System

Abstract

An object of a system according to an embodiment is to appropriately tag and update a data set in accordance with legal regulations and guidelines.SOLUTION: A system includes a collection unit, a tagging unit, an update unit, and a provision unit. The collection unit collects a data set. The tagging unit tags the data set collected by the collection unit in accordance with laws and regulations or guidelines. The updating unit updates the tag of the data set tagged by the tagging unit. The providing unit provides the data set tagged by the tagging unit.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, the datasets used for AI training were disorganized, making it difficult to verify whether they complied with laws, regulations, and guidelines.

[0005] The system according to the embodiment aims to properly tag and update datasets in accordance with regulations and guidelines. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a tagging unit, an update unit, and a providing unit. The collection unit collects datasets. The tagging unit tags the datasets collected by the collection unit in accordance with laws, regulations, and guidelines. The update unit updates tags on the datasets tagged by the tagging unit. The providing unit provides the datasets tagged by the tagging unit. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately tag and update the dataset in accordance with regulations and guidelines. [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 dataset hub system according to an embodiment of the present invention aggregates and appropriately tags datasets for AI training. The dataset hub system aggregates unorganized datasets collected from the web, tags each dataset based on laws, regulations, and guidelines, and updates and provides the dataset tags in response to changes in laws, regulations, and guidelines. For example, the dataset hub system aggregates datasets collected from the web. In doing so, it records the origin and content of each dataset in detail. This includes various types of datasets, such as news articles, blog posts, and social media posts. The dataset hub system then tags each dataset based on laws, regulations, and guidelines. For example, tags may include information such as whether the dataset complies with the laws and regulations of a particular country or whether the dataset's content is ethically sound. Furthermore, the dataset hub system provides a mechanism for updating dataset tags in response to changes in laws, regulations, and guidelines. For example, when new laws and regulations are enacted, the dataset tags are updated based on the new laws and regulations. The dataset hub system also appropriately tags the origin of each dataset to prevent content created by generative AI from being distributed on the web. For example, content created by generative AI can be tagged with a specific tag to prevent that dataset from being used as training data. This allows the dataset hub system to reduce legal risks for AI developers and enable them to develop AI using appropriate datasets. It also prevents performance degradation of generative AI, enabling the development of higher quality AI. This allows the dataset hub system to reduce legal risks for AI developers and enable them to develop AI using appropriate datasets. It also prevents performance degradation of generative AI, enabling the development of higher quality AI.

[0029] A dataset hub system according to an embodiment includes a collection unit, a tagging unit, an update unit, and a providing unit. The collection unit collects datasets. The collection unit may use, for example, crawling technology to automatically collect data from the web. The collection unit may also collect data using an API. For example, the collection unit collects various types of data, such as news articles, blog posts, and social media posts. The tagging unit tags the datasets collected by the collection unit in accordance with laws, regulations, and guidelines. The tagging unit assigns tags to datasets based on, for example, the laws, regulations, and guidelines of each country. For example, the tag assigns information such as whether the dataset complies with the laws and regulations of a specific country or whether the contents of the dataset are ethically sound. The update unit updates the tags of the datasets tagged by the tagging unit. For example, the update unit automatically updates the tags of the datasets when new laws, regulations, or guidelines are enacted. For example, the update unit updates the tags of the datasets based on the new laws and regulations. The providing unit provides the datasets tagged by the tagging unit. For example, the providing unit may assign a specific identification tag to content created by the generation AI, preventing the use of that dataset as training data. For example, the providing unit may assign a specific identification tag to content created by the generation AI, preventing the use of that dataset as training data. This allows the dataset hub system according to the embodiment to reduce legal risks for AI developers and enable them to develop AI using appropriate datasets. For example, the providing unit may record the origin and content of datasets in detail, allowing AI developers to easily check whether they comply with legal regulations. This allows AI developers to always use datasets that comply with the latest legal regulations.

[0030] The collection unit can use a means for automatically collecting data from the web. The collection unit can automatically collect data from the web using, for example, crawling technology. For example, the collection unit can periodically collect data from specific websites. The collection unit can also collect data using an API. For example, the collection unit can collect various types of data, such as news articles, blog posts, and social media posts. This allows for efficient aggregation of data sets by automatically collecting data from the web. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data collected using crawling technology into a generation AI and have the generation AI classify and filter the data.

[0031] The tagging unit can assign tags to datasets in accordance with the laws, regulations, and guidelines of each country. The tagging unit assigns tags to datasets based on, for example, the laws, regulations, and guidelines of each country. For example, the tagging unit assigns tags containing information such as whether the dataset complies with the laws and regulations of a specific country or whether the contents of the dataset are ethically sound. The tagging unit can also use AI to analyze the contents of the dataset and determine whether the dataset complies with laws, regulations, and guidelines. For example, the tagging unit can use a generation AI to analyze the contents of the dataset and determine whether the dataset complies with laws and regulations. This allows tagging based on the laws, regulations, and guidelines of each country to reduce legal risks. Some or all of the above-mentioned processing in the tagging unit may be performed using AI or without AI. For example, the tagging unit can input the contents of the dataset into a generation AI and have the generation AI determine whether the dataset complies with laws and regulations.

[0032] The update unit can automatically update the tags of the dataset when new laws, regulations, or guidelines are enacted. For example, when new laws, regulations, or guidelines are enacted, the update unit automatically updates the tags of the dataset. For example, the update unit updates the tags of the dataset based on the new laws, regulations, or guidelines. The update unit can also use AI to detect changes in laws, regulations, or guidelines and update the tags of the dataset. For example, the update unit uses a generation AI to detect changes in laws, regulations, or guidelines and update the tags of the dataset. This allows the tags to be updated based on new laws, regulations, or guidelines, so that a dataset that complies with the latest laws and regulations can always be used. Some or all of the above-mentioned processing in the update unit may be performed using AI, for example, or may be performed without using AI. For example, the update unit can input changes in laws, regulations, or guidelines into the generation AI and cause the generation AI to update the tags.

[0033] The providing unit can assign a specific identification tag to content created by the generation AI and prevent the data set from being used as training data. For example, the providing unit assigns a specific identification tag to content created by the generation AI and prevents the data set from being used as training data. For example, the providing unit assigns a specific identification tag to content created by the generation AI and prevents the data set from being used as training data. The providing unit can also use AI to detect content created by the generation AI and assign an identification tag to it. For example, the providing unit uses the generation AI to detect content created by the generation AI and assign an identification tag to it. This prevents the content created by the generation AI from being used as training data, thereby preventing overlearning and performance degradation of the generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input content created by the generation AI to the generation AI and have the generation AI assign an identification tag.

[0034] The providing unit can specifically record the origin and content of the dataset, allowing the AI ​​developer to easily check whether the dataset complies with laws and regulations. For example, the providing unit can specifically record the origin and content of the dataset, allowing the AI ​​developer to easily check whether the dataset complies with laws and regulations. For example, the providing unit can record the origin and content of the dataset in detail, allowing the AI ​​developer to easily check whether the dataset complies with laws and regulations. The providing unit can also use AI to analyze the origin and content of the dataset and determine whether the dataset complies with laws and regulations. For example, the providing unit can use a generation AI to analyze the origin and content of the dataset and determine whether the dataset complies with laws and regulations. In this way, by recording the origin and content of the dataset in detail, the AI ​​developer can easily check whether the dataset complies with laws and regulations. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the origin and content of the dataset into the generation AI and have the generation AI determine whether the dataset complies with laws and regulations.

[0035] When collecting data, the collection unit can evaluate the reliability of the websites from which data is collected and prioritize collecting data from websites whose reliability is confirmed. For example, the collection unit can analyze the websites' past data provision history and prioritize selecting highly reliable sites. The collection unit can also check website operator information and prioritize collecting data from highly reliable operators. The collection unit can also check the website's security certificate and collect data from highly reliable sites. For example, the collection unit can evaluate the reliability of websites and prioritize collecting data from highly reliable sites. This improves the quality of data by collecting data from highly reliable sites. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the reliability evaluation of websites into a generation AI and cause the generation AI to select highly reliable sites.

[0036] The collection unit can select an appropriate collection method depending on the type of data to be collected when collecting data. For example, in the case of text data, the collection unit can efficiently collect data using web scraping technology. In addition, in the case of image data, the collection unit can automatically collect necessary images using image recognition technology. In addition, in the case of video data, the collection unit can extract and collect only necessary portions using video analysis technology. For example, the collection unit can select the optimal collection method depending on the type of data to be collected (text, image, video, etc.) when collecting data. This allows for efficient data collection by selecting the optimal collection method depending on the type of data. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the type of data to be collected into the generation AI and have the generation AI select the optimal collection method.

[0037] The collection unit can optimize the collection schedule during data collection, taking into account the update frequency of the data to be collected. For example, the collection unit periodically collects data from frequently updated news sites. The collection unit can also periodically collect data from blog sites that are updated infrequently. The collection unit can also collect data in real time from SNS sites that are updated irregularly. For example, the collection unit optimizes the collection schedule during data collection, taking into account the update frequency of the data to be collected. In this way, data can be collected efficiently by optimizing the collection schedule taking into account the update frequency of the data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the update frequency of the data to the generation AI and cause the generation AI to optimize the collection schedule.

[0038] The collection unit can adjust the collection range during data collection, taking into account the geographic distribution of the data to be collected. For example, if data from a specific region is required, the collection unit collects data only from that region. Furthermore, if global data is required, the collection unit can collect data evenly from multiple regions. The collection unit can also adjust the collection range based on the laws and regulations of a specific country. For example, the collection unit adjusts the collection range during data collection, taking into account the geographic distribution of the data to be collected. This allows data from required regions to be collected efficiently by adjusting the collection range taking into account the geographic distribution of the data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographic distribution of the data into the generation AI and have the generation AI adjust the collection range.

[0039] The collection unit can automatically determine the language of the data to be collected during data collection, and perform multilingual collection. For example, the collection unit can automatically determine the language of the data to be collected using language identification technology during data collection. The collection unit can also efficiently collect data using data collection means that support multiple languages. The collection unit can also perform optimal data collection by combining different collection means for each language. For example, the collection unit can automatically determine the language of the data to be collected during data collection, and perform multilingual collection. This allows efficient data collection by automatically determining the language of the data and performing multilingual collection. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the language of the data to be collected into a generation AI and cause the generation AI to perform multilingual collection.

[0040] The collection unit can evaluate the relevance of the data to be collected during data collection and prioritize collecting highly relevant data. For example, the collection unit can use a relevance assessment algorithm during data collection to evaluate the relevance of the data to be collected. The collection unit can also prioritize collecting highly relevant data, thereby efficiently collecting data. The collection unit can also prioritize collecting important data, leaving less relevant data for later. For example, the collection unit evaluates the relevance of the data to be collected during data collection and prioritizes collecting highly relevant data. In this way, by evaluating the relevance of the data and priority-collecting highly relevant data, data can be collected efficiently. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the relevance of the data to be collected to a generation AI and cause the generation AI to select highly relevant data.

[0041] The tagging unit can adjust the level of detail of the tags based on the content of the dataset during tagging. For example, if the dataset contains detailed information, the tagging unit can perform detailed tagging to provide abundant information. Furthermore, if the dataset contains concise information, the tagging unit can perform concise tagging to provide information quickly. The tagging unit can also perform tagging with an appropriate level of detail depending on the content of the dataset. For example, the tagging unit adjusts the level of detail of the tags based on the content of the dataset during tagging. This makes it possible to provide appropriate information by adjusting the level of detail of the tags based on the content of the dataset. Some or all of the above-described processing in the tagging unit may be performed using, for example, AI, or may be performed without using AI. For example, the tagging unit can input the content of the dataset to a generation AI and cause the generation AI to adjust the level of detail of the tags.

[0042] The tagging unit can apply different tagging algorithms depending on the category of the dataset during tagging. For example, in the case of text data, the tagging unit performs tagging using natural language processing technology. Furthermore, in the case of image data, the tagging unit can also perform tagging using image recognition technology. Furthermore, in the case of video data, the tagging unit can also perform tagging using video analysis technology. For example, the tagging unit applies different tagging algorithms depending on the category of the dataset during tagging. This allows for efficient tagging by applying different tagging algorithms depending on the category of the dataset. Some or all of the above-mentioned processing in the tagging unit may be performed using, for example, AI, or may be performed without using AI. For example, the tagging unit can input the category of the dataset to the generation AI and cause the generation AI to apply different tagging algorithms.

[0043] The tagging unit can improve the accuracy of tagging by referring to past tagging results during tagging. For example, the tagging unit analyzes past tagging results and applies an algorithm that improves the accuracy of tagging. The tagging unit can also efficiently tag similar data sets based on past tagging results. The tagging unit can also maintain consistency of tagging by referring to past tagging results. For example, the tagging unit improves the accuracy of tagging by referring to past tagging results during tagging. In this way, the accuracy of tagging is improved by referring to past tagging results. Some or all of the above-described processing in the tagging unit may be performed using, for example, AI, or may be performed without using AI. For example, the tagging unit can input past tagging results to a generation AI and cause the generation AI to improve the accuracy of tagging.

[0044] The tagging unit can determine the priority of tags based on the collection date of the dataset during tagging. For example, the tagging unit prioritizes tagging the most recent dataset to maintain the freshness of the information. The tagging unit can also appropriately tag older datasets to maintain the consistency of the information. The tagging unit can also adjust the priority of tagging depending on the collection date of the dataset. For example, the tagging unit determines the priority of tags based on the collection date of the dataset during tagging. This allows the most recent information to be provided preferentially by determining the priority of tags based on the collection date of the dataset. Some or all of the above-described processing in the tagging unit may be performed using, for example, AI, or may be performed without using AI. For example, the tagging unit can input the collection date of the dataset to the generation AI and cause the generation AI to determine the priority of tags.

[0045] The tagging unit can adjust the order of tags based on the relevance of the datasets during tagging. For example, the tagging unit prioritizes tagging for highly relevant datasets, thereby improving information efficiency. The tagging unit can also appropriately tag less relevant datasets to maintain information consistency. The tagging unit can also adjust the order of tags according to the relevance of the datasets. For example, the tagging unit adjusts the order of tags based on the relevance of the datasets during tagging. This allows information to be provided efficiently by adjusting the order of tags based on the relevance of the datasets. Some or all of the above-described processing in the tagging unit may be performed using, for example, AI, or may be performed without using AI. For example, the tagging unit can input the relevance of the datasets to a generation AI and cause the generation AI to adjust the order of tags.

[0046] The tagging unit can evaluate the reliability of the source of a dataset during tagging and assign specific tags to highly reliable datasets. For example, the tagging unit evaluates the reliability of the source of a dataset and assigns specific tags to highly reliable datasets. The tagging unit can also appropriately tag datasets with low reliability to maintain consistency of information. The tagging unit can also adjust the level of detail of tagging depending on the reliability of the source of the dataset. For example, the tagging unit evaluates the reliability of the source of a dataset during tagging and assigns specific tags to highly reliable datasets. This makes it possible to provide highly reliable information by evaluating the reliability of the source of the dataset. Some or all of the above-described processing in the tagging unit may be performed using, for example, AI, or may be performed without using AI. For example, the tagging unit can input the reliability of the source of a dataset to a generation AI and cause the generation AI to assign specific tags.

[0047] The update unit can select the optimal update method by referring to the change history of laws, regulations, and guidelines when updating tags. For example, the update unit can refer to the change history of laws, regulations, and guidelines and update tags based on the latest laws, regulations, and guidelines. The update unit can also refer to the change history of guidelines and update tags based on the latest guidelines. The update unit can also select the optimal tag update method based on the change history of laws, regulations, and guidelines. For example, the update unit can select the optimal update method by referring to the change history of laws, regulations, and guidelines when updating tags. This makes it possible to provide the latest information by referring to the change history of laws, regulations, and guidelines. Some or all of the above-mentioned processing in the update unit can be performed using, for example, AI, or can be performed without using AI. For example, the update unit can input the change history of laws, regulations, and guidelines into the generation AI and cause the generation AI to select the optimal update method.

[0048] The update unit can adjust the level of detail of the update based on the contents of the dataset when updating tags. For example, if the dataset contains detailed information, the update unit can perform a detailed tag update to provide abundant information. Furthermore, if the dataset contains concise information, the update unit can perform a concise tag update to provide quick information. The update unit can also perform tag updates with an appropriate level of detail depending on the contents of the dataset. For example, the update unit adjusts the level of detail of the update based on the contents of the dataset when updating tags. As a result, appropriate information can be provided by adjusting the level of detail of the update based on the contents of the dataset. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input the contents of the dataset to a generation AI and cause the generation AI to adjust the level of detail of the update.

[0049] The update unit can improve the accuracy of tags by referring to past update results when updating tags. For example, the update unit analyzes past tag update results and applies an algorithm that improves the accuracy of the update. The update unit can also efficiently perform tag updates on similar data sets based on past update results. The update unit can also maintain consistency of tag updates by referring to past update results. For example, the update unit improves the accuracy of tags by referring to past update results when updating tags. As a result, the accuracy of the update is improved by referring to past update results. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input past update results into a generation AI and cause the generation AI to improve the accuracy of the update.

[0050] The update unit can adjust the update content when updating tags, taking into account regional differences in laws, regulations, and guidelines. For example, the update unit can perform appropriate tag updates for each region, taking into account differences in laws, regulations, and guidelines between countries. The update unit can also perform appropriate tag updates for each region, taking into account differences in guidelines between each region. The update unit can also adjust the tag update content based on regional differences in laws, regulations, and guidelines. For example, the update unit adjusts the update content when updating tags, taking into account regional differences in laws, regulations, and guidelines. This makes it possible to provide appropriate information by taking into account regional differences in laws, regulations, and guidelines. Some or all of the above-described processing in the update unit can be performed using, for example, AI, or can be performed without using AI. For example, the update unit can input regional differences in laws, regulations, and guidelines into a generation AI and cause the generation AI to adjust the update content.

[0051] The update unit can adjust the frequency of updates based on when the dataset was collected when updating tags. For example, the update unit frequently updates tags on the latest dataset to keep the information fresh. The update unit can also appropriately update tags on older datasets to keep the information consistent. The update unit can also adjust the frequency of tag updates based on when the dataset was collected. For example, the update unit adjusts the frequency of updates based on when the dataset was collected when updating tags. This makes it possible to provide the latest information by adjusting the frequency of updates based on when the dataset was collected. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input the time when the dataset was collected to the generation AI and cause the generation AI to adjust the update frequency.

[0052] The update unit can evaluate the reliability of the source of a dataset when updating tags and prioritize updating highly reliable datasets. For example, the update unit evaluates the reliability of the source of a dataset and prioritizes updating highly reliable datasets. The update unit can also appropriately perform tag updates on datasets with low reliability to maintain information consistency. The update unit can also adjust the priority of tag updates according to the reliability of the source of the dataset. For example, the update unit evaluates the reliability of the source of a dataset when updating tags and prioritizes updating highly reliable datasets. This makes it possible to provide highly reliable information by evaluating the reliability of the source of a dataset. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input the reliability of the source of a dataset to the generation AI and cause the generation AI to perform preferential updates.

[0053] When providing a dataset, the providing unit can select the optimal providing method by referring to the user's past usage history. For example, the providing unit analyzes the user's past usage history and provides the optimal dataset. The providing unit can also provide a similar dataset based on a dataset the user has used in the past. The providing unit can also select the optimal providing method by referring to the user's past usage history. For example, when providing a dataset, the providing unit selects the optimal providing method by referring to the user's past usage history. This makes it possible to provide optimal information by referring to the user's past usage history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past usage history into the generation AI and cause the generation AI to select the optimal providing method.

[0054] The providing unit can customize the provided content based on the user's current project or area of ​​interest when providing a dataset. For example, the providing unit can preferentially provide a dataset related to the user's current project. The providing unit can also provide an optimal dataset based on the user's area of ​​interest. The providing unit can also customize the provided content according to the user's project or area of ​​interest. For example, the providing unit customizes the provided content based on the user's current project or area of ​​interest when providing a dataset. This allows optimal information to be provided by customizing the provided content based on the user's current project or area of ​​interest. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current project or area of ​​interest into the generation AI and cause the generation AI to customize the provided content.

[0055] The providing unit can improve the providing method by reflecting user feedback when providing a dataset. The providing unit, for example, collects user feedback and improves the providing method. The providing unit can also provide an optimal dataset based on user feedback. The providing unit can also continuously improve the providing method by reflecting user feedback. For example, the providing unit improves the providing method by reflecting user feedback when providing a dataset. In this way, the providing method can be continuously improved by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback into the generation AI and cause the generation AI to improve the providing method.

[0056] The providing unit can select the optimal providing method by taking into account the user's geographical location information when providing the dataset. The providing unit, for example, provides the optimal dataset based on the user's geographical location information. The providing unit can also provide a dataset specialized for a region based on the user's geographical location information. The providing unit can also select the optimal providing method by taking into account the user's geographical location information. For example, the providing unit selects the optimal providing method by taking into account the user's geographical location information when providing the dataset. This makes it possible to provide information specialized for a region by taking into account the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal providing method.

[0057] When providing a dataset, the providing unit can select the optimal providing method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a dataset that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a dataset optimized for a larger screen. Furthermore, if the user is using a desktop, the providing unit can provide a detailed dataset. For example, when providing a dataset, the providing unit selects the optimal providing method by taking into account the user's device information. This allows information to be provided in an optimal format by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal providing method.

[0058] The providing unit can customize the content to be provided according to the user's level of expertise when providing a dataset. For example, if the user is a beginner, the providing unit can provide a basic dataset. Furthermore, if the user is an intermediate user, the providing unit can provide a detailed dataset. Furthermore, if the user is an advanced user, the providing unit can provide a specialized dataset. For example, the providing unit customizes the content to be provided according to the user's level of expertise when providing a dataset. This allows optimal information to be provided by customizing the content to be provided according to the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's level of expertise to the generation AI and cause the generation AI to customize the content to be provided.

[0059] When providing a dataset, the providing unit can select the optimal providing method by referring to the user's past usage history. For example, the providing unit analyzes the user's past usage history and provides the optimal dataset. The providing unit can also provide a similar dataset based on a dataset the user has used in the past. The providing unit can also select the optimal providing method by referring to the user's past usage history. For example, when providing a dataset, the providing unit selects the optimal providing method by referring to the user's past usage history. This makes it possible to provide optimal information by referring to the user's past usage history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past usage history into the generation AI and cause the generation AI to select the optimal providing method.

[0060] The providing unit can customize the provided content based on the user's current project or area of ​​interest when providing a dataset. For example, the providing unit can preferentially provide a dataset related to the user's current project. The providing unit can also provide an optimal dataset based on the user's area of ​​interest. The providing unit can also customize the provided content according to the user's project or area of ​​interest. For example, the providing unit customizes the provided content based on the user's current project or area of ​​interest when providing a dataset. This allows optimal information to be provided by customizing the provided content based on the user's current project or area of ​​interest. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current project or area of ​​interest into the generation AI and cause the generation AI to customize the provided content.

[0061] The providing unit can improve the providing method by reflecting user feedback when providing a dataset. The providing unit, for example, collects user feedback and improves the providing method. The providing unit can also provide an optimal dataset based on user feedback. The providing unit can also continuously improve the providing method by reflecting user feedback. For example, the providing unit improves the providing method by reflecting user feedback when providing a dataset. In this way, the providing method can be continuously improved by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback into the generation AI and cause the generation AI to improve the providing method.

[0062] The providing unit can select the optimal providing method by taking into account the user's geographical location information when providing the dataset. The providing unit, for example, provides the optimal dataset based on the user's geographical location information. The providing unit can also provide a dataset specialized for a region based on the user's geographical location information. The providing unit can also select the optimal providing method by taking into account the user's geographical location information. For example, the providing unit selects the optimal providing method by taking into account the user's geographical location information when providing the dataset. This makes it possible to provide information specialized for a region by taking into account the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal providing method.

[0063] When providing a dataset, the providing unit can select the optimal providing method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a dataset that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a dataset optimized for a larger screen. Furthermore, if the user is using a desktop, the providing unit can provide a detailed dataset. For example, when providing a dataset, the providing unit selects the optimal providing method by taking into account the user's device information. This allows information to be provided in an optimal format by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal providing method.

[0064] The providing unit can customize the content to be provided according to the user's level of expertise when providing a dataset. For example, if the user is a beginner, the providing unit can provide a basic dataset. Furthermore, if the user is an intermediate user, the providing unit can provide a detailed dataset. Furthermore, if the user is an advanced user, the providing unit can provide a specialized dataset. For example, the providing unit customizes the content to be provided according to the user's level of expertise when providing a dataset. This allows optimal information to be provided by customizing the content to be provided according to the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's level of expertise to the generation AI and cause the generation AI to customize the content to be provided.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] When collecting data, the collection unit can evaluate the reliability of the websites from which data is to be collected and prioritize collecting data from sites whose reliability has been confirmed. For example, it can analyze the websites' past data provision history and prioritize selecting highly reliable sites. It can also check website operator information and prioritize collecting data from highly reliable operators. It can also check the website's security certificate and collect data from highly reliable sites. This allows the quality of data to be improved by collecting data from highly reliable sites.

[0067] When collecting data, the collection unit can select an appropriate collection method depending on the type of data to be collected. For example, in the case of text data, web scraping technology can be used to efficiently collect data. In addition, in the case of image data, image recognition technology can be used to automatically collect the necessary images. Furthermore, in the case of video data, video analysis technology can be used to extract and collect only the necessary parts. This allows for efficient data collection by selecting the optimal collection method depending on the type of data.

[0068] When collecting data, the collection unit can optimize the collection schedule by taking into account the update frequency of the data to be collected. For example, data can be collected periodically from news sites that are updated frequently. Data can also be collected periodically from blog sites that are updated infrequently. Furthermore, data can be collected in real time from social networking sites that are updated irregularly. This allows for efficient data collection by optimizing the collection schedule by taking into account the update frequency of the data.

[0069] The tagging unit can adjust the level of detail of tags based on the content of the dataset during tagging. For example, if the dataset contains detailed information, detailed tagging can be performed to provide a wealth of information. Alternatively, if the dataset contains concise information, concise tagging can be performed to provide the information quickly. Furthermore, tagging with an appropriate level of detail can be performed depending on the content of the dataset. As a result, by adjusting the level of detail of tags based on the content of the dataset, appropriate information can be provided.

[0070] The tagging unit can apply different tagging algorithms depending on the category of the dataset during tagging. For example, in the case of text data, tagging can be performed using natural language processing technology. In addition, in the case of image data, tagging can also be performed using image recognition technology. Furthermore, in the case of video data, tagging can also be performed using video analysis technology. In this way, by applying different tagging algorithms depending on the category of the dataset, tagging can be performed efficiently.

[0071] The tagging unit can improve the accuracy of tagging by referring to past tagging results. For example, the tagging unit analyzes past tagging results and applies an algorithm that improves tagging accuracy. Furthermore, the tagging unit can efficiently tag similar data sets based on past tagging results. Furthermore, the tagging unit can maintain consistency by referring to past tagging results. Thus, the accuracy of tagging can be improved by referring to past tagging results.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The collection unit collects a dataset. For example, the collection unit uses crawling technology to automatically collect data from the web. The collection unit can also collect data using APIs. For example, the collection unit collects various types of data, such as news articles, blog posts, and social media posts. Step 2: The tagging unit tags the dataset collected by the collection unit in accordance with laws, regulations, and guidelines. The tagging unit assigns tags to the dataset based on, for example, the laws, regulations, and guidelines of each country. For example, tags may include information such as whether the dataset complies with the laws and regulations of a specific country or whether the contents of the dataset are ethically sound. Step 3: The update unit updates the tags of the dataset tagged by the tagging unit. For example, when new laws and regulations or guidelines are enacted, the update unit automatically updates the tags of the dataset. For example, the update unit updates the tags of the dataset based on the new laws and regulations. Step 4: The providing unit provides the dataset tagged by the tagging unit. For example, the providing unit assigns a specific identification tag to content created by the generation AI, preventing the dataset from being used as training data. For example, the providing unit assigns a specific identification tag to content created by the generation AI, preventing the dataset from being used as training data. This allows the dataset hub system according to the embodiment to reduce legal risks for AI developers and enable them to develop AI using appropriate datasets. For example, the providing unit records the origin and content of datasets in detail, allowing AI developers to easily check whether they comply with legal regulations. This allows AI developers to always use datasets that comply with the latest legal regulations.

[0074] (Example 2) A dataset hub system according to an embodiment of the present invention aggregates and appropriately tags datasets for AI training. The dataset hub system aggregates unorganized datasets collected from the web, tags each dataset based on laws, regulations, and guidelines, and updates and provides the dataset tags in response to changes in laws, regulations, and guidelines. For example, the dataset hub system aggregates datasets collected from the web. In doing so, it records the origin and content of each dataset in detail. This includes various types of datasets, such as news articles, blog posts, and social media posts. The dataset hub system then tags each dataset based on laws, regulations, and guidelines. For example, tags may include information such as whether the dataset complies with the laws and regulations of a particular country or whether the dataset's content is ethically sound. Furthermore, the dataset hub system provides a mechanism for updating dataset tags in response to changes in laws, regulations, and guidelines. For example, when new laws and regulations are enacted, the dataset tags are updated based on the new laws and regulations. The dataset hub system also appropriately tags the origin of each dataset to prevent content created by generative AI from being distributed on the web. For example, content created by generative AI can be tagged with a specific tag to prevent that dataset from being used as training data. This allows the dataset hub system to reduce legal risks for AI developers and enable them to develop AI using appropriate datasets. It also prevents performance degradation of generative AI, enabling the development of higher quality AI. This allows the dataset hub system to reduce legal risks for AI developers and enable them to develop AI using appropriate datasets. It also prevents performance degradation of generative AI, enabling the development of higher quality AI.

[0075] A dataset hub system according to an embodiment includes a collection unit, a tagging unit, an update unit, and a providing unit. The collection unit collects datasets. The collection unit may use, for example, crawling technology to automatically collect data from the web. The collection unit may also collect data using an API. For example, the collection unit collects various types of data, such as news articles, blog posts, and social media posts. The tagging unit tags the datasets collected by the collection unit in accordance with laws, regulations, and guidelines. The tagging unit assigns tags to datasets based on, for example, the laws, regulations, and guidelines of each country. For example, the tag assigns information such as whether the dataset complies with the laws and regulations of a specific country or whether the contents of the dataset are ethically sound. The update unit updates the tags of the datasets tagged by the tagging unit. For example, the update unit automatically updates the tags of the datasets when new laws, regulations, or guidelines are enacted. For example, the update unit updates the tags of the datasets based on the new laws and regulations. The providing unit provides the datasets tagged by the tagging unit. For example, the providing unit may assign a specific identification tag to content created by the generation AI, preventing the use of that dataset as training data. For example, the providing unit may assign a specific identification tag to content created by the generation AI, preventing the use of that dataset as training data. This allows the dataset hub system according to the embodiment to reduce legal risks for AI developers and enable them to develop AI using appropriate datasets. For example, the providing unit may record the origin and content of datasets in detail, allowing AI developers to easily check whether they comply with legal regulations. This allows AI developers to always use datasets that comply with the latest legal regulations.

[0076] The collection unit can use a means for automatically collecting data from the web. The collection unit can automatically collect data from the web using, for example, crawling technology. For example, the collection unit can periodically collect data from specific websites. The collection unit can also collect data using an API. For example, the collection unit can collect various types of data, such as news articles, blog posts, and social media posts. This allows for efficient aggregation of data sets by automatically collecting data from the web. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data collected using crawling technology into a generation AI and have the generation AI classify and filter the data.

[0077] The tagging unit can assign tags to datasets in accordance with the laws, regulations, and guidelines of each country. The tagging unit assigns tags to datasets based on, for example, the laws, regulations, and guidelines of each country. For example, the tagging unit assigns tags containing information such as whether the dataset complies with the laws and regulations of a specific country or whether the contents of the dataset are ethically sound. The tagging unit can also use AI to analyze the contents of the dataset and determine whether the dataset complies with laws, regulations, and guidelines. For example, the tagging unit can use a generation AI to analyze the contents of the dataset and determine whether the dataset complies with laws and regulations. This allows tagging based on the laws, regulations, and guidelines of each country to reduce legal risks. Some or all of the above-mentioned processing in the tagging unit may be performed using AI or without AI. For example, the tagging unit can input the contents of the dataset into a generation AI and have the generation AI determine whether the dataset complies with laws and regulations.

[0078] The update unit can automatically update the tags of the dataset when new laws, regulations, or guidelines are enacted. For example, when new laws, regulations, or guidelines are enacted, the update unit automatically updates the tags of the dataset. For example, the update unit updates the tags of the dataset based on the new laws, regulations, or guidelines. The update unit can also use AI to detect changes in laws, regulations, or guidelines and update the tags of the dataset. For example, the update unit uses a generation AI to detect changes in laws, regulations, or guidelines and update the tags of the dataset. This allows the tags to be updated based on new laws, regulations, or guidelines, so that a dataset that complies with the latest laws and regulations can always be used. Some or all of the above-mentioned processing in the update unit may be performed using AI, for example, or may be performed without using AI. For example, the update unit can input changes in laws, regulations, or guidelines into the generation AI and cause the generation AI to update the tags.

[0079] The providing unit can assign a specific identification tag to content created by the generation AI and prevent the data set from being used as training data. For example, the providing unit assigns a specific identification tag to content created by the generation AI and prevents the data set from being used as training data. For example, the providing unit assigns a specific identification tag to content created by the generation AI and prevents the data set from being used as training data. The providing unit can also use AI to detect content created by the generation AI and assign an identification tag to it. For example, the providing unit uses the generation AI to detect content created by the generation AI and assign an identification tag to it. This prevents the content created by the generation AI from being used as training data, thereby preventing overlearning and performance degradation of the generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input content created by the generation AI to the generation AI and have the generation AI assign an identification tag.

[0080] The providing unit can specifically record the origin and content of the dataset, allowing the AI ​​developer to easily check whether the dataset complies with laws and regulations. For example, the providing unit can specifically record the origin and content of the dataset, allowing the AI ​​developer to easily check whether the dataset complies with laws and regulations. For example, the providing unit can record the origin and content of the dataset in detail, allowing the AI ​​developer to easily check whether the dataset complies with laws and regulations. The providing unit can also use AI to analyze the origin and content of the dataset and determine whether the dataset complies with laws and regulations. For example, the providing unit can use a generation AI to analyze the origin and content of the dataset and determine whether the dataset complies with laws and regulations. In this way, by recording the origin and content of the dataset in detail, the AI ​​developer can easily check whether the dataset complies with laws and regulations. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the origin and content of the dataset into the generation AI and have the generation AI determine whether the dataset complies with laws and regulations.

[0081] The collection unit can analyze the user's emotions and adjust the timing of data collection based on the analyzed user emotions. For example, if the user is feeling stressed, the collection unit temporarily stops data collection and resumes it when the user is relaxed. Furthermore, if the user is concentrating, the collection unit can quickly collect data and efficiently collect data. Furthermore, if the user is tired, the collection unit can reduce the frequency of data collection to reduce the burden on the user. For example, the collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. This reduces the burden on the user by adjusting the timing of data collection based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0082] When collecting data, the collection unit can evaluate the reliability of the websites from which data is collected and prioritize collecting data from websites whose reliability is confirmed. For example, the collection unit can analyze the websites' past data provision history and prioritize selecting highly reliable sites. The collection unit can also check website operator information and prioritize collecting data from highly reliable operators. The collection unit can also check the website's security certificate and collect data from highly reliable sites. For example, the collection unit can evaluate the reliability of websites and prioritize collecting data from highly reliable sites. This improves the quality of data by collecting data from highly reliable sites. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the reliability evaluation of websites into a generation AI and cause the generation AI to select highly reliable sites.

[0083] The collection unit can select an appropriate collection method depending on the type of data to be collected when collecting data. For example, in the case of text data, the collection unit can efficiently collect data using web scraping technology. In addition, in the case of image data, the collection unit can automatically collect necessary images using image recognition technology. In addition, in the case of video data, the collection unit can extract and collect only necessary portions using video analysis technology. For example, the collection unit can select the optimal collection method depending on the type of data to be collected (text, image, video, etc.) when collecting data. This allows for efficient data collection by selecting the optimal collection method depending on the type of data. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the type of data to be collected into the generation AI and have the generation AI select the optimal collection method.

[0084] The collection unit can optimize the collection schedule during data collection, taking into account the update frequency of the data to be collected. For example, the collection unit periodically collects data from frequently updated news sites. The collection unit can also periodically collect data from blog sites that are updated infrequently. The collection unit can also collect data in real time from SNS sites that are updated irregularly. For example, the collection unit optimizes the collection schedule during data collection, taking into account the update frequency of the data to be collected. In this way, data can be collected efficiently by optimizing the collection schedule taking into account the update frequency of the data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the update frequency of the data to the generation AI and cause the generation AI to optimize the collection schedule.

[0085] The collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions. For example, when the user is excited, the collection unit can prioritize collecting the latest trend information. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed analysis data. Furthermore, when the user is stressed, the collection unit can prioritize collecting simple, easy-to-understand data. For example, the collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions. This allows data to be collected according to the user's needs by prioritizing the data to be collected based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data to be collected.

[0086] The collection unit can adjust the collection range during data collection, taking into account the geographic distribution of the data to be collected. For example, if data from a specific region is required, the collection unit collects data only from that region. Furthermore, if global data is required, the collection unit can collect data evenly from multiple regions. The collection unit can also adjust the collection range based on the laws and regulations of a specific country. For example, the collection unit adjusts the collection range during data collection, taking into account the geographic distribution of the data to be collected. This allows data from required regions to be collected efficiently by adjusting the collection range taking into account the geographic distribution of the data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographic distribution of the data into the generation AI and have the generation AI adjust the collection range.

[0087] The collection unit can automatically determine the language of the data to be collected during data collection, and perform multilingual collection. For example, the collection unit can automatically determine the language of the data to be collected using language identification technology during data collection. The collection unit can also efficiently collect data using data collection means that support multiple languages. The collection unit can also perform optimal data collection by combining different collection means for each language. For example, the collection unit can automatically determine the language of the data to be collected during data collection, and perform multilingual collection. This allows efficient data collection by automatically determining the language of the data and performing multilingual collection. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the language of the data to be collected into a generation AI and cause the generation AI to perform multilingual collection.

[0088] The collection unit can evaluate the relevance of the data to be collected during data collection and prioritize collecting highly relevant data. For example, the collection unit can use a relevance assessment algorithm during data collection to evaluate the relevance of the data to be collected. The collection unit can also prioritize collecting highly relevant data, thereby efficiently collecting data. The collection unit can also prioritize collecting important data, leaving less relevant data for later. For example, the collection unit evaluates the relevance of the data to be collected during data collection and prioritizes collecting highly relevant data. In this way, by evaluating the relevance of the data and priority-collecting highly relevant data, data can be collected efficiently. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the relevance of the data to be collected to a generation AI and cause the generation AI to select highly relevant data.

[0089] The tagging unit can estimate the user's emotions and adjust the tagging expression method based on the estimated user's emotions. For example, if the user is relaxed, the tagging unit can perform detailed tagging to provide abundant information. If the user is in a hurry, the tagging unit can perform concise tagging to quickly provide information. If the user is excited, the tagging unit can perform visually appealing tagging to effectively convey information. For example, the tagging unit can estimate the user's emotions and adjust the tagging expression method based on the estimated user's emotions. By adjusting the tagging expression method based on the user's emotions, optimal information can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the tagging unit can be performed, for example, using AI or without AI. For example, the tagging unit can input the user's emotional data into the generation AI and have the generation AI adjust the tagging expression method.

[0090] The tagging unit can adjust the level of detail of the tags based on the content of the dataset during tagging. For example, if the dataset contains detailed information, the tagging unit can perform detailed tagging to provide abundant information. Furthermore, if the dataset contains concise information, the tagging unit can perform concise tagging to provide information quickly. The tagging unit can also perform tagging with an appropriate level of detail depending on the content of the dataset. For example, the tagging unit adjusts the level of detail of the tags based on the content of the dataset during tagging. This makes it possible to provide appropriate information by adjusting the level of detail of the tags based on the content of the dataset. Some or all of the above-described processing in the tagging unit may be performed using, for example, AI, or may be performed without using AI. For example, the tagging unit can input the content of the dataset to a generation AI and cause the generation AI to adjust the level of detail of the tags.

[0091] The tagging unit can apply different tagging algorithms depending on the category of the dataset during tagging. For example, in the case of text data, the tagging unit performs tagging using natural language processing technology. Furthermore, in the case of image data, the tagging unit can also perform tagging using image recognition technology. Furthermore, in the case of video data, the tagging unit can also perform tagging using video analysis technology. For example, the tagging unit applies different tagging algorithms depending on the category of the dataset during tagging. This allows for efficient tagging by applying different tagging algorithms depending on the category of the dataset. Some or all of the above-mentioned processing in the tagging unit may be performed using, for example, AI, or may be performed without using AI. For example, the tagging unit can input the category of the dataset to the generation AI and cause the generation AI to apply different tagging algorithms.

[0092] The tagging unit can improve the accuracy of tagging by referring to past tagging results during tagging. For example, the tagging unit analyzes past tagging results and applies an algorithm that improves the accuracy of tagging. The tagging unit can also efficiently tag similar data sets based on past tagging results. The tagging unit can also maintain consistency of tagging by referring to past tagging results. For example, the tagging unit improves the accuracy of tagging by referring to past tagging results during tagging. In this way, the accuracy of tagging is improved by referring to past tagging results. Some or all of the above-described processing in the tagging unit may be performed using, for example, AI, or may be performed without using AI. For example, the tagging unit can input past tagging results to a generation AI and cause the generation AI to improve the accuracy of tagging.

[0093] The tagging unit can estimate the user's emotions and adjust the length of tagging based on the estimated user emotions. For example, if the user is relaxed, the tagging unit can provide detailed tagging to provide abundant information. If the user is in a hurry, the tagging unit can provide concise tagging to quickly provide information. If the user is excited, the tagging unit can provide visually appealing tagging to effectively convey information. For example, the tagging unit can estimate the user's emotions and adjust the length of tagging based on the estimated user emotions. By adjusting the length of tagging based on the user's emotions, optimal information can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the tagging unit can be performed using, for example, an AI. For example, the tagging unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of tagging.

[0094] The tagging unit can determine the priority of tags based on the collection date of the dataset during tagging. For example, the tagging unit prioritizes tagging the most recent dataset to maintain the freshness of the information. The tagging unit can also appropriately tag older datasets to maintain the consistency of the information. The tagging unit can also adjust the priority of tagging depending on the collection date of the dataset. For example, the tagging unit determines the priority of tags based on the collection date of the dataset during tagging. This allows the most recent information to be provided preferentially by determining the priority of tags based on the collection date of the dataset. Some or all of the above-described processing in the tagging unit may be performed using, for example, AI, or may be performed without using AI. For example, the tagging unit can input the collection date of the dataset to the generation AI and cause the generation AI to determine the priority of tags.

[0095] The tagging unit can adjust the order of tags based on the relevance of the datasets during tagging. For example, the tagging unit prioritizes tagging for highly relevant datasets, thereby improving information efficiency. The tagging unit can also appropriately tag less relevant datasets to maintain information consistency. The tagging unit can also adjust the order of tags according to the relevance of the datasets. For example, the tagging unit adjusts the order of tags based on the relevance of the datasets during tagging. This allows information to be provided efficiently by adjusting the order of tags based on the relevance of the datasets. Some or all of the above-described processing in the tagging unit may be performed using, for example, AI, or may be performed without using AI. For example, the tagging unit can input the relevance of the datasets to a generation AI and cause the generation AI to adjust the order of tags.

[0096] The tagging unit can evaluate the reliability of the source of a dataset during tagging and assign specific tags to highly reliable datasets. For example, the tagging unit evaluates the reliability of the source of a dataset and assigns specific tags to highly reliable datasets. The tagging unit can also appropriately tag datasets with low reliability to maintain consistency of information. The tagging unit can also adjust the level of detail of tagging depending on the reliability of the source of the dataset. For example, the tagging unit evaluates the reliability of the source of a dataset during tagging and assigns specific tags to highly reliable datasets. This makes it possible to provide highly reliable information by evaluating the reliability of the source of the dataset. Some or all of the above-described processing in the tagging unit may be performed using, for example, AI, or may be performed without using AI. For example, the tagging unit can input the reliability of the source of a dataset to a generation AI and cause the generation AI to assign specific tags.

[0097] The update unit can estimate the user's emotions and adjust the timing of tag updates based on the estimated user emotions. For example, if the user is relaxed, the update unit can perform detailed tag updates to provide abundant information. If the user is in a hurry, the update unit can perform concise tag updates to quickly provide information. If the user is excited, the update unit can perform visually appealing tag updates to effectively convey information. For example, the update unit can estimate the user's emotions and adjust the timing of tag updates based on the estimated user emotions. By adjusting the timing of tag updates based on the user's emotions, optimal information can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the update unit can be performed using, for example, an AI. For example, the update unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of tag updates.

[0098] The update unit can select the optimal update method by referring to the change history of laws, regulations, and guidelines when updating tags. For example, the update unit can refer to the change history of laws, regulations, and guidelines and update tags based on the latest laws, regulations, and guidelines. The update unit can also refer to the change history of guidelines and update tags based on the latest guidelines. The update unit can also select the optimal tag update method based on the change history of laws, regulations, and guidelines. For example, the update unit can select the optimal update method by referring to the change history of laws, regulations, and guidelines when updating tags. This makes it possible to provide the latest information by referring to the change history of laws, regulations, and guidelines. Some or all of the above-mentioned processing in the update unit can be performed using, for example, AI, or can be performed without using AI. For example, the update unit can input the change history of laws, regulations, and guidelines into the generation AI and cause the generation AI to select the optimal update method.

[0099] The update unit can adjust the level of detail of the update based on the contents of the dataset when updating tags. For example, if the dataset contains detailed information, the update unit can perform a detailed tag update to provide abundant information. Furthermore, if the dataset contains concise information, the update unit can perform a concise tag update to provide quick information. The update unit can also perform tag updates with an appropriate level of detail depending on the contents of the dataset. For example, the update unit adjusts the level of detail of the update based on the contents of the dataset when updating tags. As a result, appropriate information can be provided by adjusting the level of detail of the update based on the contents of the dataset. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input the contents of the dataset to a generation AI and cause the generation AI to adjust the level of detail of the update.

[0100] The update unit can improve the accuracy of tags by referring to past update results when updating tags. For example, the update unit analyzes past tag update results and applies an algorithm that improves the accuracy of the update. The update unit can also efficiently perform tag updates on similar data sets based on past update results. The update unit can also maintain consistency of tag updates by referring to past update results. For example, the update unit improves the accuracy of tags by referring to past update results when updating tags. As a result, the accuracy of the update is improved by referring to past update results. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input past update results into a generation AI and cause the generation AI to improve the accuracy of the update.

[0101] The update unit can estimate the user's emotions and prioritize tag updates based on the estimated user emotions. For example, if the user is relaxed, the update unit can prioritize detailed tag updates to provide abundant information. Furthermore, if the user is in a hurry, the update unit can prioritize concise tag updates to quickly provide information. Furthermore, if the user is excited, the update unit can prioritize visually appealing tag updates to effectively convey information. For example, the update unit can estimate the user's emotions and prioritize tag updates based on the estimated user emotions. By prioritizing tag updates based on the user's emotions, optimal information can be provided to the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the update unit can be performed using, for example, an AI, or without an AI. For example, the update unit can input user emotion data to the generation AI and have the generation AI determine the priority of tag updates.

[0102] The update unit can adjust the update content when updating tags, taking into account regional differences in laws, regulations, and guidelines. For example, the update unit can perform appropriate tag updates for each region, taking into account differences in laws, regulations, and guidelines between countries. The update unit can also perform appropriate tag updates for each region, taking into account differences in guidelines between each region. The update unit can also adjust the tag update content based on regional differences in laws, regulations, and guidelines. For example, the update unit adjusts the update content when updating tags, taking into account regional differences in laws, regulations, and guidelines. This makes it possible to provide appropriate information by taking into account regional differences in laws, regulations, and guidelines. Some or all of the above-described processing in the update unit can be performed using, for example, AI, or can be performed without using AI. For example, the update unit can input regional differences in laws, regulations, and guidelines into a generation AI and cause the generation AI to adjust the update content.

[0103] The update unit can adjust the frequency of updates based on when the dataset was collected when updating tags. For example, the update unit frequently updates tags on the latest dataset to keep the information fresh. The update unit can also appropriately update tags on older datasets to keep the information consistent. The update unit can also adjust the frequency of tag updates based on when the dataset was collected. For example, the update unit adjusts the frequency of updates based on when the dataset was collected when updating tags. This makes it possible to provide the latest information by adjusting the frequency of updates based on when the dataset was collected. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input the time when the dataset was collected to the generation AI and cause the generation AI to adjust the update frequency.

[0104] The update unit can evaluate the reliability of the source of a dataset when updating tags and prioritize updating highly reliable datasets. For example, the update unit evaluates the reliability of the source of a dataset and prioritizes updating highly reliable datasets. The update unit can also appropriately perform tag updates on datasets with low reliability to maintain information consistency. The update unit can also adjust the priority of tag updates according to the reliability of the source of the dataset. For example, the update unit evaluates the reliability of the source of a dataset when updating tags and prioritizes updating highly reliable datasets. This makes it possible to provide highly reliable information by evaluating the reliability of the source of a dataset. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input the reliability of the source of a dataset to the generation AI and cause the generation AI to perform preferential updates.

[0105] The providing unit can estimate the user's emotions and adjust the way the dataset is provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide a detailed dataset to provide a wealth of information. If the user is in a hurry, the providing unit can provide a concise dataset to quickly provide information. If the user is excited, the providing unit can provide a visually appealing dataset to effectively convey information. For example, the providing unit can estimate the user's emotions and adjust the way the dataset is provided based on the estimated user's emotions. By adjusting the way the dataset is provided based on the user's emotions, optimal information can be provided to the user. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the data is provided.

[0106] When providing a dataset, the providing unit can select the optimal providing method by referring to the user's past usage history. For example, the providing unit analyzes the user's past usage history and provides the optimal dataset. The providing unit can also provide a similar dataset based on a dataset the user has used in the past. The providing unit can also select the optimal providing method by referring to the user's past usage history. For example, when providing a dataset, the providing unit selects the optimal providing method by referring to the user's past usage history. This makes it possible to provide optimal information by referring to the user's past usage history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past usage history into the generation AI and cause the generation AI to select the optimal providing method.

[0107] The providing unit can customize the provided content based on the user's current project or area of ​​interest when providing a dataset. For example, the providing unit can preferentially provide a dataset related to the user's current project. The providing unit can also provide an optimal dataset based on the user's area of ​​interest. The providing unit can also customize the provided content according to the user's project or area of ​​interest. For example, the providing unit customizes the provided content based on the user's current project or area of ​​interest when providing a dataset. This allows optimal information to be provided by customizing the provided content based on the user's current project or area of ​​interest. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current project or area of ​​interest into the generation AI and cause the generation AI to customize the provided content.

[0108] The providing unit can improve the providing method by reflecting user feedback when providing a dataset. The providing unit, for example, collects user feedback and improves the providing method. The providing unit can also provide an optimal dataset based on user feedback. The providing unit can also continuously improve the providing method by reflecting user feedback. For example, the providing unit improves the providing method by reflecting user feedback when providing a dataset. In this way, the providing method can be continuously improved by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback into the generation AI and cause the generation AI to improve the providing method.

[0109] The providing unit can estimate the user's emotions and adjust the order in which the data sets are provided based on the estimated user emotions. For example, if the user is relaxed, the providing unit can prioritize providing detailed data sets. Furthermore, if the user is in a hurry, the providing unit can prioritize providing concise data sets. Furthermore, if the user is excited, the providing unit can prioritize providing visually appealing data sets. For example, the providing unit can estimate the user's emotions and adjust the order in which the data sets are provided based on the estimated user emotions. By adjusting the order in which the data sets are provided based on the user's emotions, optimal information can be provided to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the order in which the data sets are provided.

[0110] The providing unit can select the optimal providing method by taking into account the user's geographical location information when providing the dataset. The providing unit, for example, provides the optimal dataset based on the user's geographical location information. The providing unit can also provide a dataset specialized for a region based on the user's geographical location information. The providing unit can also select the optimal providing method by taking into account the user's geographical location information. For example, the providing unit selects the optimal providing method by taking into account the user's geographical location information when providing the dataset. This makes it possible to provide information specialized for a region by taking into account the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal providing method.

[0111] When providing a dataset, the providing unit can select the optimal providing method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a dataset that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a dataset optimized for a larger screen. Furthermore, if the user is using a desktop, the providing unit can provide a detailed dataset. For example, when providing a dataset, the providing unit selects the optimal providing method by taking into account the user's device information. This allows information to be provided in an optimal format by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal providing method.

[0112] The providing unit can customize the content to be provided according to the user's level of expertise when providing a dataset. For example, if the user is a beginner, the providing unit can provide a basic dataset. Furthermore, if the user is an intermediate user, the providing unit can provide a detailed dataset. Furthermore, if the user is an advanced user, the providing unit can provide a specialized dataset. For example, the providing unit customizes the content to be provided according to the user's level of expertise when providing a dataset. This allows optimal information to be provided by customizing the content to be provided according to the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's level of expertise to the generation AI and cause the generation AI to customize the content to be provided.

[0113] The providing unit can estimate the user's emotions and adjust the way the dataset is provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide a detailed dataset to provide a wealth of information. If the user is in a hurry, the providing unit can provide a concise dataset to quickly provide information. If the user is excited, the providing unit can provide a visually appealing dataset to effectively convey information. For example, the providing unit can estimate the user's emotions and adjust the way the dataset is provided based on the estimated user's emotions. By adjusting the way the dataset is provided based on the user's emotions, optimal information can be provided to the user. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the data is provided.

[0114] When providing a dataset, the providing unit can select the optimal providing method by referring to the user's past usage history. For example, the providing unit analyzes the user's past usage history and provides the optimal dataset. The providing unit can also provide a similar dataset based on a dataset the user has used in the past. The providing unit can also select the optimal providing method by referring to the user's past usage history. For example, when providing a dataset, the providing unit selects the optimal providing method by referring to the user's past usage history. This makes it possible to provide optimal information by referring to the user's past usage history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past usage history into the generation AI and cause the generation AI to select the optimal providing method.

[0115] The providing unit can customize the provided content based on the user's current project or area of ​​interest when providing a dataset. For example, the providing unit can preferentially provide a dataset related to the user's current project. The providing unit can also provide an optimal dataset based on the user's area of ​​interest. The providing unit can also customize the provided content according to the user's project or area of ​​interest. For example, the providing unit customizes the provided content based on the user's current project or area of ​​interest when providing a dataset. This allows optimal information to be provided by customizing the provided content based on the user's current project or area of ​​interest. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current project or area of ​​interest into the generation AI and cause the generation AI to customize the provided content.

[0116] The providing unit can improve the providing method by reflecting user feedback when providing a dataset. The providing unit, for example, collects user feedback and improves the providing method. The providing unit can also provide an optimal dataset based on user feedback. The providing unit can also continuously improve the providing method by reflecting user feedback. For example, the providing unit improves the providing method by reflecting user feedback when providing a dataset. In this way, the providing method can be continuously improved by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback into the generation AI and cause the generation AI to improve the providing method.

[0117] The providing unit can estimate the user's emotions and adjust the order in which the data sets are provided based on the estimated user emotions. For example, if the user is relaxed, the providing unit can prioritize providing detailed data sets. Furthermore, if the user is in a hurry, the providing unit can prioritize providing concise data sets. Furthermore, if the user is excited, the providing unit can prioritize providing visually appealing data sets. For example, the providing unit can estimate the user's emotions and adjust the order in which the data sets are provided based on the estimated user emotions. By adjusting the order in which the data sets are provided based on the user's emotions, optimal information can be provided to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the order in which the data sets are provided.

[0118] The providing unit can select the optimal providing method by taking into account the user's geographical location information when providing the dataset. The providing unit, for example, provides the optimal dataset based on the user's geographical location information. The providing unit can also provide a dataset specialized for a region based on the user's geographical location information. The providing unit can also select the optimal providing method by taking into account the user's geographical location information. For example, the providing unit selects the optimal providing method by taking into account the user's geographical location information when providing the dataset. This makes it possible to provide information specialized for a region by taking into account the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal providing method.

[0119] When providing a dataset, the providing unit can select the optimal providing method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a dataset that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a dataset optimized for a larger screen. Furthermore, if the user is using a desktop, the providing unit can provide a detailed dataset. For example, when providing a dataset, the providing unit selects the optimal providing method by taking into account the user's device information. This allows information to be provided in an optimal format by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal providing method.

[0120] The providing unit can customize the content to be provided according to the user's level of expertise when providing a dataset. For example, if the user is a beginner, the providing unit can provide a basic dataset. Furthermore, if the user is an intermediate user, the providing unit can provide a detailed dataset. Furthermore, if the user is an advanced user, the providing unit can provide a specialized dataset. For example, the providing unit customizes the content to be provided according to the user's level of expertise when providing a dataset. This allows optimal information to be provided by customizing the content to be provided according to the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's level of expertise to the generation AI and cause the generation AI to customize the content to be provided. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned collection unit, tagging unit, update unit, and providing unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the tagging unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the update unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the providing unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, tagging unit, update unit, and providing unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the tagging unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the update unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the providing unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, tagging unit, update unit, and providing unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the tagging unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the update unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the providing unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, tagging unit, update unit, and providing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the tagging unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the update unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the providing unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0121] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0122] The collection unit can analyze the user's emotions and adjust the timing of data collection based on the analyzed user's emotions. For example, if the user is feeling stressed, data collection can be temporarily stopped and resumed when the user is relaxed. Also, if the user is concentrating, data collection can be performed quickly and efficiently. Furthermore, if the user is tired, the frequency of data collection can be reduced to reduce the burden on the user. In this way, the burden on the user can be reduced by adjusting the timing of data collection based on the user's emotions.

[0123] When collecting data, the collection unit can evaluate the reliability of the websites from which data is to be collected and prioritize collecting data from sites whose reliability has been confirmed. For example, it can analyze the websites' past data provision history and prioritize selecting highly reliable sites. It can also check website operator information and prioritize collecting data from highly reliable operators. It can also check the website's security certificate and collect data from highly reliable sites. This allows the quality of data to be improved by collecting data from highly reliable sites.

[0124] When collecting data, the collection unit can select an appropriate collection method depending on the type of data to be collected. For example, in the case of text data, web scraping technology can be used to efficiently collect data. In addition, in the case of image data, image recognition technology can be used to automatically collect the necessary images. Furthermore, in the case of video data, video analysis technology can be used to extract and collect only the necessary parts. This allows for efficient data collection by selecting the optimal collection method depending on the type of data.

[0125] When collecting data, the collection unit can optimize the collection schedule by taking into account the update frequency of the data to be collected. For example, data can be collected periodically from news sites that are updated frequently. Data can also be collected periodically from blog sites that are updated infrequently. Furthermore, data can be collected in real time from social networking sites that are updated irregularly. This allows for efficient data collection by optimizing the collection schedule by taking into account the update frequency of the data.

[0126] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. For example, if the user is excited, the latest trend information can be collected with priority. Also, if the user is relaxed, detailed analysis data can be collected with priority. Furthermore, if the user is stressed, simple and easy-to-understand data can be collected with priority. In this way, by determining the priority of data to be collected based on the user's emotions, data that meets the user's needs can be collected.

[0127] The tagging unit can estimate the user's emotions and adjust the tagging expression method based on the estimated user's emotions. For example, if the user is relaxed, detailed tagging can be performed to provide a wealth of information. If the user is in a hurry, concise tagging can be performed to quickly provide information. Furthermore, if the user is excited, visually appealing tagging can be performed to effectively convey information. In this way, by adjusting the tagging expression method based on the user's emotions, it is possible to provide the user with the most appropriate information.

[0128] The tagging unit can adjust the level of detail of tags based on the content of the dataset during tagging. For example, if the dataset contains detailed information, detailed tagging can be performed to provide a wealth of information. Alternatively, if the dataset contains concise information, concise tagging can be performed to provide the information quickly. Furthermore, tagging with an appropriate level of detail can be performed depending on the content of the dataset. As a result, by adjusting the level of detail of tags based on the content of the dataset, appropriate information can be provided.

[0129] The tagging unit can apply different tagging algorithms depending on the category of the dataset during tagging. For example, in the case of text data, tagging can be performed using natural language processing technology. In addition, in the case of image data, tagging can also be performed using image recognition technology. Furthermore, in the case of video data, tagging can also be performed using video analysis technology. In this way, by applying different tagging algorithms depending on the category of the dataset, tagging can be performed efficiently.

[0130] The tagging unit can improve the accuracy of tagging by referring to past tagging results. For example, the tagging unit analyzes past tagging results and applies an algorithm that improves tagging accuracy. Furthermore, the tagging unit can efficiently tag similar data sets based on past tagging results. Furthermore, the tagging unit can maintain consistency by referring to past tagging results. Thus, the accuracy of tagging can be improved by referring to past tagging results.

[0131] The update unit can estimate the user's emotions and adjust the timing of tag updates based on the estimated user emotions. For example, if the user is relaxed, detailed tag updates can be performed to provide a wealth of information. If the user is in a hurry, brief tag updates can be performed to quickly provide information. Furthermore, if the user is excited, visually appealing tag updates can be performed to effectively convey information. In this way, by adjusting the timing of tag updates based on the user's emotions, it is possible to provide optimal information for the user.

[0132] The processing flow of the second embodiment will be briefly explained below.

[0133] Step 1: The collection unit collects a dataset. For example, the collection unit uses crawling technology to automatically collect data from the web. The collection unit can also collect data using APIs. For example, the collection unit collects various types of data, such as news articles, blog posts, and social media posts. Step 2: The tagging unit tags the dataset collected by the collection unit in accordance with laws, regulations, and guidelines. The tagging unit assigns tags to the dataset based on, for example, the laws, regulations, and guidelines of each country. For example, tags may include information such as whether the dataset complies with the laws and regulations of a specific country or whether the contents of the dataset are ethically sound. Step 3: The update unit updates the tags of the dataset tagged by the tagging unit. For example, when new laws and regulations or guidelines are enacted, the update unit automatically updates the tags of the dataset. For example, the update unit updates the tags of the dataset based on the new laws and regulations. Step 4: The providing unit provides the dataset tagged by the tagging unit. For example, the providing unit assigns a specific identification tag to content created by the generation AI, preventing the dataset from being used as training data. For example, the providing unit assigns a specific identification tag to content created by the generation AI, preventing the dataset from being used as training data. This allows the dataset hub system according to the embodiment to reduce legal risks for AI developers and enable them to develop AI using appropriate datasets. For example, the providing unit records the origin and content of datasets in detail, allowing AI developers to easily check whether they comply with legal regulations. This allows AI developers to always use datasets that comply with the latest legal regulations.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0139] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0171] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0185] 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.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0192] 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."

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] [Explanation of symbols]

[0206] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit for collecting a dataset; a tagging unit that tags the dataset collected by the collection unit in accordance with laws, regulations, and guidelines; an updating unit that updates the tags of the data set tagged by the tagging unit; a providing unit that provides the data set tagged by the tagging unit. A system characterized by:

2. The collecting unit Use automated data collection methods from the web 2. The system of claim 1.

3. The tagging unit Tag datasets in accordance with local regulations and guidelines 2. The system of claim 1.

4. The update unit Automatically update dataset tags when new regulations or guidelines are enacted 2. The system of claim 1.

5. The providing unit Content created by generative AI will be tagged with specific identification tags, and the dataset will not be used as training data.

2. The system of claim 1.

6. The providing unit The origin and content of datasets will be specifically recorded, making it easier for AI developers to check whether they comply with legal regulations.

2. The system of claim 1.

7. The collecting unit Analyze user sentiment and adjust the timing of data collection based on the analyzed user sentiment.

2. The system of claim 1.

8. The collecting unit When collecting data, we evaluate the reliability of the websites from which data is collected and prioritize collecting data from sites that have been confirmed to be reliable.

2. The system of claim 1.

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