. For example, the digital archiving unit can improve the accuracy of digitization by referring to literature related to the material. The digital archiving unit can also select a digitization method by referring to literature related to the material. The digital archiving unit can also determine a priority for digitization by referring to literature related to the material. This can improve the accuracy of digitization by referring to literature related to the material. Some or all of the above-mentioned processing in the digital archiving unit can be performed using or without the generation AI. For example, the digital archiving unit can input literature data related to the material into the generation AI and have the generation AI improve the accuracy of digitization.

[0086] The digital archiving unit can select a digitization method taking into consideration the cultural value of the material when digitally archiving. The digital archiving unit selects a digitization method taking into consideration, for example, the cultural value of the material. For example, the digital archiving unit prioritizes digitizing culturally important materials. The digital archiving unit can also prioritize digitizing culturally rare materials. The digital archiving unit can also prioritize digitizing materials that are culturally widely distributed. This makes it possible to select a digitization method taking into consideration the cultural value of the material. Some or all of the above-mentioned processing in the digital archiving unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the digital archiving unit can input cultural value data of the material into the generation AI and have the generation AI select a digitization method.

[0087] The language storage unit can estimate the user's emotion and adjust the analysis method of the language data based on the estimated user's emotion. For example, the language storage unit can estimate the user's emotion and adjust the analysis method of the language data based on the estimated user's emotion. For example, if the user is emotional, the language storage unit can prioritize analyzing language data that is likely to cause emotion. Furthermore, if the user is excited, the language storage unit can prioritize analyzing language data that is likely to cause excitement. Furthermore, if the user is relaxed, the language storage unit can prioritize analyzing language data that is likely to cause relaxation. This makes it possible to adjust the analysis method of the language data based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the language storage unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the language storage unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the analysis method of the language data.

[0088] The language storage unit can determine the analysis priority based on the frequency of language use when storing the language. The language storage unit determines the analysis priority based on, for example, the frequency of language use. For example, the language storage unit prioritizes analysis of frequently used languages. The language storage unit can also prioritize analysis of less frequently used languages. The language storage unit can also prioritize analysis of medium-frequency used languages. This makes it possible to determine the analysis priority based on the frequency of language use. Some or all of the above-mentioned processing in the language storage unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the language storage unit can input language frequency data into the generation AI and have the generation AI determine the analysis priority.

[0089] The language storage unit can select an analysis algorithm based on the regional characteristics of the language when storing the language. The language storage unit selects an analysis algorithm based on, for example, the regional characteristics of the language. For example, the language storage unit selects an analysis algorithm based on the regional characteristics. The language storage unit can also determine an analysis priority based on the regional characteristics. The language storage unit can also select an analysis method based on the regional characteristics. This makes it possible to select an analysis algorithm taking the regional characteristics of the language into consideration. Some or all of the above-mentioned processing in the language storage unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the language storage unit can input regional characteristic data of the language to the generation AI and cause the generation AI to select an analysis algorithm.

[0090] The language storage unit can improve the accuracy of analysis by referring to the historical evolution of the language when storing the language. The language storage unit, for example, improves the accuracy of analysis by referring to the historical evolution of the language. For example, the language storage unit improves the accuracy of analysis by referring to the historical evolution of the language. The language storage unit can also select an analysis method by referring to the historical evolution of the language. The language storage unit can also determine an analysis priority by referring to the historical evolution of the language. This makes it possible to improve the accuracy of analysis by referring to the historical evolution of the language. Some or all of the above-mentioned processing in the language storage unit may be performed using or without the generation AI. For example, the language storage unit can input language historical evolution data into the generation AI and cause the generation AI to improve the accuracy of analysis.

[0091] The language storage unit can estimate the user's emotion and adjust the display method of the language data based on the estimated user's emotion. For example, the language storage unit estimates the user's emotion and adjusts the display method of the language data based on the estimated user's emotion. For example, when the user is emotional, the language storage unit displays language data that is likely to evoke emotion in a larger size. Furthermore, when the user is excited, the language storage unit can dynamically display language data that is likely to evoke excitement. Furthermore, when the user is relaxed, the language storage unit can statically display language data that is likely to evoke relaxation. This makes it possible to adjust the display method of the language data based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the language storage unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the language storage unit can input user's emotion data into the generation AI and cause the generation AI to adjust the display method of the language data.

[0092] The language storage unit can determine the analysis priority based on the geographical distribution of languages ​​when storing languages. The language storage unit determines the analysis priority, for example, taking into account the geographical distribution of languages. For example, the language storage unit prioritizes analysis of geographically important languages. The language storage unit can also prioritize analysis of geographically rare languages. The language storage unit can also prioritize analysis of languages ​​that are geographically widely distributed. This makes it possible to determine the analysis priority taking into account the geographical distribution of languages. Some or all of the above-mentioned processing in the language storage unit may be performed using or without the generation AI. For example, the language storage unit can input language geographical distribution data to the generation AI and have the generation AI determine the analysis priority.

[0093] The language storage unit can improve the accuracy of analysis by referring to literature related to the language when storing the language. The language storage unit, for example, improves the accuracy of analysis by referring to literature related to the language. For example, the language storage unit improves the accuracy of analysis by referring to literature related to the language. The language storage unit can also select an analysis method by referring to literature related to the language. The language storage unit can also determine an analysis priority by referring to literature related to the language. This makes it possible to improve the accuracy of analysis by referring to literature related to the language. Some or all of the above-mentioned processing in the language storage unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the language storage unit can input literature data related to the language into the generation AI and cause the generation AI to improve the accuracy of analysis.

[0094] The language storage unit can select an analysis method based on the cultural background of the language when storing the language. The language storage unit selects the analysis method based on, for example, the cultural background of the language. For example, the language storage unit selects an analysis algorithm based on the cultural background. The language storage unit can also determine an analysis priority based on the cultural background. The language storage unit can also select an analysis method based on the cultural background. This makes it possible to select an analysis method taking into account the cultural background of the language. Some or all of the above-mentioned processing in the language storage unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the language storage unit can input cultural background data of the language into the generation AI and have the generation AI select an analysis method.

[0095] The teaching material generation unit can estimate the user's emotions and adjust the content of the teaching material based on the estimated user emotions. The teaching material generation unit, for example, estimates the user's emotions and adjusts the content of the teaching material based on the estimated user emotions. For example, if the user is emotional, the teaching material generation unit generates teaching material that is likely to evoke emotion. Furthermore, if the user is excited, the teaching material generation unit can generate teaching material that is likely to evoke excitement. Furthermore, if the user is relaxed, the teaching material generation unit can generate teaching material that is likely to evoke relaxation. This allows the content of the teaching material to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the teaching material generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the teaching material generation unit can input user emotion data into the generation AI and cause the generation AI to adjust the content of the teaching material.

[0096] The teaching material generation unit can adjust the difficulty of the teaching material based on the learner's stage when generating the teaching material. The teaching material generation unit adjusts the difficulty of the teaching material based on, for example, the learner's stage. For example, the teaching material generation unit generates teaching material for beginners. The teaching material generation unit can also generate teaching material for intermediate learners. The teaching material generation unit can also generate teaching material for advanced learners. This makes it possible to adjust the difficulty of the teaching material based on the learner's stage. Some or all of the above-mentioned processing in the teaching material generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the teaching material generation unit can input learner's stage data into the generation AI and cause the generation AI to adjust the difficulty of the teaching material.

[0097] The teaching material generation unit can apply different teaching material generation algorithms depending on the characteristics of the language when generating teaching materials. The teaching material generation unit applies different teaching material generation algorithms depending on, for example, the characteristics of the language. For example, the teaching material generation unit selects a teaching material generation algorithm based on the grammatical characteristics of the language. The teaching material generation unit can also select a teaching material generation algorithm based on the pronunciation characteristics of the language. The teaching material generation unit can also select a teaching material generation algorithm based on the lexical characteristics of the language. This makes it possible to apply different teaching material generation algorithms depending on the characteristics of the language. Some or all of the above-mentioned processing in the teaching material generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the teaching material generation unit can input language characteristic data into the generation AI and cause the generation AI to apply an appropriate teaching material generation algorithm.

[0098] The teaching material generation unit can improve the accuracy of the teaching materials by referring to past learning data when generating the teaching materials. The teaching material generation unit, for example, improves the accuracy of the teaching materials by referring to past learning data. For example, the teaching material generation unit improves the accuracy of the teaching materials by referring to past learning data. The teaching material generation unit can also select a teaching material method by referring to past learning data. The teaching material generation unit can also determine the priority of the teaching materials by referring to past learning data. This makes it possible to improve the accuracy of the teaching materials by referring to past learning data. Some or all of the above-mentioned processing in the teaching material generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the teaching material generation unit can input past learning data into the generation AI and cause the generation AI to improve the accuracy of the teaching materials.

[0099] The teaching material generation unit can estimate the user's emotions and adjust the display method of the teaching material based on the estimated user emotions. For example, the teaching material generation unit estimates the user's emotions and adjusts the display method of the teaching material based on the estimated user emotions. For example, when the user is emotional, the teaching material generation unit displays teaching materials that are likely to evoke emotion in a larger size. Furthermore, when the user is excited, the teaching material generation unit can dynamically display teaching materials that are likely to evoke excitement. Furthermore, when the user is relaxed, the teaching material generation unit can statically display teaching materials that are likely to evoke relaxation. This allows the display method of the teaching material to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the teaching material generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the teaching material generation unit can input user emotion data into the generation AI and cause the generation AI to adjust the display method of the teaching material.

[0100] The teaching material generation unit can customize the content of the teaching material taking into account the geographical distribution of learners when generating teaching material. The teaching material generation unit customizes the content of the teaching material, for example, taking into account the geographical distribution of learners. For example, the teaching material generation unit generates teaching material specialized for the learner's region. The teaching material generation unit can also generate teaching material based on the culture of the learner's region. The teaching material generation unit can also generate teaching material based on the linguistic characteristics of the learner's region. This makes it possible to customize the content of the teaching material taking into account the geographical distribution of learners. Some or all of the above-mentioned processing in the teaching material generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the teaching material generation unit can input learners' geographical distribution data into the generation AI and cause the generation AI to customize the content of the teaching material.

[0101] The teaching material generation unit can improve the accuracy of the teaching materials by referring to the learner's related literature when generating the teaching materials. The teaching material generation unit, for example, improves the accuracy of the teaching materials by referring to the learner's related literature. For example, the teaching material generation unit improves the accuracy of the teaching materials by referring to the learner's related literature. The teaching material generation unit can also select a teaching material method by referring to the learner's related literature. The teaching material generation unit can also determine the priority of the teaching materials by referring to the learner's related literature. This makes it possible to improve the accuracy of the teaching materials by referring to the learner's related literature. Some or all of the above-mentioned processing in the teaching material generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the teaching material generation unit can input the learner's related literature data into the generation AI and cause the generation AI to improve the accuracy of the teaching materials.

[0102] The teaching material generation unit can select a teaching material technique based on the learner's cultural background when generating teaching materials. The teaching material generation unit selects a teaching material technique based on, for example, the learner's cultural background. For example, the teaching material generation unit selects a teaching material generation algorithm based on the learner's cultural background. The teaching material generation unit can also determine the priority of teaching materials based on the learner's cultural background. The teaching material generation unit can also select a teaching material technique based on the learner's cultural background. This makes it possible to select a teaching material technique taking the learner's cultural background into consideration. Some or all of the above-mentioned processing in the teaching material generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the teaching material generation unit can input the learner's cultural background data into the generation AI and cause the generation AI to select a teaching material technique.

[0103] The interactive teaching material providing unit can estimate the user's emotions and adjust the method of providing the interactive teaching materials based on the estimated user emotions. The interactive teaching material providing unit, for example, estimates the user's emotions and adjusts the method of providing the interactive teaching materials based on the estimated user emotions. For example, if the user is emotional, the interactive teaching material providing unit provides interactive teaching materials that are likely to evoke emotion. Furthermore, if the user is excited, the interactive teaching material providing unit can provide interactive teaching materials that are likely to evoke excitement. Furthermore, if the user is relaxed, the interactive teaching material providing unit can provide interactive teaching materials that are likely to evoke relaxation. This makes it possible to adjust the method of providing the interactive teaching materials based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the interactive teaching material providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the interactive teaching material providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the method of providing the interactive teaching materials.

[0104] When providing interactive teaching material, the interactive teaching material providing unit can select an appropriate delivery method by referring to the learner's past learning history. The interactive teaching material providing unit, for example, selects the optimal delivery method by referring to the learner's past learning history. For example, the interactive teaching material providing unit provides optimal interactive teaching material by referring to the learner's past learning history. The interactive teaching material providing unit can also select a delivery method for the interactive teaching material by referring to the learner's past learning history. The interactive teaching material providing unit can also determine the priority of the interactive teaching material by referring to the learner's past learning history. This makes it possible to select the optimal delivery method by referring to the learner's past learning history. Some or all of the above-mentioned processing in the interactive teaching material providing unit may be performed using or without the generation AI. For example, the interactive teaching material providing unit can input the learner's past learning history data into the generation AI and have the generation AI select the delivery method.

[0105] When providing interactive teaching materials, the interactive teaching material providing unit can customize the content to be provided based on the learner's current learning situation. The interactive teaching material providing unit customizes the content to be provided based on, for example, the learner's current learning situation. For example, the interactive teaching material providing unit refers to the learner's current learning situation and provides optimal interactive teaching materials. The interactive teaching material providing unit can also refer to the learner's current learning situation and select a method for providing the interactive teaching materials. The interactive teaching material providing unit can also refer to the learner's current learning situation and determine the priority of the interactive teaching materials. This makes it possible to customize the content to be provided based on the learner's current learning situation. Some or all of the above-mentioned processing in the interactive teaching material providing unit may be performed using or without the generation AI. For example, the interactive teaching material providing unit can input data on the learner's current learning situation into the generation AI and cause the generation AI to customize the content to be provided.

[0106] The interactive teaching material providing unit can estimate the user's emotions and adjust the operation procedures of the interactive teaching material based on the estimated user emotions. The interactive teaching material providing unit, for example, estimates the user's emotions and adjusts the operation procedures of the interactive teaching material based on the estimated user emotions. For example, if the user is emotional, the interactive teaching material providing unit provides operation procedures that are likely to cause the user to be emotional. Furthermore, if the user is excited, the interactive teaching material providing unit can provide operation procedures that are likely to cause the user to be excited. Furthermore, if the user is relaxed, the interactive teaching material providing unit can provide operation procedures that are likely to cause the user to be relaxed. This makes it possible to adjust the operation procedures of the interactive teaching material based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the interactive teaching material providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the interactive teaching material providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the operation procedures.

[0107] When providing interactive teaching materials, the interactive teaching material providing unit can select an appropriate delivery method based on the learner's device information. The interactive teaching material providing unit selects the optimal delivery method, for example, by taking into account the learner's device information. For example, if the learner is using a smartphone, the interactive teaching material providing unit can provide interactive teaching materials that fit the screen size. Furthermore, if the learner is using a tablet, the interactive teaching material providing unit can also provide interactive teaching materials optimized for a large screen. Furthermore, if the learner is using a smartwatch, the interactive teaching material providing unit can also provide interactive teaching materials that are concise and highly visible. This makes it possible to select the optimal delivery method by taking into account the learner's device information. Some or all of the above-mentioned processing in the interactive teaching material providing unit may be performed using or without the generation AI. For example, the interactive teaching material providing unit can input the learner's device information into the generation AI and have the generation AI select the delivery method.

[0108] When providing interactive teaching materials, the interactive teaching material providing unit can make the provided content multilingual according to the learner's language setting. The interactive teaching material providing unit makes the provided content multilingual according to, for example, the learner's language setting. For example, the interactive teaching material providing unit automatically sets the language of the interactive teaching material based on the language setting of the learner's device. The interactive teaching material providing unit can also provide a language switching function when a learner uses multiple languages. The interactive teaching material providing unit can also provide the interactive teaching material in a specific language when the learner selects that language. This makes it possible to make the provided content multilingual according to the learner's language setting. Some or all of the above-mentioned processing in the interactive teaching material providing unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the interactive teaching material providing unit can input the learner's language setting data into the generation AI and cause the generation AI to make the provided content multilingual.

[0109] When providing interactive teaching materials, the interactive teaching material providing unit can analyze the learner's social media activities and provide related information. The interactive teaching material providing unit, for example, analyzes the learner's social media activities and provides related information. For example, the interactive teaching material providing unit can provide information about places where the learner has checked in on social media. The interactive teaching material providing unit can also analyze the learner's social media posts and provide information about related tourist spots and stores. The interactive teaching material providing unit can also provide information about related places and events by referring to the activities of the learner's friends on social media. In this way, the learner's social media activities can be analyzed and related information can be provided. Some or all of the above-mentioned processing in the interactive teaching material providing unit may be performed using or without the generation AI. For example, the interactive teaching material providing unit can input the learner's social media activity data into the generation AI and cause the generation AI to provide related information.

[0110] The cultural content generation unit can estimate the user's emotions and adjust the cultural content generation method based on the estimated user emotions. The cultural content generation unit, for example, estimates the user's emotions and adjusts the cultural content generation method based on the estimated user emotions. For example, if the user is emotional, the cultural content generation unit generates cultural content that is likely to evoke emotion. Furthermore, if the user is excited, the cultural content generation unit can generate cultural content that is likely to evoke excitement. Furthermore, if the user is relaxed, the cultural content generation unit can generate cultural content that is likely to evoke relaxation. This allows the cultural content generation method to be adjusted based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the cultural content generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the cultural content generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the generation method.

[0111] The cultural content generation unit can adjust the level of detail of the cultural content generated based on the importance of the content when generating the cultural content. The cultural content generation unit adjusts the level of detail of the cultural content generated based on, for example, the importance of the content. For example, the cultural content generation unit generates highly important cultural content in detail. The cultural content generation unit can also generate less important cultural content in a simplified manner. The cultural content generation unit can also generate moderately important cultural content in an appropriate manner. This allows the level of detail of the cultural content generated to be adjusted based on the importance of the content. Some or all of the above-described processing in the cultural content generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the cultural content generation unit can input content importance data into the generation AI and cause the generation AI to adjust the level of detail of the content.

[0112] The cultural content generation unit can apply different generation algorithms depending on the content category when generating cultural content. The cultural content generation unit applies different generation algorithms depending on the content category, for example. For example, the cultural content generation unit selects a generation algorithm based on the story category. The cultural content generation unit can also select a generation algorithm based on the poetry category. The cultural content generation unit can also select a generation algorithm based on the music category. This makes it possible to apply different generation algorithms depending on the content category. Some or all of the above-mentioned processing in the cultural content generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the cultural content generation unit can input content category data into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0113] When generating cultural content, the cultural content generation unit can improve the accuracy of generation by referring to past content data. The cultural content generation unit, for example, improves the accuracy of generation by referring to past content data. For example, the cultural content generation unit improves the accuracy of generation by referring to past content data. The cultural content generation unit can also select a generation method by referring to past content data. The cultural content generation unit can also determine a generation priority by referring to past content data. This makes it possible to improve the accuracy of generation by referring to past content data. Some or all of the above-described processing in the cultural content generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the cultural content generation unit can input past content data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0114] The cultural content generation unit can estimate the user's emotions and adjust the display method of the cultural content based on the estimated user emotions. For example, the cultural content generation unit can estimate the user's emotions and adjust the display method of the cultural content based on the estimated user emotions. For example, if the user is emotional, the cultural content generation unit can enlarge cultural content that is likely to evoke emotion. Furthermore, if the user is excited, the cultural content generation unit can dynamically display cultural content that is likely to evoke excitement. Furthermore, if the user is relaxed, the cultural content generation unit can statically display cultural content that is likely to evoke relaxation. This allows the display method of the cultural content to be adjusted based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the cultural content generation unit can be performed using the generation AI, or can be performed without the generation AI. For example, the cultural content generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0115] When generating cultural content, the cultural content generation unit can determine generation priorities based on the geographical distribution of the content. The cultural content generation unit, for example, determines generation priorities taking into account the geographical distribution of the content. For example, the cultural content generation unit prioritizes generating geographically important cultural content. The cultural content generation unit can also prioritize generating geographically rare cultural content. The cultural content generation unit can also prioritize generating cultural content that is geographically distributed over a wide area. This makes it possible to determine generation priorities taking into account the geographical distribution of the content. Some or all of the above-described processing in the cultural content generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the cultural content generation unit can input geographical distribution data of the content into the generation AI and have the generation AI determine the generation priorities.

[0116] When generating cultural content, the cultural content generation unit can improve the accuracy of generation by referring to literature related to the content. The cultural content generation unit, for example, improves the accuracy of generation by referring to literature related to the content. For example, the cultural content generation unit improves the accuracy of generation by referring to literature related to the content. The cultural content generation unit can also select a generation method by referring to literature related to the content. The cultural content generation unit can also determine a generation priority by referring to literature related to the content. This makes it possible to improve the accuracy of generation by referring to literature related to the content. Some or all of the above-mentioned processing in the cultural content generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the cultural content generation unit can input literature data related to the content into the generation AI and cause the generation AI to improve the accuracy of generation.

[0117] When generating cultural content, the cultural content generation unit can select a generation method based on the cultural value of the content. The cultural content generation unit selects a generation method based on, for example, the cultural value of the content. For example, the cultural content generation unit selects a generation algorithm based on the cultural value. The cultural content generation unit can also determine a generation priority based on the cultural value. The cultural content generation unit can also select a generation method based on the cultural value. This makes it possible to select a generation method taking the cultural value of the content into consideration. Some or all of the above-mentioned processing in the cultural content generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the cultural content generation unit can input cultural value data of the content into the generation AI and have the generation AI select a generation method. === Hard Collateral 1-1 === Each of the above-mentioned elements, including the digital archiving unit, language storage unit, learning material generation unit, interactive learning material provision unit, and cultural content generation unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the digital archiving unit can digitize cultural materials using the camera 42 or microphone 38B of the smart device 14. For example, the language storage unit can be implemented by the specific processing unit 290 of the data processing device 12, which analyzes digitized audio data and digitizes grammar and vocabulary. For example, the learning material generation unit can be implemented by the specific processing unit 290 of the data processing device 12, which automatically generates learning materials appropriate to the learner's level using a generation AI. For example, the interactive learning material provision unit can be implemented by the control unit 46A of the smart device 14, which provides the generated learning materials through an online platform. For example, the cultural content generation unit can be implemented by the specific processing unit 290 of the data processing device 12, which provides content in a modern media format based on the digitized materials. === Hard Collateral 1-2 === Each of the above-mentioned elements, including the digital archiving unit, language storage unit, teaching material generation unit, interactive teaching material provision unit, and cultural content generation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the digital archiving unit can digitize cultural materials using the camera 42 and microphone 238 of the smart glasses 214. For example, the language storage unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes digitized audio data to digitize grammar and vocabulary. For example, the teaching material generation unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically generates teaching materials appropriate to the learner's level using a generation AI. For example, the interactive teaching material provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated teaching materials through an online platform. For example, the cultural content generation unit is implemented by the specific processing unit 290 of the data processing device 12 and provides content in a modern media format based on the digitized materials. === Hard Collateral 1-3 === Each of the multiple elements, including the digital archiving unit, language storage unit, teaching material generation unit, interactive teaching material provision unit, and cultural content generation unit, described above, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the digital archiving unit can digitize cultural materials using the camera 42 and microphone 238 of the headset terminal 314. For example, the language storage unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes digitized audio data to digitize grammar and vocabulary. For example, the teaching material generation unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically generates teaching materials appropriate to the learner's level using a generation AI. For example, the interactive teaching material provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated teaching materials through an online platform. For example, the cultural content generation unit is implemented by the specific processing unit 290 of the data processing device 12 and provides content in a modern media format based on the digitized materials. === Hard Collateral 1-4 === Each of the multiple elements, including the digital archiving unit, language storage unit, teaching material generation unit, interactive teaching material provision unit, and cultural content generation unit, described above, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the digital archiving unit can digitize cultural materials using the camera 42 and microphone 238 of the robot 414. For example, the language storage unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes digitized audio data to digitize grammar and vocabulary. For example, the teaching material generation unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically generates teaching materials appropriate to the learner's level using a generation AI. For example, the interactive teaching material provision unit is implemented by the control unit 46A of the robot 414 and provides the generated teaching materials through an online platform. For example, the cultural content generation unit is implemented by the specific processing unit 290 of the data processing device 12 and provides content in a modern media format based on the digitized materials.

[0118] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0119] The cultural preservation system can further include an emotion estimation unit that estimates the user's emotion and determines the priority for digitizing cultural materials based on the estimated emotion. For example, if the user is emotional, materials that are likely to evoke emotion can be digitized with priority. Also, if the user is excited, materials that are likely to evoke excitement can be digitized with priority. Furthermore, if the user is relaxed, materials that are likely to evoke relaxation can be digitized with priority. In this way, the priority for materials to be digitized can be determined based on the user's emotion.

[0120] The Digital Archiving Department can automatically detect the deterioration state of materials and select the appropriate digitization method. For example, if the paper quality of a material has deteriorated, it can be digitized by increasing the scan resolution. Also, if the image of the material has deteriorated, it can be digitized using image correction technology. Furthermore, if the audio of the material has deteriorated, it can be digitized using noise reduction technology. This makes it possible to automatically detect the deterioration state of materials and select the optimal digitization method.

[0121] The language storage unit can estimate the user's emotions and adjust the analysis method of language data based on the estimated emotions. For example, if the user is emotional, language data that is likely to cause emotion can be analyzed preferentially. Also, if the user is excited, language data that is likely to cause excitement can be analyzed preferentially. Furthermore, if the user is relaxed, language data that is likely to cause relaxation can be analyzed preferentially. In this way, the analysis method of language data can be adjusted based on the user's emotions.

[0122] The learning material generation unit can adjust the difficulty of the learning material based on the learner's level. For example, learning material for beginners can be generated. Learning material for intermediate learners can also be generated. Learning material for advanced learners can also be generated. This makes it possible to adjust the difficulty of the learning material based on the learner's level.

[0123] The interactive learning material providing unit can estimate the user's emotions and adjust the method of providing the interactive learning material based on the estimated emotions. For example, if the user is emotional, interactive learning material that is likely to cause emotion can be provided. Also, if the user is excited, interactive learning material that is likely to cause excitement can be provided. Furthermore, if the user is relaxed, interactive learning material that is likely to cause relaxation can be provided. In this way, the method of providing the interactive learning material can be adjusted based on the user's emotions.

[0124] The cultural content generation unit can apply different generation algorithms depending on the category of the content. For example, a generation algorithm can be selected based on the category of a story. Alternatively, a generation algorithm can be selected based on the category of a poem. Furthermore, a generation algorithm can be selected based on the category of a music. In this way, different generation algorithms can be applied depending on the category of the content.

[0125] The Digital Archiving Department can determine the priority of digitization taking into account the geographical distribution of materials. For example, geographically important materials can be digitized first. Geographically rare materials can also be digitized first. Furthermore, materials that are geographically distributed over a wide area can be digitized first. In this way, the priority of digitization can be determined taking into account the geographical distribution of materials.

[0126] The language storage unit can improve the accuracy of analysis by referring to the historical evolution of the language. For example, the accuracy of analysis can be improved by referring to the historical evolution of the language. The language storage unit can also select an analysis method by referring to the historical evolution of the language. Furthermore, the accuracy of analysis can be improved by referring to the historical evolution of the language.

[0127] The teaching material generation unit can estimate the user's emotions and adjust the content of the teaching material based on the estimated emotions. For example, if the user is emotional, teaching material that is likely to evoke emotion can be generated. Also, if the user is excited, teaching material that is likely to evoke excitement can be generated. Furthermore, if the user is relaxed, teaching material that is likely to evoke relaxation can be generated. In this way, the content of the teaching material can be adjusted based on the user's emotions.

[0128] The cultural content generation unit can estimate the user's emotions and adjust the display method of the cultural content based on the estimated emotions. For example, if the user is emotional, cultural content that is likely to evoke emotion can be displayed larger. Also, if the user is excited, cultural content that is likely to evoke excitement can be dynamically displayed. Furthermore, if the user is relaxed, cultural content that is likely to evoke relaxation can be statically displayed. In this way, the display method of the cultural content can be adjusted based on the user's emotions.

[0129] The processing flow of the second embodiment will be briefly explained below.

[0130] Step 1: The Digital Archiving Department digitizes cultural materials. Cultural materials include audio, video, and text. For example, audio materials are digitized as high-quality audio files, and video materials are digitized as high-resolution video files. Text materials can also be digitized using scanning technology. Specifically, videos of traditional songs and dances are digitized and preserved. Step 2: The Language Preservation Department analyzes the materials digitized by the Digital Archiving Department and digitizes the language's grammar and vocabulary. For example, it analyzes audio data from a language with a small number of speakers and digitizes grammar rules and word lists. It can also create a pronunciation guide for the language. Specifically, it uses generative AI to extract grammar rules from audio data and digitize them. Step 3: The teaching material generation unit generates teaching materials for language learning based on the language data digitized by the language storage unit. For example, it can generate interactive teaching materials for learning language pronunciation and grammar, and can automatically generate teaching materials appropriate for the learner's level. Specifically, it generates pronunciation practice materials for beginners. Step 4: The interactive teaching material provider provides the teaching materials generated by the teaching material generator. For example, the teaching materials can be provided to educational institutions or communities, or through an online platform. Specifically, the generated teaching materials can be published on a website so that learners can access them. Step 5: The Cultural Content Generation Unit generates cultural content based on the materials digitized by the Digital Archiving Unit. For example, cultural content such as traditional stories, poetry, and music can be generated, and the generation AI can be used to provide the content in modern media formats. Specifically, an animation based on a traditional story can be created.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0136] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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 AI 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.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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 AI 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.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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 AI 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.

[0182] 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.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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).

[0188] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] [Explanation of symbols]

[0203] 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. the Digital Archiving Department, which digitizes cultural materials; a language preservation unit that analyzes the materials digitized by the digital archiving unit and digitizes the grammar and vocabulary of the language; a learning material generation unit that generates learning materials for language learning based on the language data digitized by the language storage unit; an interactive teaching material providing unit that provides the teaching material generated by the teaching material generating unit; a cultural content generation unit that generates cultural content based on the materials digitized by the digital archiving unit. A system characterized by:

2. The digital archiving unit Digitizing cultural materials in various formats, including audio, video, and text 2. The system of claim 1.

3. The language storage unit Analyzing speech data from languages ​​with few speakers and digitizing their grammar and vocabulary 2. The system of claim 1.

4. The teaching material generation unit Generate interactive learning materials for learning language pronunciation and grammar 2. The system of claim 1.

5. The interactive teaching material providing unit Providing generated educational materials to educational institutions and communities 2. The system of claim 1.

6. The cultural content generation unit Producing cultural content in traditional stories, poetry, and music and presenting it in modern media formats 2. The system of claim 1.

7. The digital archiving unit Estimate user emotions and prioritize materials to be digitized based on the estimated user emotions.

2. The system of claim 1.

8. The digital archiving unit When digitally archiving, automatically detect the deterioration state of materials and select the appropriate digitization method 2. The system of claim 1.

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