system

Abstract

The system according to the embodiment aims to convert news articles into a format that is easy for children to understand and to convey them visually. [Solution] A system according to an embodiment includes an acquisition unit, a conversion unit, and a visualization unit. The acquisition unit acquires news articles. The conversion unit analyzes the news articles acquired by the acquisition unit and converts them into a format suitable for children. The visualization unit visually conveys the content converted by the conversion unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that news articles aimed at adults are difficult for children to understand.

[0005] The system according to the embodiment aims to convert news articles into a format that is easy for children to understand and to convey them visually. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a conversion unit, and a visualization unit. The acquisition unit acquires news articles. The conversion unit analyzes the news articles acquired by the acquisition unit and converts them into content suitable for children. The visualization unit visually conveys the content converted by the conversion unit. [Effects of the Invention]

[0007] The system according to the embodiment can convert news articles into a form that is easy for children to understand and convey them visually. [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 news conversion system according to an embodiment of the present invention converts news articles intended for adults into a child-friendly format and visually conveys the information to children. The news conversion system acquires news articles, uses a generation AI to convert them into language that children can easily understand, and uses an image generation AI to visually convey important information using diagrams and illustrations. For example, the news conversion system acquires news articles. For example, the news conversion system can acquire online news and newspaper articles. Next, the news conversion system uses a generation AI to analyze the content of the news article and convert it into language that children can easily understand. For example, the term "economic growth" may be replaced with a simple expression such as "increasing money." Next, the news conversion system uses an image generation AI to visually convey important information using diagrams and illustrations. For example, to explain the concept of economic growth, an illustration of increasing money may be used. This allows the news conversion system to make the content of the news easier for children to understand. This helps children develop an interest in and deepen their understanding of social and world events through the news. For example, reading news articles encourages children to form their own opinions and increases opportunities to discuss them with family and friends. It is also expected that the knowledge learned through the news will be useful in school classes and everyday life.

[0029] A news conversion system according to an embodiment includes an acquisition unit, a conversion unit, and a visualization unit. The acquisition unit acquires news articles. Examples of news articles include, but are not limited to, online news, newspaper articles, and blog articles. The acquisition unit acquires the latest news articles from a news site, for example. The acquisition unit can also automatically acquire news articles using an RSS feed. The acquisition unit can also acquire news articles using an API. For example, the acquisition unit may acquire news articles of a specific category using a news site's API. The conversion unit uses a generation AI to analyze the news articles acquired by the acquisition unit and convert them into language that is easy for children to understand. The conversion unit can, for example, analyze the content of the news article using natural language processing technology. The conversion unit can also extract important words using keyword extraction technology and replace them with simpler words. The conversion unit can also analyze the structure of a sentence using grammar analysis technology and convert it into a form that is easy for children to understand. For example, the conversion unit can replace the term "economic growth" with a simpler expression such as "increasing money." The visualization unit uses image generation AI to visually convey the content converted by the conversion unit. For example, the visualization unit represents important information using diagrams or illustrations. The visualization unit can also visually convey information using animations. The visualization unit can also visually convey information using infographics. For example, the visualization unit uses an illustration of money increasing to explain the concept of economic growth. This makes it easier for children to understand the content of the news. Some or all of the above-described processing in the visualization unit may be performed using AI, for example, or may be performed without using AI. For example, the visualization unit can visually convey the content of a news article using illustrations generated by a generation AI.

[0030] The acquisition unit can acquire news articles. News articles include, but are not limited to, online news, newspaper articles, blog articles, etc. For example, the acquisition unit acquires the latest news articles from a news site. The acquisition unit can also automatically acquire news articles using an RSS feed. For example, the acquisition unit subscribes to the RSS feed of a specific news site and periodically acquires the latest news articles. The acquisition unit can also acquire news articles using an API. For example, the acquisition unit acquires news articles of a specific category using the news site's API. In this way, by acquiring the news articles, the system can provide material for converting news for children. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input news articles acquired using the news site's API into a generation AI to analyze the content of the news articles.

[0031] The conversion unit can analyze the content of a news article and convert it into language that is easy for children to understand. The conversion unit can analyze the content of a news article using, for example, natural language processing technology. For example, the conversion unit can analyze the sentences in a news article and extract important information. The conversion unit can also extract important words using keyword extraction technology and replace them with simpler words. For example, the conversion unit can replace the term "economic growth" with a simpler expression such as "increasing money." The conversion unit can also analyze the structure of a sentence using grammar analysis technology and convert it into a form that is easy for children to understand. For example, the conversion unit can divide long sentences into short sentences and express them concisely. This converts the news article into language that is easy for children to understand, making it easier for children to understand the news. Some or all of the above-mentioned processing in the conversion unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the conversion unit can input a news article into a generation AI, which then converts it into language that is easy for children to understand.

[0032] The visualization unit can visually convey important information using diagrams or illustrations. For example, the visualization unit expresses important information using diagrams or illustrations. For example, the visualization unit illustrates how money increases to explain the concept of economic growth. The visualization unit can also visually convey information using animations. For example, the visualization unit can explain how elections work using animations. The visualization unit can also visually convey information using infographics. For example, the visualization unit visualizes statistical data using graphs and charts. This makes it easier for children to understand the content of the news by visually conveying important information. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, image generation AI, or may be performed without using image generation AI. For example, the visualization unit can visually convey the content of a news article using illustrations generated by generation AI.

[0033] The conversion unit can replace difficult words and expressions with simpler words. For example, the conversion unit analyzes the content of a news article and replaces difficult words and expressions with simpler words. For example, the conversion unit replaces the term "economic growth" with a simpler expression such as "increasing money." The conversion unit can also replace technical terms with everyday words. For example, the conversion unit replaces the term "GDP" with a simpler expression such as "the amount of money in the entire country." The conversion unit can also divide long sentences into short sentences and express them concisely. For example, the conversion unit explains complex sentences in simpler terms. This makes it easier for children to understand the news by replacing difficult words and expressions with simpler terms. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit can input a news article into a generation AI, which then replaces difficult words and expressions with simpler terms.

[0034] The visualization unit can use illustrations to illustrate the process of money increasing to explain the concept of economic growth. For example, the visualization unit uses illustrations to illustrate the concept of economic growth. For example, the visualization unit explains the process of money increasing using step-by-step illustrations. The visualization unit can also visualize economic growth data using graphs. For example, the visualization unit uses a graph to show the increase in GDP. The visualization unit can also visually explain the process of economic growth using animations. For example, the visualization unit uses animations to illustrate the increase in money. This visually conveys the concept of economic growth, making it easier for children to understand basic economic concepts. Some or all of the above-described processing in the visualization unit may be performed using, for example, image generation AI, or may be performed without using image generation AI. For example, the visualization unit can visually convey the concept of economic growth using illustrations generated by generation AI.

[0035] The acquisition unit can analyze the user's past news browsing history and select the optimal acquisition method. The acquisition unit, for example, analyzes the user's past news browsing history and selects the optimal acquisition method. For example, the acquisition unit prioritizes acquiring news articles in a category that the user has frequently browsed in the past. The acquisition unit can also analyze trends in news articles that the user has browsed for a long time in the past and prioritize acquiring articles with similar content. The acquisition unit can also analyze trends in news articles that the user has highly rated in the past and prioritize acquiring related articles. In this way, by analyzing the user's past news browsing history, the optimal news article can be provided to the user. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the user's news browsing history data to a generation AI, which can select the optimal acquisition method.

[0036] The acquisition unit can filter news articles based on the user's current areas of interest when acquiring the news articles. For example, the acquisition unit can filter news articles based on the user's current areas of interest when acquiring the news articles. For example, the acquisition unit can prioritize acquiring news articles related to topics in which the user is currently interested. The acquisition unit can also filter and acquire related news articles based on keywords recently searched by the user. The acquisition unit can also prioritize acquiring news articles related to topics the user follows on social media. In this way, by filtering news articles based on the user's current areas of interest, it is possible to provide the user with news that is highly relevant to the user. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input user's area of ​​interest data to a generation AI, which can then filter the news articles.

[0037] The acquisition unit can select an appropriate acquisition means according to the user's input method when acquiring a news article. For example, the acquisition unit can select an appropriate acquisition means according to the user's input method when acquiring a news article. For example, if the user uses voice input, the acquisition unit can acquire the news article using voice recognition technology. Also, if the user uses text input, the acquisition unit can acquire the news article using a keyword search. Also, if the user uses image input, the acquisition unit can acquire related news articles using image recognition technology. This enables news acquisition that is easy for the user to use by selecting the optimal acquisition means according to the user's input method. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input the user's input data to a generation AI, which can select the optimal acquisition means.

[0038] The acquisition unit can prioritize acquiring highly relevant articles by taking into account the user's geographical location information when acquiring news articles. For example, the acquisition unit prioritizes acquiring highly relevant articles by taking into account the user's geographical location information when acquiring news articles. For example, the acquisition unit prioritizes acquiring news articles related to the area where the user is currently located. The acquisition unit can also prioritize acquiring news articles related to places the user has visited in the past. The acquisition unit can also prioritize acquiring news articles related to places the user plans to visit in the future. In this way, highly relevant news can be provided to the user by taking into account the user's geographical location information. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information data to a generation AI, which can select highly relevant news articles.

[0039] The acquisition unit can analyze the user's social media activity when acquiring a news article and acquire related articles. For example, the acquisition unit can analyze the user's social media activity when acquiring a news article and acquire related articles. For example, the acquisition unit can prioritize acquiring content related to news articles shared by the user on social media. The acquisition unit can also prioritize acquiring news articles shared by accounts the user follows on social media. The acquisition unit can also prioritize acquiring content related to news articles that the user has "liked" on social media. In this way, by analyzing the user's social media activity, it is possible to provide the user with news that is highly relevant to the user. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media data into a generation AI, which can select related news articles.

[0040] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring a news article. For example, the acquisition unit customizes the acquisition method by reflecting the user's past feedback when acquiring a news article. For example, the acquisition unit analyzes the trend of news articles that the user has previously rated highly and prioritizes acquiring related articles. The acquisition unit can also analyze the trend of news articles that the user has previously rated poorly and exclude similar content. The acquisition unit can also customize the acquisition method based on the content of news articles for which the user has previously provided feedback. In this way, the user's past feedback can be reflected to provide optimal news to the user. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's feedback data into a generation AI, which can customize the acquisition method.

[0041] The conversion unit can adjust the level of detail of the conversion based on the importance of the news article during conversion. For example, the conversion unit can adjust the level of detail of the conversion based on the importance of the news article during conversion. For example, the conversion unit can convert news articles with high importance in detail so that they are easy for children to understand. The conversion unit can also convert news articles with low importance in a concise manner so that only the main points are conveyed. The conversion unit can also gradually adjust the level of detail of the conversion depending on the importance. In this way, by adjusting the level of detail of the conversion based on the importance of the news article, important news can be conveyed in detail. Some or all of the above-mentioned processing in the conversion unit can be performed using, or without, a generation AI, for example. For example, the conversion unit can input importance data of the news article into the generation AI, and the generation AI can adjust the level of detail of the conversion.

[0042] The conversion unit can apply different conversion algorithms depending on the category of the news article during conversion. For example, the conversion unit can apply different conversion algorithms depending on the category of the news article during conversion. For example, the conversion unit can apply an algorithm that replaces technical terms with simpler terms to science and technology news articles. The conversion unit can also apply an algorithm that provides a simple explanation of how elections and laws work to political news articles. The conversion unit can also apply an algorithm that replaces economic terms with everyday language to economic news articles. In this way, by applying an appropriate conversion algorithm depending on the category of the news article, optimal conversion for each category is possible. Some or all of the above-mentioned processing in the conversion unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the conversion unit can input category data of the news article into a generation AI, which can then apply an appropriate conversion algorithm.

[0043] The conversion unit can improve the accuracy of the conversion by referring to the user's past conversion results. For example, the conversion unit can improve the accuracy of the conversion by referring to the user's past conversion results. For example, the conversion unit can refer to conversion results that the user has previously rated highly and apply a similar conversion method. The conversion unit can also refer to conversion results that the user has previously rated poorly and reflect improvements. The conversion unit can also analyze the user's past conversion results and suggest an optimal conversion method. By referring to the user's past conversion results, the conversion accuracy can be improved, and news that is easy for the user to understand can be provided. Some or all of the above-described processing in the conversion unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the conversion unit can input the user's past conversion result data into the generation AI, which can improve the conversion accuracy.

[0044] The conversion unit can determine the priority of conversion based on the publication time of the news article during conversion. The conversion unit, for example, determines the priority of conversion based on the publication time of the news article during conversion. For example, the conversion unit preferentially converts the latest news article and provides it quickly. The conversion unit can also prioritize the latest information, leaving older news articles for later. The conversion unit can also gradually adjust the priority of conversion according to the publication time. In this way, by determining the priority of conversion based on the publication time of the news article, the latest news can be provided quickly. Some or all of the above-mentioned processing in the conversion unit may be performed using, or without, a generation AI, for example. For example, the conversion unit can input publication time data of the news article into the generation AI, and the generation AI can determine the priority of conversion.

[0045] The conversion unit can adjust the order of conversion based on the relevance of the news articles during conversion. The conversion unit, for example, adjusts the order of conversion based on the relevance of the news articles during conversion. For example, the conversion unit prioritizes converting news articles related to topics in which the user is interested. The conversion unit can also prioritize highly relevant articles, leaving less relevant news articles for later. The conversion unit can also adjust the order of conversion in stages according to the relevance. In this way, by adjusting the order of conversion based on the relevance of the news articles, it is possible to provide news that is highly relevant to the user preferentially. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit can input relevance data of news articles into the generation AI, and the generation AI can adjust the order of conversion.

[0046] The conversion unit can adjust the use of technical terms during conversion according to the user's level of expertise. For example, the conversion unit can adjust the use of technical terms during conversion according to the user's level of expertise. For example, if the user has technical expertise, the conversion unit can use technical terms moderately. Furthermore, if the user does not have technical expertise, the conversion unit can replace technical terms with simpler words. Furthermore, the conversion unit can gradually adjust the use of technical terms according to the user's level of expertise. This allows for the provision of news that is easy for the user to understand by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the conversion unit can be performed using, or without, a generation AI. For example, the conversion unit can input the user's level of expertise data into the generation AI, which can then adjust the use of technical terms.

[0047] The visualization unit can adjust the level of detail of the visualization based on the importance of the news article during visualization. For example, the visualization unit can adjust the level of detail of the visualization based on the importance of the news article during visualization. For example, the visualization unit can visualize high-importance news articles using detailed illustrations. The visualization unit can also visualize low-importance news articles using simple illustrations. The visualization unit can also gradually adjust the level of detail of the visualization depending on the importance. In this way, important news can be visualized in detail by adjusting the level of detail of the visualization based on the importance of the news article. Some or all of the above-mentioned processing in the visualization unit can be performed using, for example, an image generation AI, or can be performed without using an image generation AI. For example, the visualization unit can input importance data of the news article to a generation AI, which can adjust the level of detail of the visualization.

[0048] The visualization unit can apply different visualization techniques depending on the category of the news article during visualization. For example, the visualization unit can apply different visualization techniques depending on the category of the news article during visualization. For example, the visualization unit can visualize an experiment using illustrations for a science and technology news article. The visualization unit can also visualize the election system using diagrams for a political news article. The visualization unit can also visualize economic growth using illustrations for an economic news article. In this way, by applying an appropriate visualization technique depending on the category of the news article, optimal visualization for each category is possible. Some or all of the above-mentioned processing in the visualization unit can be performed using, for example, an image generation AI, or can be performed without using an image generation AI. For example, the visualization unit can input category data of the news article into a generation AI, which can then apply an appropriate visualization technique.

[0049] The visualization unit can improve the accuracy of visualization by referring to the user's past visualization results during visualization. For example, the visualization unit can improve the accuracy of visualization by referring to the user's past visualization results during visualization. For example, the visualization unit can refer to visualization results that the user previously rated highly and apply a similar visualization technique. The visualization unit can also refer to visualization results that the user previously rated poorly and reflect improvements. The visualization unit can also analyze the user's past visualization results and suggest an optimal visualization technique. By referring to the user's past visualization results, the accuracy of visualization can be improved, and news that is easy for the user to understand can be provided. Some or all of the above-described processing in the visualization unit can be performed, for example, using an image generation AI, or can be performed without using an image generation AI. For example, the visualization unit can input the user's past visualization result data into a generation AI, which can improve the accuracy of the visualization.

[0050] The visualization unit can adjust the order of visualization based on the publication dates of the news articles during visualization. For example, the visualization unit can adjust the order of visualization based on the publication dates of the news articles during visualization. For example, the visualization unit can prioritize visualizing the latest news articles and provide them quickly. The visualization unit can also prioritize the latest information, leaving older news articles for later. The visualization unit can also gradually adjust the order of visualization based on the publication dates. In this way, by adjusting the order of visualization based on the publication dates of the news articles, the latest news can be provided quickly. Some or all of the above-mentioned processing in the visualization unit can be performed using, for example, an image generation AI, or can be performed without using an image generation AI. For example, the visualization unit can input publication date data of news articles into a generation AI, and the generation AI can adjust the order of visualization.

[0051] The visualization unit can adjust the visualization method based on the relevance of the news article during visualization. For example, the visualization unit can adjust the visualization method based on the relevance of the news article during visualization. For example, the visualization unit can prioritize visualizing news articles related to topics in which the user is interested. The visualization unit can also prioritize highly relevant articles while leaving less relevant news articles for later. The visualization unit can also gradually adjust the visualization method according to the relevance. In this way, by adjusting the visualization method based on the relevance of the news article, it is possible to provide news that is highly relevant to the user. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, an image generation AI, or may be performed without using an image generation AI. For example, the visualization unit can input relevance data of the news article into a generation AI, which can adjust the visualization method.

[0052] The visualization unit can customize the visualization method according to the user's level of visual comprehension when visualizing. For example, the visualization unit can customize the visualization method according to the user's level of visual comprehension when visualizing. For example, if the user has a high level of visual comprehension, the visualization unit can visualize using detailed illustrations. Also, if the user has a low level of visual comprehension, the visualization unit can visualize using simple illustrations. Also, the visualization unit can gradually adjust the visualization method according to the user's level of visual comprehension. In this way, by customizing the visualization method according to the user's level of visual comprehension, it is possible to provide news that is easy for the user to understand. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, an image generation AI, or may be performed without using an image generation AI. For example, the visualization unit can input the user's visual comprehension data into a generation AI, which can customize the visualization method.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The news conversion system may further include a voice conversion unit. The voice conversion unit may convert news articles into voice and provide them in a form that is easy for children to understand auditorily. For example, the voice conversion unit may analyze the content of a news article, convert it into simple language for children, and then convert the content into voice using voice synthesis technology. The voice conversion unit may also adjust the tone and speed of the voice to emphasize important parts of the news article. Furthermore, the voice conversion unit may use different character voices depending on the content of the news article. This may make it easier for children to understand the news auditorily.

[0055] The news conversion system may further include an interactive section. The interactive section provides an interface that allows children to ask questions and express their opinions about news articles. For example, the interactive section may provide a quiz about the content of a news article, allowing children to deepen their understanding by answering the quizzes. The interactive section may also provide a function that allows children to leave comments on news articles. The interactive section may also include a function that allows children to request additional information related to a news article. This allows children to actively participate in the news and deepen their understanding.

[0056] The news conversion system may further include a gamification unit. The gamification unit presents the content of news articles in a game format, allowing children to learn while having fun. For example, the gamification unit may provide a quiz game based on the content of the news article, allowing children to earn points for each correct answer. The gamification unit may also incorporate the content of the news article into a story-based game, allowing children to experience the events in the news as characters. Furthermore, the gamification unit may provide puzzles or mini-games related to the content of the news article, allowing children to learn while having fun reading the news.

[0057] The news conversion system may further include a multilingual unit. The multilingual unit translates news articles into multiple languages, allowing children to learn the news in different languages. For example, the multilingual unit may translate news articles into English, Spanish, Chinese, etc., allowing children to read the news in a language of their choice. The multilingual unit may also convert the content of the news article into audio in different languages, allowing children to learn aurally. The multilingual unit may also visualize the content of the news article in different languages, making it easier for children to understand visually. This allows children to learn the news in different languages.

[0058] The news conversion system may further include an education linkage unit. The education linkage unit may link with a school curriculum and relate the content of news articles to lessons. For example, the education linkage unit may provide news articles related to school lessons with priority, allowing children to review what they learned in class through the news. The education linkage unit may also provide news articles as teaching materials for teachers to use in class. Furthermore, the education linkage unit may suggest assignments or projects based on the content of the news articles, allowing children to study independently. This enhances the educational effect of news.

[0059] The news conversion system may further include a parent-child collaboration unit. The parent-child collaboration unit provides a function for parents and children to learn the news together. For example, the parent-child collaboration unit may provide a co-viewing mode for parents to read news articles together with their children. The parent-child collaboration unit may also provide guidelines for parents to explain the contents of news articles to their children. Furthermore, the parent-child collaboration unit may also provide a discussion function for parents and children to exchange opinions on news articles. This allows parents and children to learn the news together and deepen their communication.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The acquisition unit acquires news articles. News articles include online news, newspaper articles, blog articles, etc. The acquisition unit not only acquires the latest news articles from news sites, but can also automatically acquire news articles using RSS feeds or APIs. For example, it uses the API of a news site to acquire news articles in a specific category. Step 2: The conversion unit uses generation AI to analyze the news articles acquired by the acquisition unit and convert them into language that is easy for children to understand. The conversion unit analyzes the content of the news articles using natural language processing, keyword extraction, and grammar analysis technologies, and replaces important words with simpler terms. For example, it converts the term "economic growth" into a simpler expression such as "increasing money." Step 3: The visualization part uses image generation AI to visually communicate the content converted by the conversion part. The visualization part expresses important information through diagrams, illustrations, animations, and infographics. For example, to explain the concept of economic growth, the visualization part uses an illustration of money increasing.

[0062] (Example 2) A news conversion system according to an embodiment of the present invention converts news articles intended for adults into a child-friendly format and visually conveys the information to children. The news conversion system acquires news articles, uses a generation AI to convert them into language that children can easily understand, and uses an image generation AI to visually convey important information using diagrams and illustrations. For example, the news conversion system acquires news articles. For example, the news conversion system can acquire online news and newspaper articles. Next, the news conversion system uses a generation AI to analyze the content of the news article and convert it into language that children can easily understand. For example, the term "economic growth" may be replaced with a simple expression such as "increasing money." Next, the news conversion system uses an image generation AI to visually convey important information using diagrams and illustrations. For example, to explain the concept of economic growth, an illustration of increasing money may be used. This allows the news conversion system to make the content of the news easier for children to understand. This helps children develop an interest in and deepen their understanding of social and world events through the news. For example, reading news articles encourages children to form their own opinions and increases opportunities to discuss them with family and friends. It is also expected that the knowledge learned through the news will be useful in school classes and everyday life.

[0063] A news conversion system according to an embodiment includes an acquisition unit, a conversion unit, and a visualization unit. The acquisition unit acquires news articles. Examples of news articles include, but are not limited to, online news, newspaper articles, and blog articles. The acquisition unit acquires the latest news articles from a news site, for example. The acquisition unit can also automatically acquire news articles using an RSS feed. The acquisition unit can also acquire news articles using an API. For example, the acquisition unit may acquire news articles of a specific category using a news site's API. The conversion unit uses a generation AI to analyze the news articles acquired by the acquisition unit and convert them into language that is easy for children to understand. The conversion unit can, for example, analyze the content of the news article using natural language processing technology. The conversion unit can also extract important words using keyword extraction technology and replace them with simpler words. The conversion unit can also analyze the structure of a sentence using grammar analysis technology and convert it into a form that is easy for children to understand. For example, the conversion unit can replace the term "economic growth" with a simpler expression such as "increasing money." The visualization unit uses image generation AI to visually convey the content converted by the conversion unit. For example, the visualization unit represents important information using diagrams or illustrations. The visualization unit can also visually convey information using animations. The visualization unit can also visually convey information using infographics. For example, the visualization unit uses an illustration of money increasing to explain the concept of economic growth. This makes it easier for children to understand the content of the news. Some or all of the above-described processing in the visualization unit may be performed using AI, for example, or may be performed without using AI. For example, the visualization unit can visually convey the content of a news article using illustrations generated by a generation AI.

[0064] The acquisition unit can acquire news articles. News articles include, but are not limited to, online news, newspaper articles, blog articles, etc. For example, the acquisition unit acquires the latest news articles from a news site. The acquisition unit can also automatically acquire news articles using an RSS feed. For example, the acquisition unit subscribes to the RSS feed of a specific news site and periodically acquires the latest news articles. The acquisition unit can also acquire news articles using an API. For example, the acquisition unit acquires news articles of a specific category using the news site's API. In this way, by acquiring the news articles, the system can provide material for converting news for children. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input news articles acquired using the news site's API into a generation AI to analyze the content of the news articles.

[0065] The conversion unit can analyze the content of a news article and convert it into language that is easy for children to understand. The conversion unit can analyze the content of a news article using, for example, natural language processing technology. For example, the conversion unit can analyze the sentences in a news article and extract important information. The conversion unit can also extract important words using keyword extraction technology and replace them with simpler words. For example, the conversion unit can replace the term "economic growth" with a simpler expression such as "increasing money." The conversion unit can also analyze the structure of a sentence using grammar analysis technology and convert it into a form that is easy for children to understand. For example, the conversion unit can divide long sentences into short sentences and express them concisely. This converts the news article into language that is easy for children to understand, making it easier for children to understand the news. Some or all of the above-mentioned processing in the conversion unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the conversion unit can input a news article into a generation AI, which then converts it into language that is easy for children to understand.

[0066] The visualization unit can visually convey important information using diagrams or illustrations. For example, the visualization unit expresses important information using diagrams or illustrations. For example, the visualization unit illustrates how money increases to explain the concept of economic growth. The visualization unit can also visually convey information using animations. For example, the visualization unit can explain how elections work using animations. The visualization unit can also visually convey information using infographics. For example, the visualization unit visualizes statistical data using graphs and charts. This makes it easier for children to understand the content of the news by visually conveying important information. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, image generation AI, or may be performed without using image generation AI. For example, the visualization unit can visually convey the content of a news article using illustrations generated by generation AI.

[0067] The conversion unit can replace difficult words and expressions with simpler words. For example, the conversion unit analyzes the content of a news article and replaces difficult words and expressions with simpler words. For example, the conversion unit replaces the term "economic growth" with a simpler expression such as "increasing money." The conversion unit can also replace technical terms with everyday words. For example, the conversion unit replaces the term "GDP" with a simpler expression such as "the amount of money in the entire country." The conversion unit can also divide long sentences into short sentences and express them concisely. For example, the conversion unit explains complex sentences in simpler terms. This makes it easier for children to understand the news by replacing difficult words and expressions with simpler terms. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit can input a news article into a generation AI, which then replaces difficult words and expressions with simpler terms.

[0068] The visualization unit can use illustrations to illustrate the process of money increasing to explain the concept of economic growth. For example, the visualization unit uses illustrations to illustrate the concept of economic growth. For example, the visualization unit explains the process of money increasing using step-by-step illustrations. The visualization unit can also visualize economic growth data using graphs. For example, the visualization unit uses a graph to show the increase in GDP. The visualization unit can also visually explain the process of economic growth using animations. For example, the visualization unit uses animations to illustrate the increase in money. This visually conveys the concept of economic growth, making it easier for children to understand basic economic concepts. Some or all of the above-described processing in the visualization unit may be performed using, for example, image generation AI, or may be performed without using image generation AI. For example, the visualization unit can visually convey the concept of economic growth using illustrations generated by generation AI.

[0069] The acquisition unit can estimate the user's emotions and adjust the timing of news article acquisition based on the estimated user emotions. For example, the acquisition unit can estimate the user's emotions and adjust the timing of news article acquisition based on the estimated user emotions. For example, when the user is excited, the acquisition unit can increase the frequency of news article acquisition and quickly provide the latest information. Furthermore, when the user is relaxed, the acquisition unit can reduce the frequency of news article acquisition and provide information at a slower pace. Furthermore, when the user is stressed, the acquisition unit can temporarily stop news article acquisition and provide relaxing content. By adjusting the timing of news article acquisition according to the user's emotions, news can be provided to the user at the optimal timing. Emotion estimation is realized 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 acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input user emotion data into the generation AI, which can then adjust the timing of acquiring news articles.

[0070] The acquisition unit can analyze the user's past news browsing history and select the optimal acquisition method. The acquisition unit, for example, analyzes the user's past news browsing history and selects the optimal acquisition method. For example, the acquisition unit prioritizes acquiring news articles in a category that the user has frequently browsed in the past. The acquisition unit can also analyze trends in news articles that the user has browsed for a long time in the past and prioritize acquiring articles with similar content. The acquisition unit can also analyze trends in news articles that the user has highly rated in the past and prioritize acquiring related articles. In this way, by analyzing the user's past news browsing history, the optimal news article can be provided to the user. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the user's news browsing history data to a generation AI, which can select the optimal acquisition method.

[0071] The acquisition unit can filter news articles based on the user's current areas of interest when acquiring the news articles. For example, the acquisition unit can filter news articles based on the user's current areas of interest when acquiring the news articles. For example, the acquisition unit can prioritize acquiring news articles related to topics in which the user is currently interested. The acquisition unit can also filter and acquire related news articles based on keywords recently searched by the user. The acquisition unit can also prioritize acquiring news articles related to topics the user follows on social media. In this way, by filtering news articles based on the user's current areas of interest, it is possible to provide the user with news that is highly relevant to the user. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input user's area of ​​interest data to a generation AI, which can then filter the news articles.

[0072] The acquisition unit can select an appropriate acquisition means according to the user's input method when acquiring a news article. For example, the acquisition unit can select an appropriate acquisition means according to the user's input method when acquiring a news article. For example, if the user uses voice input, the acquisition unit can acquire the news article using voice recognition technology. Also, if the user uses text input, the acquisition unit can acquire the news article using a keyword search. Also, if the user uses image input, the acquisition unit can acquire related news articles using image recognition technology. This enables news acquisition that is easy for the user to use by selecting the optimal acquisition means according to the user's input method. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input the user's input data to a generation AI, which can select the optimal acquisition means.

[0073] The acquisition unit can estimate the user's emotions and determine the priority of news articles to be acquired based on the estimated user emotions. The acquisition unit, for example, estimates the user's emotions and determines the priority of news articles to be acquired based on the estimated user emotions. For example, when the user is excited, the acquisition unit can prioritize acquiring positive news articles. Furthermore, when the user is relaxed, the acquisition unit can prioritize acquiring news articles with relaxing content. Furthermore, when the user is stressed, the acquisition unit can prioritize acquiring news articles with stress-relieving content. In this way, by determining the priority of news articles according to the user's emotions, optimal news can be provided to the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the acquisition unit may be performed using an AI, for example, or without an AI. For example, the acquisition unit can input the user's emotion data into the generation AI, which can then determine the priority of news articles.

[0074] The acquisition unit can prioritize acquiring highly relevant articles by taking into account the user's geographical location information when acquiring news articles. For example, the acquisition unit prioritizes acquiring highly relevant articles by taking into account the user's geographical location information when acquiring news articles. For example, the acquisition unit prioritizes acquiring news articles related to the area where the user is currently located. The acquisition unit can also prioritize acquiring news articles related to places the user has visited in the past. The acquisition unit can also prioritize acquiring news articles related to places the user plans to visit in the future. In this way, highly relevant news can be provided to the user by taking into account the user's geographical location information. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information data to a generation AI, which can select highly relevant news articles.

[0075] The acquisition unit can analyze the user's social media activity when acquiring a news article and acquire related articles. For example, the acquisition unit can analyze the user's social media activity when acquiring a news article and acquire related articles. For example, the acquisition unit can prioritize acquiring content related to news articles shared by the user on social media. The acquisition unit can also prioritize acquiring news articles shared by accounts the user follows on social media. The acquisition unit can also prioritize acquiring content related to news articles that the user has "liked" on social media. In this way, by analyzing the user's social media activity, it is possible to provide the user with news that is highly relevant to the user. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media data into a generation AI, which can select related news articles.

[0076] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring a news article. For example, the acquisition unit customizes the acquisition method by reflecting the user's past feedback when acquiring a news article. For example, the acquisition unit analyzes the trend of news articles that the user has previously rated highly and prioritizes acquiring related articles. The acquisition unit can also analyze the trend of news articles that the user has previously rated poorly and exclude similar content. The acquisition unit can also customize the acquisition method based on the content of news articles for which the user has previously provided feedback. In this way, the user's past feedback can be reflected to provide optimal news to the user. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's feedback data into a generation AI, which can customize the acquisition method.

[0077] The conversion unit can estimate the user's emotion and adjust the conversion expression method based on the estimated user's emotion. The conversion unit, for example, estimates the user's emotion and adjusts the conversion expression method based on the estimated user's emotion. For example, if the user is relaxed, the conversion unit can convert the news article using a soft expression. If the user is excited, the conversion unit can convert the news article using a lively expression. If the user is stressed, the conversion unit can convert the news article using a calm expression. This allows the user to be provided with news that is easy to understand by adjusting the conversion expression method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the conversion unit can input the user's emotion data into the generation AI, and the generation AI can adjust the conversion expression method.

[0078] The conversion unit can adjust the level of detail of the conversion based on the importance of the news article during conversion. For example, the conversion unit can adjust the level of detail of the conversion based on the importance of the news article during conversion. For example, the conversion unit can convert news articles with high importance in detail so that they are easy for children to understand. The conversion unit can also convert news articles with low importance in a concise manner so that only the main points are conveyed. The conversion unit can also gradually adjust the level of detail of the conversion depending on the importance. In this way, by adjusting the level of detail of the conversion based on the importance of the news article, important news can be conveyed in detail. Some or all of the above-mentioned processing in the conversion unit can be performed using, or without, a generation AI, for example. For example, the conversion unit can input importance data of the news article into the generation AI, and the generation AI can adjust the level of detail of the conversion.

[0079] The conversion unit can apply different conversion algorithms depending on the category of the news article during conversion. For example, the conversion unit can apply different conversion algorithms depending on the category of the news article during conversion. For example, the conversion unit can apply an algorithm that replaces technical terms with simpler terms to science and technology news articles. The conversion unit can also apply an algorithm that provides a simple explanation of how elections and laws work to political news articles. The conversion unit can also apply an algorithm that replaces economic terms with everyday language to economic news articles. In this way, by applying an appropriate conversion algorithm depending on the category of the news article, optimal conversion for each category is possible. Some or all of the above-mentioned processing in the conversion unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the conversion unit can input category data of the news article into a generation AI, which can then apply an appropriate conversion algorithm.

[0080] The conversion unit can improve the accuracy of the conversion by referring to the user's past conversion results. For example, the conversion unit can improve the accuracy of the conversion by referring to the user's past conversion results. For example, the conversion unit can refer to conversion results that the user has previously rated highly and apply a similar conversion method. The conversion unit can also refer to conversion results that the user has previously rated poorly and reflect improvements. The conversion unit can also analyze the user's past conversion results and suggest an optimal conversion method. By referring to the user's past conversion results, the conversion accuracy can be improved, and news that is easy for the user to understand can be provided. Some or all of the above-described processing in the conversion unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the conversion unit can input the user's past conversion result data into the generation AI, which can improve the conversion accuracy.

[0081] The conversion unit can estimate the user's emotions and adjust the length of the conversion based on the estimated user emotions. The conversion unit, for example, estimates the user's emotions and adjusts the length of the conversion based on the estimated user emotions. For example, if the user is in a hurry, the conversion unit can perform a short, to-the-point conversion. If the user is relaxed, the conversion unit can perform a longer conversion with detailed explanations. If the user is excited, the conversion unit can perform a conversion with visually stimulating effects. By adjusting the length of the conversion according to the user's emotions, news of an optimal length 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, for example, 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 conversion unit can be performed using, for example, the generation AI. For example, the conversion unit can input the user's emotion data into the generation AI, which can then adjust the length of the conversion.

[0082] The conversion unit can determine the priority of conversion based on the publication time of the news article during conversion. The conversion unit, for example, determines the priority of conversion based on the publication time of the news article during conversion. For example, the conversion unit preferentially converts the latest news article and provides it quickly. The conversion unit can also prioritize the latest information, leaving older news articles for later. The conversion unit can also gradually adjust the priority of conversion according to the publication time. In this way, by determining the priority of conversion based on the publication time of the news article, the latest news can be provided quickly. Some or all of the above-mentioned processing in the conversion unit may be performed using, or without, a generation AI, for example. For example, the conversion unit can input publication time data of the news article into the generation AI, and the generation AI can determine the priority of conversion.

[0083] The conversion unit can adjust the order of conversion based on the relevance of the news articles during conversion. The conversion unit, for example, adjusts the order of conversion based on the relevance of the news articles during conversion. For example, the conversion unit prioritizes converting news articles related to topics in which the user is interested. The conversion unit can also prioritize highly relevant articles, leaving less relevant news articles for later. The conversion unit can also adjust the order of conversion in stages according to the relevance. In this way, by adjusting the order of conversion based on the relevance of the news articles, it is possible to provide news that is highly relevant to the user preferentially. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit can input relevance data of news articles into the generation AI, and the generation AI can adjust the order of conversion.

[0084] The conversion unit can adjust the use of technical terms during conversion according to the user's level of expertise. For example, the conversion unit can adjust the use of technical terms during conversion according to the user's level of expertise. For example, if the user has technical expertise, the conversion unit can use technical terms moderately. Furthermore, if the user does not have technical expertise, the conversion unit can replace technical terms with simpler words. Furthermore, the conversion unit can gradually adjust the use of technical terms according to the user's level of expertise. This allows for the provision of news that is easy for the user to understand by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the conversion unit can be performed using, or without, a generation AI. For example, the conversion unit can input the user's level of expertise data into the generation AI, which can then adjust the use of technical terms.

[0085] The visualization unit can estimate the user's emotions and adjust the visualization method based on the estimated user emotions. For example, the visualization unit can estimate the user's emotions and adjust the visualization method based on the estimated user emotions. For example, if the user is relaxed, the visualization unit can visualize using illustrations with soft colors. If the user is excited, the visualization unit can visualize using illustrations with bright colors. If the user is stressed, the visualization unit can visualize using illustrations with calm colors. This allows the visualization method to be adjusted according to the user's emotions, thereby providing news that is visually easy for the user to understand. 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-mentioned processing in the visualization unit can be performed using, for example, an image generation AI, or can be performed without using an image generation AI. For example, the visualization unit can input the user's emotion data into the generation AI, which can adjust the visualization method.

[0086] The visualization unit can adjust the level of detail of the visualization based on the importance of the news article during visualization. For example, the visualization unit can adjust the level of detail of the visualization based on the importance of the news article during visualization. For example, the visualization unit can visualize high-importance news articles using detailed illustrations. The visualization unit can also visualize low-importance news articles using simple illustrations. The visualization unit can also gradually adjust the level of detail of the visualization depending on the importance. In this way, important news can be visualized in detail by adjusting the level of detail of the visualization based on the importance of the news article. Some or all of the above-mentioned processing in the visualization unit can be performed using, for example, an image generation AI, or can be performed without using an image generation AI. For example, the visualization unit can input importance data of the news article to a generation AI, which can adjust the level of detail of the visualization.

[0087] The visualization unit can apply different visualization techniques depending on the category of the news article during visualization. For example, the visualization unit can apply different visualization techniques depending on the category of the news article during visualization. For example, the visualization unit can visualize an experiment using illustrations for a science and technology news article. The visualization unit can also visualize the election system using diagrams for a political news article. The visualization unit can also visualize economic growth using illustrations for an economic news article. In this way, by applying an appropriate visualization technique depending on the category of the news article, optimal visualization for each category is possible. Some or all of the above-mentioned processing in the visualization unit can be performed using, for example, an image generation AI, or can be performed without using an image generation AI. For example, the visualization unit can input category data of the news article into a generation AI, which can then apply an appropriate visualization technique.

[0088] The visualization unit can improve the accuracy of visualization by referring to the user's past visualization results during visualization. For example, the visualization unit can improve the accuracy of visualization by referring to the user's past visualization results during visualization. For example, the visualization unit can refer to visualization results that the user previously rated highly and apply a similar visualization technique. The visualization unit can also refer to visualization results that the user previously rated poorly and reflect improvements. The visualization unit can also analyze the user's past visualization results and suggest an optimal visualization technique. By referring to the user's past visualization results, the accuracy of visualization can be improved, and news that is easy for the user to understand can be provided. Some or all of the above-described processing in the visualization unit can be performed, for example, using an image generation AI, or can be performed without using an image generation AI. For example, the visualization unit can input the user's past visualization result data into a generation AI, which can improve the accuracy of the visualization.

[0089] The visualization unit can estimate the user's emotions and determine the priority of visualization based on the estimated user emotions. The visualization unit, for example, estimates the user's emotions and determines the priority of visualization based on the estimated user emotions. For example, if the user is excited, the visualization unit can prioritize visually stimulating content to visualize. Furthermore, if the user is relaxed, the visualization unit can prioritize relaxing content to visualize. Furthermore, if the user is stressed, the visualization unit can prioritize stress-relieving content to visualize. This allows optimal visualization to be provided by determining the priority of visualization 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 visualization unit can be performed using, for example, an image generation AI, or can be performed without using an image generation AI. For example, the visualization unit can input the user's emotion data into the generation AI, which can then determine the priority of visualization.

[0090] The visualization unit can adjust the order of visualization based on the publication dates of the news articles during visualization. For example, the visualization unit can adjust the order of visualization based on the publication dates of the news articles during visualization. For example, the visualization unit can prioritize visualizing the latest news articles and provide them quickly. The visualization unit can also prioritize the latest information, leaving older news articles for later. The visualization unit can also gradually adjust the order of visualization based on the publication dates. In this way, by adjusting the order of visualization based on the publication dates of the news articles, the latest news can be provided quickly. Some or all of the above-mentioned processing in the visualization unit can be performed using, for example, an image generation AI, or can be performed without using an image generation AI. For example, the visualization unit can input publication date data of news articles into a generation AI, and the generation AI can adjust the order of visualization.

[0091] The visualization unit can adjust the visualization method based on the relevance of the news article during visualization. For example, the visualization unit can adjust the visualization method based on the relevance of the news article during visualization. For example, the visualization unit can prioritize visualizing news articles related to topics in which the user is interested. The visualization unit can also prioritize highly relevant articles while leaving less relevant news articles for later. The visualization unit can also gradually adjust the visualization method according to the relevance. In this way, by adjusting the visualization method based on the relevance of the news article, it is possible to provide news that is highly relevant to the user. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, an image generation AI, or may be performed without using an image generation AI. For example, the visualization unit can input relevance data of the news article into a generation AI, which can adjust the visualization method.

[0092] The visualization unit can customize the visualization method according to the user's level of visual comprehension when visualizing. For example, the visualization unit can customize the visualization method according to the user's level of visual comprehension when visualizing. For example, if the user has a high level of visual comprehension, the visualization unit can visualize using detailed illustrations. Also, if the user has a low level of visual comprehension, the visualization unit can visualize using simple illustrations. Also, the visualization unit can gradually adjust the visualization method according to the user's level of visual comprehension. In this way, by customizing the visualization method according to the user's level of visual comprehension, it is possible to provide news that is easy for the user to understand. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, an image generation AI, or may be performed without using an image generation AI. For example, the visualization unit can input the user's visual comprehension data into a generation AI, which can customize the visualization method. === Hard Collateral 1-1 === Each of the multiple elements, including the acquisition unit, conversion unit, and visualization unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit can acquire news articles using the communication I / F 44 of the smart device 14. The conversion unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the news articles using a generation AI and converts them into language that is easy for children to understand. The visualization unit is realized by the control unit 46A of the smart device 14, and uses an image generation AI to visually convey important information using diagrams and illustrations. === Hard Collateral 1-2 === Each of the multiple elements, including the acquisition unit, conversion unit, and visualization unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit can acquire news articles using the communication I / F 44 of the smart glasses 214. The conversion unit is realized by the specific processing unit 290 of the data processing device 12 and uses a generation AI to analyze the news articles and convert them into language that is easy for children to understand. The visualization unit is realized by the control unit 46A of the smart glasses 214 and uses an image generation AI to visually convey important information using diagrams and illustrations. === Hard Collateral 1-3 === Each of the multiple elements including the acquisition unit, conversion unit, and visualization unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the acquisition unit can acquire news articles using the communication I / F 44 of the headset-type terminal 314. The conversion unit is realized by the specific processing unit 290 of the data processing device 12, and uses a generation AI to analyze the news articles and convert them into language that is easy for children to understand. The visualization unit is realized by the control unit 46A of the headset-type terminal 314, and uses an image generation AI to visually convey important information using diagrams and illustrations. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, conversion unit, and visualization unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit can acquire news articles using the communication I / F 44 of the robot 414. The conversion unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the news articles using a generation AI and converts them into language that is easy for children to understand. The visualization unit is realized by the control unit 46A of the robot 414, and uses an image generation AI to visually convey important information using diagrams and illustrations.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The news conversion system may further include a voice conversion unit. The voice conversion unit may convert news articles into voice and provide them in a form that is easy for children to understand auditorily. For example, the voice conversion unit may analyze the content of a news article, convert it into simple language for children, and then convert the content into voice using voice synthesis technology. The voice conversion unit may also adjust the tone and speed of the voice to emphasize important parts of the news article. Furthermore, the voice conversion unit may use different character voices depending on the content of the news article. This may make it easier for children to understand the news auditorily.

[0095] The news conversion system may further include an interactive section. The interactive section provides an interface that allows children to ask questions and express their opinions about news articles. For example, the interactive section may provide a quiz about the content of a news article, allowing children to deepen their understanding by answering the quizzes. The interactive section may also provide a function that allows children to leave comments on news articles. The interactive section may also include a function that allows children to request additional information related to a news article. This allows children to actively participate in the news and deepen their understanding.

[0096] The news conversion system may further include an emotion feedback unit. The emotion feedback unit may record the emotion a child feels after reading a news article and reflect that emotion in the next news presentation. For example, the emotion feedback unit may provide an interface for the child to select the emotion they felt after reading a news article. The emotion feedback unit may also analyze the child's emotion data and prioritize the content and expression style preferred by the child when providing news the next time. Furthermore, if the child feels strong emotion toward a particular news article, the emotion feedback unit may also provide related news articles based on that emotion. This makes it possible to provide news that is tailored to the child's emotions.

[0097] The news conversion system may further include a gamification unit. The gamification unit presents the content of news articles in a game format, allowing children to learn while having fun. For example, the gamification unit may provide a quiz game based on the content of the news article, allowing children to earn points for each correct answer. The gamification unit may also incorporate the content of the news article into a story-based game, allowing children to experience the events in the news as characters. Furthermore, the gamification unit may provide puzzles or mini-games related to the content of the news article, allowing children to learn while having fun reading the news.

[0098] The news conversion system may further include an emotion estimation unit. The emotion estimation unit analyzes the child's facial expression and tone of voice while reading a news article and estimates the emotion in real time. For example, the emotion estimation unit may use a camera to analyze the child's facial expression and estimate emotions such as joy or surprise. The emotion estimation unit may also use a microphone to analyze the child's tone of voice and estimate emotions such as excitement or calmness. Furthermore, the emotion estimation unit may adjust the content and expression of the news article in real time based on the estimated emotion. This makes it possible to provide news that suits the child's emotions.

[0099] The news conversion system may further include a multilingual unit. The multilingual unit translates news articles into multiple languages, allowing children to learn the news in different languages. For example, the multilingual unit may translate news articles into English, Spanish, Chinese, etc., allowing children to read the news in a language of their choice. The multilingual unit may also convert the content of the news article into audio in different languages, allowing children to learn aurally. The multilingual unit may also visualize the content of the news article in different languages, making it easier for children to understand visually. This allows children to learn the news in different languages.

[0100] The news conversion system may further include an emotion estimation unit. The emotion estimation unit estimates the emotion a child feels after reading a news article and adjusts the next news provision based on the emotion. For example, the emotion estimation unit estimates the emotion a child feels after reading a news article and prioritizes content and expression methods preferred by the child when providing the next news. In addition, if a child feels strong emotion toward a particular news article, the emotion estimation unit may also provide related news articles based on the emotion. Furthermore, the emotion estimation unit may analyze the child's emotion data and adjust the timing and frequency of news provision. This makes it possible to provide news according to the child's emotions.

[0101] The news conversion system may further include an education linkage unit. The education linkage unit may link with a school curriculum and relate the content of news articles to lessons. For example, the education linkage unit may provide news articles related to school lessons with priority, allowing children to review what they learned in class through the news. The education linkage unit may also provide news articles as teaching materials for teachers to use in class. Furthermore, the education linkage unit may suggest assignments or projects based on the content of the news articles, allowing children to study independently. This enhances the educational effect of news.

[0102] The news conversion system may further include an emotion estimation unit. The emotion estimation unit estimates the emotion of a child while reading a news article in real time and adjusts the news provided based on the emotion. For example, the emotion estimation unit estimates the emotion felt by a child while reading a news article in real time and provides content that the child is interested in preferentially. The emotion estimation unit may also adjust the way the news article is presented in real time based on the emotion felt by the child while reading the news article. Furthermore, the emotion estimation unit may record the emotion felt by the child while reading the news article and reflect the recorded emotion in the next news provided. This makes it possible to provide news that is tailored to the child's emotion.

[0103] The news conversion system may further include a parent-child collaboration unit. The parent-child collaboration unit provides a function for parents and children to learn the news together. For example, the parent-child collaboration unit may provide a co-viewing mode for parents to read news articles together with their children. The parent-child collaboration unit may also provide guidelines for parents to explain the contents of news articles to their children. Furthermore, the parent-child collaboration unit may also provide a discussion function for parents and children to exchange opinions on news articles. This allows parents and children to learn the news together and deepen their communication.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The acquisition unit acquires news articles. News articles include online news, newspaper articles, blog articles, etc. The acquisition unit not only acquires the latest news articles from news sites, but can also automatically acquire news articles using RSS feeds or APIs. For example, it uses the API of a news site to acquire news articles in a specific category. Step 2: The conversion unit uses generation AI to analyze the news articles acquired by the acquisition unit and convert them into language that is easy for children to understand. The conversion unit analyzes the content of the news articles using natural language processing, keyword extraction, and grammar analysis technologies, and replaces important words with simpler terms. For example, it converts the term "economic growth" into a simpler expression such as "increasing money." Step 3: The visualization part uses image generation AI to visually communicate the content converted by the conversion part. The visualization part expresses important information through diagrams, illustrations, animations, and infographics. For example, to explain the concept of economic growth, the visualization part uses an illustration of money increasing.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit for acquiring news articles; a conversion unit that analyzes the news articles acquired by the acquisition unit and converts them into articles suitable for children; a visualization unit that visually conveys the content converted by the conversion unit. A system characterized by:

2. The acquisition unit Get news articles 2. The system of claim 1.

3. The conversion unit Analyze the content of news articles and translate them into language that children can easily understand 2. The system of claim 1.

4. The visualization unit Visually convey important information through diagrams or illustrations 2. The system of claim 1.

5. The conversion unit Replace difficult words and expressions with simpler terms 2. The system of claim 1.

6. The visualization unit Illustrate the process of money growing to explain the concept of economic growth 2. The system of claim 1.

7. The acquisition unit Estimate user emotions and adjust the timing of news article retrieval based on the estimated user emotions 2. The system of claim 1.

8. The acquisition unit Analyze the user's past news browsing history and select the optimal acquisition method 2. The system of claim 1.

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