System

Abstract

An object of the system according to the embodiment is to efficiently store a technology and a culture of an traditional craft and to inherit the technology and the culture to a subsequent generation.SOLUTION: A system includes a collection unit, an analysis unit, a generation unit, and a storage unit. The collection unit collects data of old documents, video materials, and interviews of craftsmen. The analysis unit analyzes the data collected by the collection unit and extracts key points of technology and culture. The generation unit generates a moving image based on the main points extracted by the analysis unit. The storage unit stores the moving image generated by the generation unit as an archive.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] With conventional technology, it was difficult to pass on traditional craft techniques and culture, and there were concerns about a lack of successors and the dispersion of information.

[0005] The system according to the embodiment aims to efficiently preserve traditional craft techniques and culture and pass them on to future generations. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a storage unit. The collection unit collects data from old documents, video materials, and interviews with craftsmen. The analysis unit analyzes the data collected by the collection unit and extracts key points of technology and culture. The generation unit generates videos based on the key points extracted by the analysis unit. The storage unit stores the videos generated by the generation unit as an archive. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently preserve traditional craft techniques and culture and pass them on to future generations. [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 uses AI to analyze data and documents related to traditional craft techniques and culture, generating easy-to-understand videos based on the results and storing them as an archive on an online platform. This system was developed to address the lack of successors and the difficulty of passing on traditional crafts. The system collects old documents, video materials, and interviews with artisans, and then uses AI to analyze this data and extract key points of the techniques and culture. Next, videos are generated based on the extracted key points and stored as an archive. Furthermore, a dedicated app is provided with a viewing section for viewing videos, a registration section for registering potential successors, and a matching section for matching with artisans. For example, the system collects old documents, video materials, and interviews with artisans. Next, AI analyzes this data and extracts key points of the techniques and culture. For example, it extracts technical procedures and historical background from old documents and analyzes the actual movements of the techniques from video materials. Next, it generates videos in a visually easy-to-understand format based on the extracted key points. For example, it generates videos that explain the technical procedures step by step or videos that introduce the historical background. The generated videos are stored in an archive preservation unit and made accessible on an online platform. This allows the system to widely preserve traditional craft techniques and culture and pass them on to future generations. This allows the system to widely preserve valuable information and techniques related to traditional craft techniques and culture and pass them on to future generations. For example, the system can help discover people who want to learn traditional crafts and find successors, thereby passing on a rich traditional craft culture to the future.

[0029] A traditional craft preservation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a storage unit. The collection unit collects old documents, video materials, and artisan interview data. For example, the collection unit may collect documents and video materials in the form of books, papers, films, documentaries, etc. The collection unit may also collect artisan interview data in the form of audio data, video data, text data, etc. The collection unit may also collect document data from digital archives and library databases. The analysis unit analyzes the data collected by the collection unit to extract key points of techniques and culture. For example, the analysis unit may use text mining technology to extract technical procedures and historical background from the document data. The analysis unit may also use video analysis technology to analyze the actual movements of techniques from video data. The analysis unit may also use audio analysis technology to extract key points of artisans' experiences and techniques from interview data. The generation unit generates a video based on the key points extracted by the analysis unit. For example, the generation unit may generate a video that explains the steps of a technique step by step. The generation unit may also generate a video that introduces the historical background. Furthermore, the generation unit can use animation or live-action footage to generate videos in a visually easy-to-understand format. The storage unit stores the videos generated by the generation unit as an archive. For example, the storage unit stores the generated videos as a digital archive and provides them in an accessible form on an online platform. This allows the traditional craft preservation system according to the embodiment to efficiently collect, analyze, generate, and store data related to traditional craft techniques and culture.

[0030] Furthermore, the traditional craft preservation system includes a viewing unit for viewing videos, a registration unit for registering as a successor, and a matching unit for matching with artisans through a dedicated app. The viewing unit provides a function for users to watch videos. For example, the viewing unit can provide a function for streaming viewing or downloading viewing. The registration unit provides a function for registering as a successor. For example, the registration unit supports the user in entering necessary information and the procedure for registering as a successor. The matching unit provides a function for matching with artisans. For example, the matching unit matches with an appropriate artisan based on the user's skills and interests. This allows users to watch videos, register as a successor, and be matched with artisans.

[0031] The collection unit can evaluate the reliability of data at the time of collection and prioritize collection of highly reliable data. For example, the collection unit evaluates whether the source of the data is from a reliable academic institution or expert and prioritizes collection of highly reliable data. The collection unit can also check the publication date or update date of the data and prioritize collection of the latest information. Furthermore, the collection unit can check whether the content of the data matches other highly reliable data and prioritize collection of highly reliable data. This allows highly reliable data to be collected preferentially.

[0032] The collection department can select the most appropriate collection method depending on the type of data when collecting the data. For example, in the case of document data, the collection department collects data from digital archives or library databases. In the case of video data, the collection department can also collect data from video material providers or online video platforms. Furthermore, in the case of interview data, the collection department can collect data by conducting direct interviews with craftsmen or experts. This makes it possible to select the most appropriate collection method depending on the type of data.

[0033] When collecting data, the collection department can prioritize collecting the latest information, taking into account the frequency of data updates. For example, the collection department collects the latest literature and video materials from a database that is updated regularly. The collection department can also prioritize collecting the latest interviews with craftsmen and experts. Furthermore, the collection department can prioritize collecting data that includes information on the latest technology and culture. This allows the collection of the latest information to be prioritized.

[0034] When collecting data, the collection department can take geographical distribution into consideration and collect data for each region in a balanced manner. For example, the collection department can collect literature and video materials related to traditional crafts in each region in a balanced manner. The collection department can also collect interviews with artisans in each region in a balanced manner. Furthermore, the collection department can collect data related to the techniques and culture of each region in a balanced manner. This allows for the collection of data for each region in a balanced manner.

[0035] The collection unit can select the most appropriate collection method depending on the format of the data when collecting the data. For example, in the case of text data, the collection unit collects the data from digital archives or library databases. In addition, in the case of audio data, the collection unit can collect the data from providers of audio materials or online audio platforms. Furthermore, in the case of image data, the collection unit can collect the data from providers of image materials or online image platforms. This makes it possible to select the most appropriate collection method depending on the format of the data.

[0036] The collection unit can evaluate the relevance of data at the time of collection and prioritize collection of highly relevant data. For example, the collection unit checks whether the data to be collected matches other highly reliable data and prioritizes collection of highly relevant data. The collection unit can also evaluate whether the data to be collected is related to a specific technology or culture and prioritize collection of highly relevant data. Furthermore, the collection unit can evaluate whether the data to be collected is related to the user's interests and concerns and prioritize collection of highly relevant data. This allows highly relevant data to be collected preferentially.

[0037] During analysis, the analysis unit can classify the content of the data and extract key points of technology and culture. For example, the analysis unit extracts technical procedures and historical background from document data. The analysis unit can also analyze the actual movements of technology from video data and extract key points. Furthermore, the analysis unit can extract key points of craftsmen's experiences and techniques from interview data. This makes it possible to classify the content of the data and extract key points of technology and culture.

[0038] During analysis, the analysis unit can apply an analysis method that takes into account the interrelationships between data to gain a deeper understanding. For example, the analysis unit can combine and analyze document data and video data to understand the procedures and actual movements of the technology. The analysis unit can also combine and analyze interview data and document data to understand the historical background of the technology and the experience of the craftsman. Furthermore, the analysis unit can combine and analyze video data and interview data to understand the actual movements of the technology and the explanations of the craftsman. This makes it possible to apply an analysis method that takes into account the interrelationships between data to gain a deeper understanding.

[0039] During analysis, the analysis unit can evaluate the reliability of the data and prioritize analysis of highly reliable data. For example, the analysis unit evaluates whether the source of the data is from a reliable academic institution or expert, and prioritizes analysis of highly reliable data. The analysis unit can also check the publication date and update date of the data, and prioritize analysis of the latest information. Furthermore, the analysis unit can check whether the content of the data matches other highly reliable data, and prioritize analysis of highly reliable data. This allows highly reliable data to be prioritized in analysis.

[0040] During analysis, the analysis unit can apply different analysis methods depending on the type of data. For example, in the case of document data, the analysis unit applies text analysis methods to extract technical procedures and historical background. In addition, in the case of video data, the analysis unit can apply video analysis methods to analyze the actual movements of the technology. Furthermore, in the case of interview data, the analysis unit can apply audio analysis methods to extract key points of the craftsman's experience and skills. This makes it possible to apply different analysis methods depending on the type of data.

[0041] During analysis, the analysis unit can prioritize analyzing the latest information, taking into account the frequency of data updates. For example, the analysis unit analyzes the latest literature and video materials from a database that is updated regularly. The analysis unit can also prioritize analyzing the latest interviews with craftsmen and experts. Furthermore, the analysis unit can prioritize analyzing data that includes information on the latest technology and culture. This allows the latest information to be analyzed with priority.

[0042] During analysis, the analysis unit can evaluate the relevance of the data and prioritize analysis of highly relevant data. For example, the analysis unit checks whether the data to be analyzed matches other highly reliable data and prioritizes analysis of highly relevant data. The analysis unit can also evaluate whether the data to be analyzed is related to a specific technology or culture and prioritize analysis of highly relevant data. Furthermore, the analysis unit can evaluate whether the data to be analyzed is related to the user's interests and concerns and prioritize analysis of highly relevant data. This allows for prioritized analysis of highly relevant data.

[0043] During generation, the generation unit can generate a video that explains the steps of a technique step by step. For example, the generation unit generates a video that explains the steps of a technique in detail and provides it in a format that is visually easy to understand. The generation unit can also generate a video that explains the steps of a technique step by step and provide it in a format that is easy for even beginners to understand. Furthermore, the generation unit can generate a video that explains the steps of a technique using actual movements and provide it in a format that is visually easy to understand. In this way, a video that explains the steps of a technique step by step can be generated.

[0044] The generation unit can generate a video introducing the historical background during generation. For example, the generation unit generates a video that explains the historical background of a technology in detail and provides it in a format that is visually easy to understand. The generation unit can also generate a video that explains the historical background of a technology step by step and provides it in a format that is easy for even beginners to understand. Furthermore, the generation unit can generate a video that explains the historical background of a technology using actual footage and photos and provides it in a format that is visually easy to understand. In this way, a video introducing the historical background can be generated.

[0045] During generation, the generation unit can select an optimal method for generating a video in a format that is visually easy to understand. For example, the generation unit generates a video that explains a technical procedure using animation and provides it in a format that is visually easy to understand. The generation unit can also generate a video that explains a technical procedure using actual footage and provide it in a format that is visually easy to understand. Furthermore, the generation unit can generate a video that explains a technical procedure using a 3D model and provide it in a format that is visually easy to understand. This makes it possible to select an optimal method for generating a video in a format that is visually easy to understand.

[0046] The generation unit can apply different video generation methods depending on the type of data during generation. For example, in the case of literature data, the generation unit generates a video that combines text and images. In the case of video data, the generation unit can also edit the video to generate a video that is visually easy to understand. Furthermore, in the case of interview data, the generation unit can also generate a video that combines audio and video. This makes it possible to apply different video generation methods depending on the type of data.

[0047] The generation unit can generate videos that reflect the latest information, taking into account the frequency of data updates during generation. For example, the generation unit generates videos that reflect the latest literature and video materials from a database that is updated regularly. The generation unit can also generate videos that reflect the latest interviews with craftsmen and experts. Furthermore, the generation unit can generate videos that include information on the latest technology and culture. This makes it possible to generate videos that reflect the latest information.

[0048] The generation unit can evaluate the relevance of data at the time of generation and preferentially reflect highly relevant information in the video. For example, the generation unit checks whether the video to be generated matches other highly reliable data and preferentially reflects highly relevant information. The generation unit can also evaluate whether the video to be generated is related to a specific technology or culture and preferentially reflect highly relevant information. Furthermore, the generation unit can evaluate whether the video to be generated is related to the user's interests and concerns and preferentially reflect highly relevant information. This allows highly relevant information to be preferentially reflected in the video.

[0049] The storage unit can assign metadata to videos when storing them, thereby improving searchability. For example, the storage unit assigns appropriate keywords as metadata based on the content of the video. The storage unit can also assign the creation date or update date of the video as metadata, making it easier to search for the latest information. Furthermore, the storage unit can assign appropriate categories as metadata based on the technology or culture related to the video. In this way, assigning metadata to videos can improve searchability.

[0050] The storage unit can optimize the format of the video when storing it and store it in a format suitable for long-term storage. The storage unit can, for example, compress the format of the video to improve storage efficiency. The storage unit can also standardize the format of the video and store it in a format suitable for long-term storage. Furthermore, the storage unit can convert the format of the video and store it in a format that can be played on different devices. In this way, the video format can be optimized and stored in a format suitable for long-term storage.

[0051] The storage unit periodically backs up the videos when storing them, thereby ensuring the safety of the data. For example, the storage unit periodically backs up the videos to ensure the safety of the data. The storage unit can also store the backups of the videos in different locations to ensure the safety of the data. Furthermore, the storage unit can store the backups of the videos in cloud storage to ensure the safety of the data. In this way, the safety of the data can be ensured by periodically backing up the videos.

[0052] The storage unit can set access permissions for the video when storing the video, and allow access only to appropriate users. The storage unit can set appropriate access permissions based on, for example, the content of the video. The storage unit can also set appropriate access permissions based on the creator or provider of the video. Furthermore, the storage unit can also set appropriate access permissions based on the technology or culture related to the video. In this way, by setting access permissions for the video, it is possible to allow access only to appropriate users.

[0053] The storage unit can improve storage efficiency by applying a video compression technique when storing the video. For example, the storage unit can compress the video format to improve storage efficiency. The storage unit can also adjust the video resolution to improve storage efficiency. Furthermore, the storage unit can also adjust the video bitrate to improve storage efficiency. In this way, by applying a video compression technique, storage efficiency can be improved.

[0054] The storage unit can link related information of the video when storing the video, thereby providing comprehensive information. For example, the storage unit can link literature related to the content of the video, thereby providing comprehensive information. The storage unit can also link interviews related to the content of the video, thereby providing comprehensive information. Furthermore, the storage unit can link video materials related to the content of the video, thereby providing comprehensive information. In this way, by linking related information of the video, comprehensive information can be provided.

[0055] The viewing unit can recommend related videos by referring to the user's viewing history when viewing. The viewing unit recommends related videos based on, for example, videos the user has viewed in the past. The viewing unit can also recommend videos that the user is likely to be interested in based on the user's viewing history. Furthermore, the viewing unit can analyze the user's viewing history and recommend the most relevant video. This makes it possible to recommend related videos by referring to the user's viewing history.

[0056] The viewing unit can adjust the playback speed of the video during viewing to provide a viewing experience that suits the user's level of understanding. For example, the viewing unit plays the video at a speed that is easy for the user to understand. The viewing unit can also adjust the playback speed so that the user can easily understand detailed information. Furthermore, if the user is in a hurry, the viewing unit can also speed up the playback speed to provide a viewing experience. In this way, by adjusting the playback speed of the video, a viewing experience that suits the user's level of understanding can be provided.

[0057] The viewing unit can provide subtitles and commentary for the video during viewing, thereby supporting viewing in a visually easy-to-understand format. The viewing unit can provide appropriate subtitles, for example, based on the content of the video. The viewing unit can also provide appropriate commentary based on the content of the video. Furthermore, the viewing unit can also provide subtitles and commentary in a visually easy-to-understand format based on the content of the video. In this way, by providing subtitles and commentary for the video, viewing can be supported in a visually easy-to-understand format.

[0058] The viewing unit can provide an optimal viewing format by taking into account device information of the user when viewing. For example, if the user is using a smartphone, the viewing unit can provide a viewing format that matches the screen size. Also, if the user is using a tablet, the viewing unit can provide a viewing format optimized for a large screen. Furthermore, if the user is using a smartwatch, the viewing unit can provide a simple and highly visible viewing format. In this way, the optimal viewing format can be provided by taking into account device information of the user.

[0059] The viewing unit can provide multilingual support for videos in accordance with the user's language settings during viewing. The viewing unit can automatically set subtitles and audio for videos based on the language settings of the user's device, for example. The viewing unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the viewing unit can provide the video in that language. This allows the video to be provided in multiple languages ​​in accordance with the user's language settings.

[0060] The viewing unit can collect user feedback during viewing and reflect it in improving the viewing experience. The viewing unit, for example, provides an interface that allows the user to provide feedback after viewing. The viewing unit can also analyze the user feedback and reflect it in improving the viewing experience. Furthermore, the viewing unit can improve the content and format of the video based on the user feedback. In this way, the collected user feedback can be reflected in improving the viewing experience.

[0061] At the time of registration, the registration unit can suggest the optimal registration method by referring to the user's past activity history. For example, the registration unit preferentially suggests registration methods (email, SNS account, etc.) that the user has used in the past. The registration unit can also suggest the optimal registration category based on the user's past activity history. Furthermore, the registration unit can analyze the user's past activity history and suggest the most efficient registration method. In this way, the optimal registration method can be suggested by referring to the user's past activity history.

[0062] The registration unit can recommend an appropriate registration category based on the user's interests at the time of registration. For example, the registration unit recommends related registration categories based on the field in which the user is interested. Furthermore, if the user has a strong interest in a particular craftsman, the registration unit can also recommend a registration category related to that craftsman. Furthermore, if the user has an interest in a particular skill, the registration unit can also recommend a registration category related to that skill. This makes it possible to recommend appropriate registration categories based on the user's interests.

[0063] The registration unit can improve the registration process by reflecting user feedback at the time of registration. For example, the registration unit provides an interface that allows the user to provide feedback after registration. The registration unit can also analyze the user feedback and reflect it in improving the registration process. Furthermore, the registration unit can improve the registration interface and procedures based on the user feedback. In this way, the registration process can be improved by reflecting user feedback.

[0064] The registration unit can support registration by region, taking into account the geographical information of the user, at the time of registration. For example, the registration unit provides a registration category by region based on the geographical information of the user. The registration unit can also support registration of craftsmen and techniques by region, based on the geographical information of the user. Furthermore, the registration unit can support registration of events and workshops by region, based on the geographical information of the user. In this way, it is possible to support registration by region, taking into account the geographical information of the user.

[0065] At the time of registration, the registration unit can analyze the user's social media activity and provide related registration information. For example, the registration unit can analyze the content posted by the user on social media and provide related registration information. The registration unit can also provide related registration information by referring to the activities of the user's friends on social media. Furthermore, the registration unit can also provide related registration information based on the user's check-in information on social media. In this way, related registration information can be provided by analyzing the user's social media activity.

[0066] The registration unit can provide the optimal registration format during registration by taking into consideration the user's device information. For example, if the user is using a smartphone, the registration unit can provide a registration format that matches the screen size. Furthermore, if the user is using a tablet, the registration unit can also provide a registration format that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the registration unit can also provide a simple and highly visible registration format. This makes it possible to provide the optimal registration format by taking into consideration the user's device information.

[0067] During matching, the matching unit can suggest the optimal matching method by referring to the user's past activity history. For example, the matching unit preferentially suggests matching methods (email, SNS account, etc.) that the user has used in the past. The matching unit can also suggest the optimal matching category from the user's past activity history. Furthermore, the matching unit can analyze the user's past activity history and suggest the most efficient matching method. In this way, the optimal matching method can be suggested by referring to the user's past activity history.

[0068] During matching, the matching unit can recommend appropriate matching candidates based on the user's interests. For example, the matching unit recommends related matching candidates based on the field in which the user is interested. Furthermore, if the user has a strong interest in a particular craftsman, the matching unit can also recommend matching candidates related to that craftsman. Furthermore, if the user has an interest in a particular skill, the matching unit can also recommend matching candidates related to that skill. This makes it possible to recommend appropriate matching candidates based on the user's interests.

[0069] The matching unit can improve the matching process by reflecting user feedback during matching. For example, the matching unit provides an interface that allows the user to provide feedback after matching. The matching unit can also analyze the user feedback and reflect it in improving the matching process. Furthermore, the matching unit can improve the matching interface and procedures based on the user feedback. In this way, the matching process can be improved by reflecting user feedback.

[0070] The matching unit can support matching for each region by taking into account the user's geographical information during matching. For example, the matching unit provides matching candidates for each region based on the user's geographical information. The matching unit can also support matching related to craftsmen and techniques for each region based on the user's geographical information. Furthermore, the matching unit can support matching related to events and workshops for each region based on the user's geographical information. In this way, matching for each region can be supported by taking into account the user's geographical information.

[0071] The matching unit can analyze the user's social media activity during matching and provide related matching information. For example, the matching unit can analyze the content posted by the user on social media and provide related matching information. The matching unit can also provide related matching information by referring to the activity of the user's friends on social media. Furthermore, the matching unit can also provide related matching information based on the user's check-in information on social media. In this way, related matching information can be provided by analyzing the user's social media activity.

[0072] The matching unit can provide an optimal matching format by taking into account the user's device information during matching. For example, if the user is using a smartphone, the matching unit can provide a matching format that suits the screen size. Furthermore, if the user is using a tablet, the matching unit can provide a matching format that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the matching unit can provide a simple and highly visible matching format. In this way, the optimal matching format can be provided by taking into account the user's device information.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The collection unit can evaluate the reliability of data at the time of collection and prioritize collection of highly reliable data. For example, it can evaluate whether the source of the data is from a reliable academic institution or expert and prioritize collection of highly reliable data. It can also check the publication date and update date of the data and prioritize collection of the latest information. Furthermore, it can check whether the content of the data matches other highly reliable data and prioritize collection of highly reliable data. This makes it possible to prioritize collection of highly reliable data.

[0075] During analysis, the analysis unit can apply analytical methods that take into account the interrelationships between data to gain a deeper understanding. For example, document data and video data can be combined and analyzed to understand the procedures and actual movements of a technique. Interview data and document data can also be combined and analyzed to understand the historical background of a technique and the experience of a craftsman. Furthermore, video data and interview data can be combined and analyzed to understand the actual movements of a technique and the artisan's commentary. This makes it possible to apply analytical methods that take into account the interrelationships between data to gain a deeper understanding.

[0076] At the time of generation, the generation unit can select the optimal method for generating a video in a format that is visually easy to understand. For example, a video that explains a technical procedure using animation can be generated and provided in a format that is visually easy to understand. Alternatively, a video that explains a technical procedure using actual footage can be generated and provided in a format that is visually easy to understand. Furthermore, a video that explains a technical procedure using a 3D model can be generated and provided in a format that is visually easy to understand. This makes it possible to select the optimal method for generating a video in a format that is visually easy to understand.

[0077] The storage unit can assign metadata to videos when saving them to improve searchability. For example, appropriate keywords can be assigned as metadata based on the content of the video. The creation date or update date of the video can also be assigned as metadata to make it easier to search for the latest information. Furthermore, appropriate categories can be assigned as metadata based on the technology or culture related to the video. In this way, assigning metadata to videos can improve searchability.

[0078] The viewing unit can recommend related videos by referring to the user's viewing history during viewing. For example, related videos can be recommended based on videos the user has viewed in the past. It can also recommend videos that the user may be interested in based on the user's viewing history. Furthermore, it can analyze the user's viewing history and recommend the most relevant videos. This makes it possible to recommend related videos by referring to the user's viewing history.

[0079] The processing flow of the first embodiment will be briefly explained below.

[0080] Step 1: The collection department collects old written or visual materials, and artisan interview data. For example, the collection department can collect written or visual materials in the form of books, papers, films, documentaries, etc. The collection department can also collect artisan interview data in the form of audio data, video data, text data, etc. Furthermore, the collection department can collect written data from digital archives and library databases. Step 2: The analysis department analyzes the data collected by the collection department and extracts key points of technology and culture. For example, the analysis department uses text mining technology to extract technical procedures and historical background from document data. The analysis department can also use video analysis technology to analyze the actual movement of technology from video data. Furthermore, the analysis department can also use audio analysis technology to extract key points of craftsmen's experiences and techniques from interview data. Step 3: The generator generates a video based on the key points extracted by the analyzer. For example, the generator generates a video that explains a technical procedure step by step. The generator can also generate a video that introduces historical background. Furthermore, the generator can use animation or live-action footage to generate the video in a format that is visually easy to understand. Step 4: The storage unit stores the video generated by the generation unit as an archive. For example, the storage unit stores the generated video as a digital archive and provides it in an accessible form on an online platform.

[0081] (Example 2) A system according to an embodiment of the present invention uses AI to analyze data and documents related to traditional craft techniques and culture, generating easy-to-understand videos based on the results and storing them as an archive on an online platform. This system was developed to address the lack of successors and the difficulty of passing on traditional crafts. The system collects old documents, video materials, and interviews with artisans, and then uses AI to analyze this data and extract key points of the techniques and culture. Next, videos are generated based on the extracted key points and stored as an archive. Furthermore, a dedicated app is provided with a viewing section for viewing videos, a registration section for registering potential successors, and a matching section for matching with artisans. For example, the system collects old documents, video materials, and interviews with artisans. Next, AI analyzes this data and extracts key points of the techniques and culture. For example, it extracts technical procedures and historical background from old documents and analyzes the actual movements of the techniques from video materials. Next, it generates videos in a visually easy-to-understand format based on the extracted key points. For example, it generates videos that explain the technical procedures step by step or videos that introduce the historical background. The generated videos are stored in an archive preservation unit and made accessible on an online platform. This allows the system to widely preserve traditional craft techniques and culture and pass them on to future generations. This allows the system to widely preserve valuable information and techniques related to traditional craft techniques and culture and pass them on to future generations. For example, the system can help discover people who want to learn traditional crafts and find successors, thereby passing on a rich traditional craft culture to the future.

[0082] A traditional craft preservation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a storage unit. The collection unit collects old documents, video materials, and artisan interview data. For example, the collection unit may collect documents and video materials in the form of books, papers, films, documentaries, etc. The collection unit may also collect artisan interview data in the form of audio data, video data, text data, etc. The collection unit may also collect document data from digital archives and library databases. The analysis unit analyzes the data collected by the collection unit to extract key points of techniques and culture. For example, the analysis unit may use text mining technology to extract technical procedures and historical background from the document data. The analysis unit may also use video analysis technology to analyze the actual movements of techniques from video data. The analysis unit may also use audio analysis technology to extract key points of artisans' experiences and techniques from interview data. The generation unit generates a video based on the key points extracted by the analysis unit. For example, the generation unit may generate a video that explains the steps of a technique step by step. The generation unit may also generate a video that introduces the historical background. Furthermore, the generation unit can use animation or live-action footage to generate videos in a visually easy-to-understand format. The storage unit stores the videos generated by the generation unit as an archive. For example, the storage unit stores the generated videos as a digital archive and provides them in an accessible form on an online platform. This allows the traditional craft preservation system according to the embodiment to efficiently collect, analyze, generate, and store data related to traditional craft techniques and culture.

[0083] Furthermore, the traditional craft preservation system includes a viewing unit for viewing videos, a registration unit for registering as a successor, and a matching unit for matching with artisans through a dedicated app. The viewing unit provides a function for users to watch videos. For example, the viewing unit can provide a function for streaming viewing or downloading viewing. The registration unit provides a function for registering as a successor. For example, the registration unit supports the user in entering necessary information and the procedure for registering as a successor. The matching unit provides a function for matching with artisans. For example, the matching unit matches with an appropriate artisan based on the user's skills and interests. This allows users to watch videos, register as a successor, and be matched with artisans.

[0084] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, the collection unit prioritizes collecting related literature and video materials based on the field of traditional crafts in which the user is interested. Furthermore, if the user has a strong interest in a particular artisan, the collection unit can prioritize collecting interviews with that artisan. Furthermore, if the user is interested in a particular technique, the collection unit can prioritize collecting data related to that technique. This makes it possible to determine the priority of data to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] The collection unit can evaluate the reliability of data at the time of collection and prioritize collection of highly reliable data. For example, the collection unit evaluates whether the source of the data is from a reliable academic institution or expert and prioritizes collection of highly reliable data. The collection unit can also check the publication date or update date of the data and prioritize collection of the latest information. Furthermore, the collection unit can check whether the content of the data matches other highly reliable data and prioritize collection of highly reliable data. This allows highly reliable data to be collected preferentially.

[0086] The collection department can select the most appropriate collection method depending on the type of data when collecting the data. For example, in the case of document data, the collection department collects data from digital archives or library databases. In the case of video data, the collection department can also collect data from video material providers or online video platforms. Furthermore, in the case of interview data, the collection department can collect data by conducting direct interviews with craftsmen or experts. This makes it possible to select the most appropriate collection method depending on the type of data.

[0087] When collecting data, the collection department can prioritize collecting the latest information, taking into account the frequency of data updates. For example, the collection department collects the latest literature and video materials from a database that is updated regularly. The collection department can also prioritize collecting the latest interviews with craftsmen and experts. Furthermore, the collection department can prioritize collecting data that includes information on the latest technology and culture. This allows the collection of the latest information to be prioritized.

[0088] The collection unit can estimate the user's emotions and select the type of data to collect based on the estimated user emotions. For example, the collection unit collects related literature and video materials based on the field in which the user is interested. If the user has a strong interest in a particular craftsman, the collection unit can also collect interviews with that craftsman. Furthermore, if the user is interested in a particular technology, the collection unit can also collect data related to that technology. This makes it possible to select the type of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] When collecting data, the collection department can take geographical distribution into consideration and collect data for each region in a balanced manner. For example, the collection department can collect literature and video materials related to traditional crafts in each region in a balanced manner. The collection department can also collect interviews with artisans in each region in a balanced manner. Furthermore, the collection department can collect data related to the techniques and culture of each region in a balanced manner. This allows for the collection of data for each region in a balanced manner.

[0090] The collection unit can select the most appropriate collection method depending on the format of the data when collecting the data. For example, in the case of text data, the collection unit collects the data from digital archives or library databases. In addition, in the case of audio data, the collection unit can collect the data from providers of audio materials or online audio platforms. Furthermore, in the case of image data, the collection unit can collect the data from providers of image materials or online image platforms. This makes it possible to select the most appropriate collection method depending on the format of the data.

[0091] The collection unit can evaluate the relevance of data at the time of collection and prioritize collection of highly relevant data. For example, the collection unit checks whether the data to be collected matches other highly reliable data and prioritizes collection of highly relevant data. The collection unit can also evaluate whether the data to be collected is related to a specific technology or culture and prioritize collection of highly relevant data. Furthermore, the collection unit can evaluate whether the data to be collected is related to the user's interests and concerns and prioritize collection of highly relevant data. This allows highly relevant data to be collected preferentially.

[0092] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. The analysis unit, for example, prioritizes the analysis of related data based on the field in which the user is interested. Furthermore, if the user has a strong interest in a particular craftsman, the analysis unit can prioritize the analysis of data related to that craftsman. Furthermore, if the user has an interest in a particular technology, the analysis unit can prioritize the analysis of data related to that technology. This makes it possible to determine the priority of analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0093] During analysis, the analysis unit can classify the content of the data and extract key points of technology and culture. For example, the analysis unit extracts technical procedures and historical background from document data. The analysis unit can also analyze the actual movements of technology from video data and extract key points. Furthermore, the analysis unit can extract key points of craftsmen's experiences and techniques from interview data. This makes it possible to classify the content of the data and extract key points of technology and culture.

[0094] During analysis, the analysis unit can apply an analysis method that takes into account the interrelationships between data to gain a deeper understanding. For example, the analysis unit can combine and analyze document data and video data to understand the procedures and actual movements of the technology. The analysis unit can also combine and analyze interview data and document data to understand the historical background of the technology and the experience of the craftsman. Furthermore, the analysis unit can combine and analyze video data and interview data to understand the actual movements of the technology and the explanations of the craftsman. This makes it possible to apply an analysis method that takes into account the interrelationships between data to gain a deeper understanding.

[0095] During analysis, the analysis unit can evaluate the reliability of the data and prioritize analysis of highly reliable data. For example, the analysis unit evaluates whether the source of the data is from a reliable academic institution or expert, and prioritizes analysis of highly reliable data. The analysis unit can also check the publication date and update date of the data, and prioritize analysis of the latest information. Furthermore, the analysis unit can check whether the content of the data matches other highly reliable data, and prioritize analysis of highly reliable data. This allows highly reliable data to be prioritized in analysis.

[0096] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can apply a detailed analysis method to gain a deep understanding. If the user is in a hurry, the analysis unit can apply a concise analysis method to quickly extract the main points. Furthermore, if the user is excited, the analysis unit can apply a visually stimulating analysis method to attract attention. This allows the analysis method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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.

[0097] During analysis, the analysis unit can apply different analysis methods depending on the type of data. For example, in the case of document data, the analysis unit applies text analysis methods to extract technical procedures and historical background. In addition, in the case of video data, the analysis unit can apply video analysis methods to analyze the actual movements of the technology. Furthermore, in the case of interview data, the analysis unit can apply audio analysis methods to extract key points of the craftsman's experience and skills. This makes it possible to apply different analysis methods depending on the type of data.

[0098] During analysis, the analysis unit can prioritize analyzing the latest information, taking into account the frequency of data updates. For example, the analysis unit analyzes the latest literature and video materials from a database that is updated regularly. The analysis unit can also prioritize analyzing the latest interviews with craftsmen and experts. Furthermore, the analysis unit can prioritize analyzing data that includes information on the latest technology and culture. This allows the latest information to be analyzed with priority.

[0099] During analysis, the analysis unit can evaluate the relevance of the data and prioritize analysis of highly relevant data. For example, the analysis unit checks whether the data to be analyzed matches other highly reliable data and prioritizes analysis of highly relevant data. The analysis unit can also evaluate whether the data to be analyzed is related to a specific technology or culture and prioritize analysis of highly relevant data. Furthermore, the analysis unit can evaluate whether the data to be analyzed is related to the user's interests and concerns and prioritize analysis of highly relevant data. This allows for prioritized analysis of highly relevant data.

[0100] The generation unit can estimate the user's emotions and adjust the way the video is presented based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates a video that progresses at a leisurely pace. If the user is in a hurry, the generation unit can also generate a video that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can also generate a video that adds visually stimulating effects. This makes it possible to adjust the way the video is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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.

[0101] During generation, the generation unit can generate a video that explains the steps of a technique step by step. For example, the generation unit generates a video that explains the steps of a technique in detail and provides it in a format that is visually easy to understand. The generation unit can also generate a video that explains the steps of a technique step by step and provide it in a format that is easy for even beginners to understand. Furthermore, the generation unit can generate a video that explains the steps of a technique using actual movements and provide it in a format that is visually easy to understand. In this way, a video that explains the steps of a technique step by step can be generated.

[0102] The generation unit can generate a video introducing the historical background during generation. For example, the generation unit generates a video that explains the historical background of a technology in detail and provides it in a format that is visually easy to understand. The generation unit can also generate a video that explains the historical background of a technology step by step and provides it in a format that is easy for even beginners to understand. Furthermore, the generation unit can generate a video that explains the historical background of a technology using actual footage and photos and provides it in a format that is visually easy to understand. In this way, a video introducing the historical background can be generated.

[0103] During generation, the generation unit can select an optimal method for generating a video in a format that is visually easy to understand. For example, the generation unit generates a video that explains a technical procedure using animation and provides it in a format that is visually easy to understand. The generation unit can also generate a video that explains a technical procedure using actual footage and provide it in a format that is visually easy to understand. Furthermore, the generation unit can generate a video that explains a technical procedure using a 3D model and provide it in a format that is visually easy to understand. This makes it possible to select an optimal method for generating a video in a format that is visually easy to understand.

[0104] The generation unit can estimate the user's emotions and adjust the length of the video based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point video. If the user is relaxed, the generation unit can also generate a longer video with detailed explanations. Furthermore, if the user is excited, the generation unit can also generate a video with visually stimulating effects. This makes it possible to adjust the length of the video based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0105] The generation unit can apply different video generation methods depending on the type of data during generation. For example, in the case of literature data, the generation unit generates a video that combines text and images. In the case of video data, the generation unit can also edit the video to generate a video that is visually easy to understand. Furthermore, in the case of interview data, the generation unit can also generate a video that combines audio and video. This makes it possible to apply different video generation methods depending on the type of data.

[0106] The generation unit can generate videos that reflect the latest information, taking into account the frequency of data updates during generation. For example, the generation unit generates videos that reflect the latest literature and video materials from a database that is updated regularly. The generation unit can also generate videos that reflect the latest interviews with craftsmen and experts. Furthermore, the generation unit can generate videos that include information on the latest technology and culture. This makes it possible to generate videos that reflect the latest information.

[0107] The generation unit can evaluate the relevance of data at the time of generation and preferentially reflect highly relevant information in the video. For example, the generation unit checks whether the video to be generated matches other highly reliable data and preferentially reflects highly relevant information. The generation unit can also evaluate whether the video to be generated is related to a specific technology or culture and preferentially reflect highly relevant information. Furthermore, the generation unit can evaluate whether the video to be generated is related to the user's interests and concerns and preferentially reflect highly relevant information. This allows highly relevant information to be preferentially reflected in the video.

[0108] The storage unit can estimate the user's emotions and determine the priority of videos to be saved based on the estimated user emotions. The storage unit, for example, prioritizes saving related videos based on the field in which the user is interested. Furthermore, if the user has a strong interest in a particular craftsman, the storage unit can also prioritize saving videos related to that craftsman. Furthermore, if the user has an interest in a particular technology, the storage unit can also prioritize saving videos related to that technology. This makes it possible to determine the priority of videos to be saved based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0109] The storage unit can assign metadata to videos when storing them, thereby improving searchability. For example, the storage unit assigns appropriate keywords as metadata based on the content of the video. The storage unit can also assign the creation date or update date of the video as metadata, making it easier to search for the latest information. Furthermore, the storage unit can assign appropriate categories as metadata based on the technology or culture related to the video. In this way, assigning metadata to videos can improve searchability.

[0110] The storage unit can optimize the format of the video when storing it and store it in a format suitable for long-term storage. The storage unit can, for example, compress the format of the video to improve storage efficiency. The storage unit can also standardize the format of the video and store it in a format suitable for long-term storage. Furthermore, the storage unit can convert the format of the video and store it in a format that can be played on different devices. In this way, the video format can be optimized and stored in a format suitable for long-term storage.

[0111] The storage unit periodically backs up the videos when storing them, thereby ensuring the safety of the data. For example, the storage unit periodically backs up the videos to ensure the safety of the data. The storage unit can also store the backups of the videos in different locations to ensure the safety of the data. Furthermore, the storage unit can store the backups of the videos in cloud storage to ensure the safety of the data. In this way, the safety of the data can be ensured by periodically backing up the videos.

[0112] The storage unit can estimate the user's emotions and select a category for the video to be saved based on the estimated user's emotions. For example, the storage unit saves related videos in an appropriate category based on the field in which the user is interested. Furthermore, if the user has a strong interest in a particular craftsman, the storage unit can save videos related to that craftsman in an appropriate category. Furthermore, if the user has an interest in a particular technology, the storage unit can save videos related to that technology in an appropriate category. This makes it possible to select a category for the video to be saved based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0113] The storage unit can set access permissions for the video when storing the video, and allow access only to appropriate users. The storage unit can set appropriate access permissions based on, for example, the content of the video. The storage unit can also set appropriate access permissions based on the creator or provider of the video. Furthermore, the storage unit can also set appropriate access permissions based on the technology or culture related to the video. In this way, by setting access permissions for the video, it is possible to allow access only to appropriate users.

[0114] The storage unit can improve storage efficiency by applying a video compression technique when storing the video. For example, the storage unit can compress the video format to improve storage efficiency. The storage unit can also adjust the video resolution to improve storage efficiency. Furthermore, the storage unit can also adjust the video bitrate to improve storage efficiency. In this way, by applying a video compression technique, storage efficiency can be improved.

[0115] The storage unit can link related information of the video when storing the video, thereby providing comprehensive information. For example, the storage unit can link literature related to the content of the video, thereby providing comprehensive information. The storage unit can also link interviews related to the content of the video, thereby providing comprehensive information. Furthermore, the storage unit can link video materials related to the content of the video, thereby providing comprehensive information. In this way, by linking related information of the video, comprehensive information can be provided.

[0116] The viewing unit can estimate the user's emotions and adjust the viewing interface based on the estimated user emotions. For example, if the user is nervous, the viewing unit can provide an interface with calm colors to reduce visual stress. Furthermore, if the user is enjoying themselves, the viewing unit can provide an interface with bright colors to make the viewing task more enjoyable. Furthermore, if the user is tired, the viewing unit can provide a simple, highly visible interface to make the viewing task easier. This allows the viewing interface to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0117] The viewing unit can recommend related videos by referring to the user's viewing history when viewing. The viewing unit recommends related videos based on, for example, videos the user has viewed in the past. The viewing unit can also recommend videos that the user is likely to be interested in based on the user's viewing history. Furthermore, the viewing unit can analyze the user's viewing history and recommend the most relevant video. This makes it possible to recommend related videos by referring to the user's viewing history.

[0118] The viewing unit can adjust the playback speed of the video during viewing to provide a viewing experience that suits the user's level of understanding. For example, the viewing unit plays the video at a speed that is easy for the user to understand. The viewing unit can also adjust the playback speed so that the user can easily understand detailed information. Furthermore, if the user is in a hurry, the viewing unit can also speed up the playback speed to provide a viewing experience. In this way, by adjusting the playback speed of the video, a viewing experience that suits the user's level of understanding can be provided.

[0119] The viewing unit can provide subtitles and commentary for the video during viewing, thereby supporting viewing in a visually easy-to-understand format. The viewing unit can provide appropriate subtitles, for example, based on the content of the video. The viewing unit can also provide appropriate commentary based on the content of the video. Furthermore, the viewing unit can also provide subtitles and commentary in a visually easy-to-understand format based on the content of the video. In this way, by providing subtitles and commentary for the video, viewing can be supported in a visually easy-to-understand format.

[0120] The viewing unit can estimate the user's emotions and adjust the viewing order based on the estimated user emotions. For example, the viewing unit can prioritize viewing of related videos based on the field in which the user is interested. Furthermore, if the user has a strong interest in a particular craftsman, the viewing unit can prioritize viewing of videos related to that craftsman. Furthermore, if the user has an interest in a particular technology, the viewing unit can prioritize viewing of videos related to that technology. This makes it possible to adjust the viewing order based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0121] The viewing unit can provide an optimal viewing format by taking into account device information of the user when viewing. For example, if the user is using a smartphone, the viewing unit can provide a viewing format that matches the screen size. Also, if the user is using a tablet, the viewing unit can provide a viewing format optimized for a large screen. Furthermore, if the user is using a smartwatch, the viewing unit can provide a simple and highly visible viewing format. In this way, the optimal viewing format can be provided by taking into account device information of the user.

[0122] The viewing unit can provide multilingual support for videos in accordance with the user's language settings during viewing. The viewing unit can automatically set subtitles and audio for videos based on the language settings of the user's device, for example. The viewing unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the viewing unit can provide the video in that language. This allows the video to be provided in multiple languages ​​in accordance with the user's language settings.

[0123] The viewing unit can collect user feedback during viewing and reflect it in improving the viewing experience. The viewing unit, for example, provides an interface that allows the user to provide feedback after viewing. The viewing unit can also analyze the user feedback and reflect it in improving the viewing experience. Furthermore, the viewing unit can improve the content and format of the video based on the user feedback. In this way, the collected user feedback can be reflected in improving the viewing experience.

[0124] The registration unit can estimate the user's emotions and adjust the registration interface based on the estimated user emotions. For example, if the user is nervous, the registration unit can provide an interface with calm colors to reduce visual stress. Furthermore, if the user is having fun, the registration unit can provide an interface with bright colors to make the registration process more enjoyable. Furthermore, if the user is tired, the registration unit can provide a simple, highly visible interface to make the registration process easier. This makes it possible to adjust the registration interface based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0125] At the time of registration, the registration unit can suggest the optimal registration method by referring to the user's past activity history. For example, the registration unit preferentially suggests registration methods (email, SNS account, etc.) that the user has used in the past. The registration unit can also suggest the optimal registration category based on the user's past activity history. Furthermore, the registration unit can analyze the user's past activity history and suggest the most efficient registration method. In this way, the optimal registration method can be suggested by referring to the user's past activity history.

[0126] The registration unit can recommend an appropriate registration category based on the user's interests at the time of registration. For example, the registration unit recommends related registration categories based on the field in which the user is interested. Furthermore, if the user has a strong interest in a particular craftsman, the registration unit can also recommend a registration category related to that craftsman. Furthermore, if the user has an interest in a particular skill, the registration unit can also recommend a registration category related to that skill. This makes it possible to recommend appropriate registration categories based on the user's interests.

[0127] The registration unit can improve the registration process by reflecting user feedback at the time of registration. For example, the registration unit provides an interface that allows the user to provide feedback after registration. The registration unit can also analyze the user feedback and reflect it in improving the registration process. Furthermore, the registration unit can improve the registration interface and procedures based on the user feedback. In this way, the registration process can be improved by reflecting user feedback.

[0128] The registration unit can estimate the user's emotions and determine the priority of registration based on the estimated user emotions. The registration unit, for example, prioritizes related registrations based on the field in which the user is interested. Furthermore, if the user has a strong interest in a particular craftsman, the registration unit can also prioritize registrations related to that craftsman. Furthermore, if the user has an interest in a particular technology, the registration unit can also prioritize registrations related to that technology. This makes it possible to determine the priority of registrations based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0129] The registration unit can support registration by region, taking into account the geographical information of the user, at the time of registration. For example, the registration unit provides a registration category by region based on the geographical information of the user. The registration unit can also support registration of craftsmen and techniques by region, based on the geographical information of the user. Furthermore, the registration unit can support registration of events and workshops by region, based on the geographical information of the user. In this way, it is possible to support registration by region, taking into account the geographical information of the user.

[0130] At the time of registration, the registration unit can analyze the user's social media activity and provide related registration information. For example, the registration unit can analyze the content posted by the user on social media and provide related registration information. The registration unit can also provide related registration information by referring to the activities of the user's friends on social media. Furthermore, the registration unit can also provide related registration information based on the user's check-in information on social media. In this way, related registration information can be provided by analyzing the user's social media activity.

[0131] The registration unit can provide the optimal registration format during registration by taking into consideration the user's device information. For example, if the user is using a smartphone, the registration unit can provide a registration format that matches the screen size. Furthermore, if the user is using a tablet, the registration unit can also provide a registration format that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the registration unit can also provide a simple and highly visible registration format. This makes it possible to provide the optimal registration format by taking into consideration the user's device information.

[0132] The matching unit can estimate the user's emotions and adjust the matching interface based on the estimated user emotions. For example, if the user is nervous, the matching unit can provide an interface with subdued colors to reduce visual stress. Furthermore, if the user is having fun, the matching unit can provide an interface with bright colors to make the matching process more enjoyable. Furthermore, if the user is tired, the matching unit can provide a simple, highly visible interface to make the matching process easier. This allows the matching interface to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0133] During matching, the matching unit can suggest the optimal matching method by referring to the user's past activity history. For example, the matching unit preferentially suggests matching methods (email, SNS account, etc.) that the user has used in the past. The matching unit can also suggest the optimal matching category from the user's past activity history. Furthermore, the matching unit can analyze the user's past activity history and suggest the most efficient matching method. In this way, the optimal matching method can be suggested by referring to the user's past activity history.

[0134] During matching, the matching unit can recommend appropriate matching candidates based on the user's interests. For example, the matching unit recommends related matching candidates based on the field in which the user is interested. Furthermore, if the user has a strong interest in a particular craftsman, the matching unit can also recommend matching candidates related to that craftsman. Furthermore, if the user has an interest in a particular skill, the matching unit can also recommend matching candidates related to that skill. This makes it possible to recommend appropriate matching candidates based on the user's interests.

[0135] The matching unit can improve the matching process by reflecting user feedback during matching. For example, the matching unit provides an interface that allows the user to provide feedback after matching. The matching unit can also analyze the user feedback and reflect it in improving the matching process. Furthermore, the matching unit can improve the matching interface and procedures based on the user feedback. In this way, the matching process can be improved by reflecting user feedback.

[0136] The matching unit can estimate the user's emotions and determine the priority of matching based on the estimated user's emotions. The matching unit, for example, prioritizes related matching based on the field in which the user is interested. Furthermore, if the user has a strong interest in a particular craftsman, the matching unit can also prioritize matching related to that craftsman. Furthermore, if the user has an interest in a particular technology, the matching unit can also prioritize matching related to that technology. This makes it possible to determine the priority of matching based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0137] The matching unit can support matching for each region by taking into account the user's geographical information during matching. For example, the matching unit provides matching candidates for each region based on the user's geographical information. The matching unit can also support matching related to craftsmen and techniques for each region based on the user's geographical information. Furthermore, the matching unit can support matching related to events and workshops for each region based on the user's geographical information. In this way, matching for each region can be supported by taking into account the user's geographical information.

[0138] The matching unit can analyze the user's social media activity during matching and provide related matching information. For example, the matching unit can analyze the content posted by the user on social media and provide related matching information. The matching unit can also provide related matching information by referring to the activity of the user's friends on social media. Furthermore, the matching unit can also provide related matching information based on the user's check-in information on social media. In this way, related matching information can be provided by analyzing the user's social media activity.

[0139] The matching unit can provide an optimal matching format by taking into account the user's device information during matching. For example, if the user is using a smartphone, the matching unit can provide a matching format that suits the screen size. Furthermore, if the user is using a tablet, the matching unit can provide a matching format that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the matching unit can provide a simple and highly visible matching format. In this way, the optimal matching format can be provided by taking into account the user's device information. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, storage unit, viewing unit, registration unit, and matching unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect old documents, video materials, and interviews with craftsmen using the camera 42 and microphone 38B of the smart device 14. The analysis unit, implemented by the specific processing unit 290 of the data processing device 12, analyzes the collected data and extracts key points of technology and culture. The generation unit, implemented by the specific processing unit 290 of the data processing device 12, generates videos based on the extracted key points. The storage unit stores the generated videos in the storage 32 of the data processing device 12 and provides them in an accessible form on an online platform. The viewing unit, registration unit, and matching unit are implemented by the control unit 46A of the smart device 14, allowing users to watch videos, register as successors, and be matched with craftsmen. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, storage unit, viewing unit, registration unit, and matching unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect old documents, video materials, and interviews with craftsmen using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit, implemented by the specific processing unit 290 of the data processing device 12, analyzes the collected data and extracts key points of technology and culture. The generation unit, implemented by the specific processing unit 290 of the data processing device 12, generates videos based on the extracted key points. The storage unit stores the generated videos in the storage 32 of the data processing device 12 and provides them in an accessible form on an online platform. The viewing unit, registration unit, and matching unit are implemented by the control unit 46A of the smart glasses 214, allowing users to watch videos, register as successors, and be matched with craftsmen. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, storage unit, viewing unit, registration unit, and matching unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect old documents, video materials, and interviews with craftsmen using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit, implemented by the specific processing unit 290 of the data processing device 12, analyzes the collected data and extracts key points of technology and culture. The generation unit, implemented by the specific processing unit 290 of the data processing device 12, generates videos based on the extracted key points. The storage unit stores the generated videos in the storage 32 of the data processing device 12 and provides them in an accessible form on an online platform. The viewing unit, registration unit, and matching unit are implemented by the control unit 46A of the headset terminal 314, allowing users to watch videos, register as successors, and be matched with craftsmen. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, storage unit, viewing unit, registration unit, and matching unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can use the camera 42 and microphone 238 of the robot 414 to collect old documents, video materials, and interviews with craftsmen. The analysis unit, implemented by the specific processing unit 290 of the data processing device 12, analyzes the collected data and extracts key points of technology and culture. The generation unit, implemented by the specific processing unit 290 of the data processing device 12, generates videos based on the extracted key points. The storage unit stores the generated videos in the storage 32 of the data processing device 12 and provides them in an accessible form on an online platform. The viewing unit, registration unit, and matching unit are implemented by the control unit 46A of the robot 414, allowing users to watch videos, register as successors, and be matched with craftsmen.

[0140] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0141] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user shows a strong interest in a particular technology, the analysis of data related to that technology can be prioritized. Also, if the user shows a strong interest in a particular craftsman, the analysis of data related to that craftsman can be prioritized. Furthermore, if the user is interested in a particular cultural background, the analysis of data related to that cultural background can be prioritized. In this way, the analysis priority can be determined based on the user's emotions.

[0142] The generation unit can estimate the user's emotions and adjust the way the video is presented based on the estimated user's emotions. For example, if the user is relaxed, a video that progresses at a leisurely pace can be generated. If the user is in a hurry, a video that emphasizes the shortest route can be generated. Furthermore, if the user is excited, a video that adds visually stimulating effects can be generated. In this way, the way the video is presented can be adjusted based on the user's emotions.

[0143] The storage unit can estimate the user's emotions and determine the priority of videos to be saved based on the estimated user's emotions. For example, if the user shows a strong interest in a particular technique, videos related to that technique can be saved with priority. Also, if the user shows a strong interest in a particular craftsman, videos related to that craftsman can be saved with priority. Furthermore, if the user is interested in a particular cultural background, videos related to that cultural background can be saved with priority. In this way, the priority of videos to be saved can be determined based on the user's emotions.

[0144] The viewing unit can estimate the user's emotions and adjust the viewing interface based on the estimated user's emotions. For example, if the user is nervous, a calm-colored interface can be provided to reduce visual stress. If the user is enjoying themselves, a bright-colored interface can be provided to make the viewing task more enjoyable. Furthermore, if the user is tired, a simple, highly visible interface can be provided to make the viewing task easier. In this way, the viewing interface can be adjusted based on the user's emotions.

[0145] The registration unit can estimate the user's emotions and adjust the registration interface based on the estimated user's emotions. For example, if the user is nervous, a calm-colored interface can be provided to reduce visual stress. If the user is having fun, a bright-colored interface can be provided to make the registration process more enjoyable. Furthermore, if the user is tired, a simple, highly visible interface can be provided to make the registration process easier. In this way, the registration interface can be adjusted based on the user's emotions.

[0146] The collection unit can evaluate the reliability of data at the time of collection and prioritize collection of highly reliable data. For example, it can evaluate whether the source of the data is from a reliable academic institution or expert and prioritize collection of highly reliable data. It can also check the publication date and update date of the data and prioritize collection of the latest information. Furthermore, it can check whether the content of the data matches other highly reliable data and prioritize collection of highly reliable data. This makes it possible to prioritize collection of highly reliable data.

[0147] During analysis, the analysis unit can apply analytical methods that take into account the interrelationships between data to gain a deeper understanding. For example, document data and video data can be combined and analyzed to understand the procedures and actual movements of a technique. Interview data and document data can also be combined and analyzed to understand the historical background of a technique and the experience of a craftsman. Furthermore, video data and interview data can be combined and analyzed to understand the actual movements of a technique and the artisan's commentary. This makes it possible to apply analytical methods that take into account the interrelationships between data to gain a deeper understanding.

[0148] At the time of generation, the generation unit can select the optimal method for generating a video in a format that is visually easy to understand. For example, a video that explains a technical procedure using animation can be generated and provided in a format that is visually easy to understand. Alternatively, a video that explains a technical procedure using actual footage can be generated and provided in a format that is visually easy to understand. Furthermore, a video that explains a technical procedure using a 3D model can be generated and provided in a format that is visually easy to understand. This makes it possible to select the optimal method for generating a video in a format that is visually easy to understand.

[0149] The storage unit can assign metadata to videos when saving them to improve searchability. For example, appropriate keywords can be assigned as metadata based on the content of the video. The creation date or update date of the video can also be assigned as metadata to make it easier to search for the latest information. Furthermore, appropriate categories can be assigned as metadata based on the technology or culture related to the video. In this way, assigning metadata to videos can improve searchability.

[0150] The viewing unit can recommend related videos by referring to the user's viewing history during viewing. For example, related videos can be recommended based on videos the user has viewed in the past. It can also recommend videos that the user may be interested in based on the user's viewing history. Furthermore, it can analyze the user's viewing history and recommend the most relevant videos. This makes it possible to recommend related videos by referring to the user's viewing history.

[0151] The processing flow of the second embodiment will be briefly explained below.

[0152] Step 1: The collection department collects old written or visual materials, and artisan interview data. For example, the collection department can collect written or visual materials in the form of books, papers, films, documentaries, etc. The collection department can also collect artisan interview data in the form of audio data, video data, text data, etc. Furthermore, the collection department can collect written data from digital archives and library databases. Step 2: The analysis department analyzes the data collected by the collection department and extracts key points of technology and culture. For example, the analysis department uses text mining technology to extract technical procedures and historical background from document data. The analysis department can also use video analysis technology to analyze the actual movement of technology from video data. Furthermore, the analysis department can also use audio analysis technology to extract key points of craftsmen's experiences and techniques from interview data. Step 3: The generator generates a video based on the key points extracted by the analyzer. For example, the generator generates a video that explains a technical procedure step by step. The generator can also generate a video that introduces historical background. Furthermore, the generator can use animation or live-action footage to generate the video in a format that is visually easy to understand. Step 4: The storage unit stores the video generated by the generation unit as an archive. For example, the storage unit stores the generated video as a digital archive and provides it in an accessible form on an online platform.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0158] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0187] 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.

[0188] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0189] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0190] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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).

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0204] 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.

[0205] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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).

[0210] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0211] 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."

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] [Explanation of symbols]

[0225] 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 collection department collects data from old documents, video materials, and interviews with artisans. an analysis unit that analyzes the data collected by the collection unit and extracts key points of technology and culture; a generating unit that generates a video based on the key points extracted by the analyzing unit; a storage unit that stores the video generated by the generation unit as an archive. A system characterized by:

2. Through a dedicated app, it also has a viewing section for watching videos, a registration section for registering as a successor, and a matching section for matching with craftsmen.

2. The system of claim 1.

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

4. The collection unit evaluates the reliability of data at the time of collection and collects reliable data with priority.

2. The system of claim 1.

5. The collection unit selects an appropriate collection means depending on the type of data at the time of collection.

2. The system of claim 1.

6. The collection unit collects the latest information preferentially based on the update frequency of the data at the time of collection.

2. The system of claim 1.

7. The collecting unit Inferring user emotions and selecting the type of data to collect based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit When collecting data, take into account geographical distribution and collect data in a balanced manner for each region.

2. The system of claim 1.

9. The collecting unit When collecting data, select the most appropriate collection method depending on the data format.

2. The system of claim 1.

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