System

Abstract

An object of a system according to an embodiment is to make a news article easy to read and efficiently provide the news article as a comic.SOLUTION: A system includes a news article summarization part, a comic generation part, and a term dictionary reference part. The news article summarization unit summarizes the news article. The cartoon generation unit may generate a cartoon based on the news article summarized by the news article summarization unit. The term dictionary reference unit may refer to a term dictionary to secure accuracy of the cartoon generated by the cartoon generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology had the problem that news articles were long and difficult to read, and explanatory comics were expensive to produce, making it difficult to keep up with the countless articles that are published every day.

[0005] The system according to the embodiment aims to make news articles easier to read and to provide them efficiently as comics. [Means for solving the problem]

[0006] The system according to the embodiment includes a news article summarization unit, a cartoon generation unit, and a term dictionary reference unit. The news article summarization unit summarizes news articles. The cartoon generation unit generates cartoons based on the news articles summarized by the news article summarization unit. The term dictionary reference unit references the term dictionary to ensure accuracy of the cartoons generated by the cartoon generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can make news articles easier to read and efficiently present them as comics. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The news article cartoon generation system according to an embodiment of the present invention automatically summarizes news articles, generates cartoon-style images using a generation AI, and references a glossary to ensure accuracy. This makes news articles easier to read and provides visually appealing content.

[0029] A news article cartoon generation system according to an embodiment includes a news article summarization unit, a cartoon generation unit, and a term dictionary reference unit. The news article summarization unit summarizes news articles. For example, the generation AI analyzes the content of a news article and extracts important points to generate a summary. The news article summarization unit also extracts key facts and events from a long news article to create a concise summary. For example, the generation AI analyzes the content of a news article and extracts important points to generate a summary. The cartoon generation unit generates a cartoon based on the news article summarized by the news article summarization unit. For example, the generation AI generates a cartoon-style image based on the summarized news article. The cartoon generation unit also selects an image according to the content of the news article and depicts an appropriate scene. For example, the generation AI selects an image according to the content of the news article and depicts an appropriate scene. The term dictionary reference unit references a term dictionary to ensure the accuracy of the cartoon generated by the cartoon generation unit. For example, the generation AI obtains economic and technical terms used in the news from a dictionary and generates an image based on them. The terminology dictionary reference unit also retrieves terms and information related to news articles from the dictionary and generates images based on them, allowing the news article cartoon generation system to make news articles easier to read and provide visually appealing content.

[0030] The news article summarization unit can cross-reference multiple news sources and prioritize highly reliable information to be included in the summary. For example, the generation AI cross-references multiple news sources and prioritizes highly reliable information to be included in the summary. For example, the same news story can be confirmed from multiple highly reliable sources and matching information can be included in the summary. This makes it possible to eliminate misinformation and provide accurate news by including highly reliable information in the summary.

[0031] The news article summarization unit can generate a summary after understanding the context by referring to the background information of the article and related past news. For example, the generation AI can refer to the background information of the news article and related past news to generate a summary after understanding the context. For example, it can refer to related past news to understand the background of the current news and reflect this in the summary. This allows the generation of a summary that understands the context, providing a deeper understanding.

[0032] The manga generation unit can generate multiple scenes in succession according to the content of a news article, creating a manga with a storyline. For example, the generation AI in the manga generation unit can generate multiple scenes in succession according to the content of a news article, creating a manga with a storyline. For example, the main events of a news article can be depicted in order to form a story. This allows the creation of a manga with a storyline, making it possible to provide more attractive content to readers.

[0033] The manga generation unit can learn the user's preferences and past browsing history and generate images and scenes optimized for each individual user. For example, the manga generation unit uses a generation AI to learn the user's preferences and past browsing history and generate images and scenes optimized for each individual user. For example, it prioritizes generating images and scenes that the user prefers. This makes it possible to provide more attractive content to each individual user by providing images and scenes optimized based on the user's preferences and past browsing history.

[0034] The terminology dictionary reference section can compare the content of a news article with multiple reliable sources to eliminate misinformation. For example, the generation AI can check the content of a news article against multiple reliable sources to eliminate misinformation. For example, the content of a news article can be verified based on major news sites and official announcements to eliminate misinformation. By comparing against multiple reliable sources, misinformation can be eliminated and accurate news can be provided.

[0035] The terminology dictionary reference unit can introduce a process for expert review to confirm the accuracy of the generated images and information. The terminology dictionary reference unit introduces a process for expert review to confirm the accuracy of the images and information generated by the generative AI. For example, in the case of economic news, reviews are conducted by economists and financial experts. By undergoing expert review, the accuracy of the generated images and information can be confirmed and reliability can be improved.

[0036] The term dictionary reference unit can update the content of a news article in real time to reflect the latest information. For example, the generation AI can update the content of a news article in real time to reflect the latest information. For example, every time a news article is updated, the generation AI can automatically update the content to reflect the latest information. This makes it possible to provide the latest information by updating the content of a news article in real time.

[0037] The terminology dictionary reference unit can provide a function to collect feedback from users and immediately correct any misinformation that may be included. The terminology dictionary reference unit, for example, provides a function for the generation AI to collect feedback from users and immediately correct any misinformation that may be included. For example, if a user reports misinformation, it is automatically corrected. This makes it possible to provide accurate information by collecting feedback from users and immediately correcting any misinformation that may be included.

[0038] The cartoon generation unit can customize by combining multiple images depending on the content of the news article. For example, the generation AI customizes the cartoon generation unit by combining multiple images depending on the genre and content of the news article. For example, serious images are used for political news, and pop images are used for entertainment news. This allows for the provision of visually appealing cartoons by combining multiple images depending on the content of the news article.

[0039] The manga generation unit can convert the generated manga into an interactive format, allowing the user to select scenes and advance the story. For example, the manga generation unit converts the manga generated by a generation AI into an interactive format, allowing the user to select scenes and advance the story. For example, the manga generation unit generates a manga in which the story branches depending on the options the user selects. This makes it possible to provide a more immersive experience by providing an interactive manga in which the user can select scenes and advance the story.

[0040] The manga generation unit can convert the generated manga into an animation format and provide it as moving content. For example, the generation AI converts manga-format images into animation format and provides it as moving content. For example, it animates manga scenes and distributes them as videos. By converting the generated manga into animation format, it is possible to provide visually appealing moving content.

[0041] The cartoon generation unit can automatically translate summaries of news articles into different languages ​​and provide summaries from an international perspective. For example, the cartoon generation unit uses a generation AI to automatically translate summaries of news articles into different languages ​​and provide summaries from an international perspective. For example, it translates into multiple languages ​​such as English, French, and Chinese, and generates summaries in each language. This makes it possible to provide summaries from an international perspective by automatically translating summaries of news articles into different languages.

[0042] The cartoon generation unit converts the summarized news article into an audio format and can distribute it as a podcast or audio news. The cartoon generation unit, for example, uses a generation AI to convert the summarized news article into an audio format and distribute it as a podcast or audio news. For example, the summarized news article is converted into audio using speech synthesis technology and distributed as a podcast. In this way, by converting the summarized news article into an audio format, it can be distributed as a podcast or audio news.

[0043] The manga generation unit can learn from the pictures provided by the user and generate new pictures that match the content of the news article. For example, the manga generation unit uses a generation AI to learn from the pictures provided by the user and generate new pictures that match the content of the news article. For example, it generates pictures that are optimal for news articles based on pictures provided by the user. In this way, by learning from the pictures provided by the user and generating new pictures that match the content of the news article, it is possible to provide more diverse and attractive manga.

[0044] The manga generation unit can evaluate the quality of drawings provided by the user and prioritize the use of high-quality drawings as training data. For example, the manga generation unit can have a generation AI evaluate the quality of drawings provided by the user and prioritize the use of high-quality drawings as training data. For example, the generation AI can evaluate the resolution and design details and select high-quality drawings. This allows the quality of the generated manga to be improved by evaluating the quality of drawings provided by the user and prioritize the use of high-quality drawings as training data.

[0045] The manga generation unit can introduce a mechanism that automatically evaluates drawings provided by users, selects excellent drawings, and returns rewards. For example, the manga generation unit introduces a mechanism whereby the generation AI automatically evaluates drawings provided by users, selects excellent drawings, and returns rewards. For example, the reward is determined based on the quality and popularity of the drawing. This can increase users' motivation to participate by introducing a mechanism whereby the generation AI automatically evaluates drawings provided by users, selects excellent drawings, and returns rewards.

[0046] The manga generation unit provides a platform where other users can rate drawings provided by users and can return rewards based on the ratings. The manga generation unit, for example, provides a platform where other users can rate drawings provided by users using a generation AI and can return rewards based on the ratings. For example, users can rate and comment on drawings. This provides a platform where other users can rate drawings provided by users and can return rewards based on the ratings, thereby increasing users' motivation to participate.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The news article summarization unit can customize summaries based on the user's interests. For example, if a user is interested in a particular topic, the summaries will prioritize information related to that topic. It can also analyze the user's browsing history and summarize news related to the user's areas of interest. This allows the news summaries to be more relevant to the user.

[0049] The news article summarization unit can be equipped with a function to evaluate the reliability of news articles when generating summaries. For example, it can adjust the content of the summary based on the reliability score of the news source. It can also generate summaries taking into account the publication date and author reliability of the news article. This allows for the provision of highly reliable news summaries.

[0050] The cartoon generator can customize different art styles to suit the content of the news article. For example, it can use a realistic art style for political news and a cartoon-like art style for entertainment news. It can also adjust the color and design depending on the genre of the news article. This allows it to provide visually appealing cartoons that match the content of the news article.

[0051] The manga generation unit can generate multiple scenes sequentially based on the content of a news article, creating a manga with a storyline. For example, it can depict the main events of a news article in order to form a story. It can also generate scenes with an understanding of the context by referencing background information about the news article and related past news. This allows the creation of manga with a storyline, providing more compelling content for readers.

[0052] The terminology dictionary reference section can compare the content of news articles with multiple reliable sources to eliminate misinformation. For example, it checks the content of news articles based on major news sites and official announcements to eliminate misinformation. It can also update the content of news articles in real time to reflect the latest information. This allows it to compare the content with multiple reliable sources to eliminate misinformation and provide accurate news.

[0053] The terminology dictionary reference unit can implement a process for expert review to confirm the accuracy of the generated images and information. For example, economic news can be reviewed by economists and financial experts. It is also possible to have appropriate experts review depending on the genre of the news article. This allows the accuracy of the generated images and information to be confirmed and their reliability to be improved by having them reviewed by experts.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The news article summarization unit summarizes the news article. For example, the generative AI analyzes the content of the news article, extracts key points, and generates a summary. It also extracts key facts and events from long news articles to create a concise summary. Step 2: The cartoon generation unit generates a cartoon based on the news article summarized by the news article summarization unit. For example, the generation AI generates a cartoon-style image based on the summarized news article, selects an image according to the content of the news article, and depicts an appropriate scene. Step 3: The dictionary reference section references the dictionary to ensure the accuracy of the cartoons generated by the cartoon generation section. For example, the generation AI retrieves economic and technical terms used in news articles from the dictionary and generates images based on them. It also retrieves terms and information related to news articles from the dictionary and generates images based on them.

[0056] (Example 2) The news article cartoon generation system according to an embodiment of the present invention automatically summarizes news articles, generates cartoon-style images using a generation AI, and references a glossary to ensure accuracy. This makes news articles easier to read and provides visually appealing content.

[0057] A news article cartoon generation system according to an embodiment includes a news article summarization unit, a cartoon generation unit, and a term dictionary reference unit. The news article summarization unit summarizes news articles. For example, the generation AI analyzes the content of a news article and extracts important points to generate a summary. The news article summarization unit also extracts key facts and events from a long news article to create a concise summary. For example, the generation AI analyzes the content of a news article and extracts important points to generate a summary. The cartoon generation unit generates a cartoon based on the news article summarized by the news article summarization unit. For example, the generation AI generates a cartoon-style image based on the summarized news article. The cartoon generation unit also selects an image according to the content of the news article and depicts an appropriate scene. For example, the generation AI selects an image according to the content of the news article and depicts an appropriate scene. The term dictionary reference unit references a term dictionary to ensure the accuracy of the cartoon generated by the cartoon generation unit. For example, the generation AI obtains economic and technical terms used in the news from a dictionary and generates an image based on them. The terminology dictionary reference unit also retrieves terms and information related to news articles from the dictionary and generates images based on them, allowing the news article cartoon generation system to make news articles easier to read and provide visually appealing content.

[0058] The news article summarization unit can analyze the emotional tone of an article using an emotion estimation function and generate a summary based on the emotion. For example, the news article summarization unit uses a generation AI to analyze the emotional tone of a news article and generate a summary based on the emotion. For example, if the article contains positive content, a positive summary is generated, and if the article contains negative content, a negative summary is generated. In this way, by generating a summary based on the emotional tone of the article, it is possible to provide content that is more easily relatable to readers.

[0059] The news article summarization unit can cross-reference multiple news sources and prioritize highly reliable information to be included in the summary. For example, the generation AI cross-references multiple news sources and prioritizes highly reliable information to be included in the summary. For example, the same news story can be confirmed from multiple highly reliable sources and matching information can be included in the summary. This makes it possible to eliminate misinformation and provide accurate news by including highly reliable information in the summary.

[0060] The news article summarization unit can generate a summary after understanding the context by referring to the background information of the article and related past news. For example, the generation AI can refer to the background information of the news article and related past news to generate a summary after understanding the context. For example, it can refer to related past news to understand the background of the current news and reflect this in the summary. This allows the generation of a summary that understands the context, providing a deeper understanding.

[0061] The cartoon generation unit can use the emotion estimation function to select images and scenes based on the emotional tone of a news article, generating a cartoon that matches the emotion. For example, the generation AI in the cartoon generation unit selects images and scenes based on the emotional tone of a news article, generating a cartoon that matches the emotion. For example, it selects bright images for positive news and dark images for negative news. This allows for the provision of visually appealing cartoons by selecting images and scenes based on the emotional tone of the news article.

[0062] The manga generation unit can generate multiple scenes in succession according to the content of a news article, creating a manga with a storyline. For example, the generation AI in the manga generation unit can generate multiple scenes in succession according to the content of a news article, creating a manga with a storyline. For example, the main events of a news article can be depicted in order to form a story. This allows the creation of a manga with a storyline, making it possible to provide more attractive content to readers.

[0063] The manga generation unit can learn the user's preferences and past browsing history and generate images and scenes optimized for each individual user. For example, the manga generation unit uses a generation AI to learn the user's preferences and past browsing history and generate images and scenes optimized for each individual user. For example, it prioritizes generating images and scenes that the user prefers. This makes it possible to provide more attractive content to each individual user by providing images and scenes optimized based on the user's preferences and past browsing history.

[0064] The term dictionary reference unit uses the emotion estimation function to select accurate terms and information based on the emotional tone of a news article, preventing hallucination. For example, the term dictionary reference unit uses a generation AI to select accurate terms and information based on the emotional tone of a news article, preventing hallucination. For example, it selects positive terms for positive news and negative terms for negative news. This allows the selection of accurate terms and information based on the emotional tone of a news article, preventing hallucination and providing accurate information.

[0065] The terminology dictionary reference section can compare the content of a news article with multiple reliable sources to eliminate misinformation. For example, the generation AI can check the content of a news article against multiple reliable sources to eliminate misinformation. For example, the content of a news article can be verified based on major news sites and official announcements to eliminate misinformation. By comparing against multiple reliable sources, misinformation can be eliminated and accurate news can be provided.

[0066] The terminology dictionary reference unit can introduce a process for expert review to confirm the accuracy of the generated images and information. The terminology dictionary reference unit introduces a process for expert review to confirm the accuracy of the images and information generated by the generative AI. For example, in the case of economic news, reviews are conducted by economists and financial experts. By undergoing expert review, the accuracy of the generated images and information can be confirmed and reliability can be improved.

[0067] The term dictionary reference unit can update the content of a news article in real time to reflect the latest information. For example, the generation AI can update the content of a news article in real time to reflect the latest information. For example, every time a news article is updated, the generation AI can automatically update the content to reflect the latest information. This makes it possible to provide the latest information by updating the content of a news article in real time.

[0068] The terminology dictionary reference unit can provide a function to collect feedback from users and immediately correct any misinformation that may be included. The terminology dictionary reference unit, for example, provides a function for the generation AI to collect feedback from users and immediately correct any misinformation that may be included. For example, if a user reports misinformation, it is automatically corrected. This makes it possible to provide accurate information by collecting feedback from users and immediately correcting any misinformation that may be included.

[0069] The term dictionary reference unit can use the emotion estimation function to analyze the user's emotional response to the generated images and information, and identify and correct information that may be misleading. For example, the generation AI uses the emotion estimation function to analyze the user's emotional response to the generated images and information, and identify and correct information that may be misleading. For example, the term dictionary reference unit corrects information if the user shows negative emotions. This makes it possible to provide accurate information by analyzing the user's emotional response and identifying and correcting information that may be misleading.

[0070] The cartoon generation unit can customize by combining multiple images depending on the content of the news article. For example, the generation AI customizes the cartoon generation unit by combining multiple images depending on the genre and content of the news article. For example, serious images are used for political news, and pop images are used for entertainment news. This allows for the provision of visually appealing cartoons by combining multiple images depending on the content of the news article.

[0071] The manga generation unit can convert the generated manga into an interactive format, allowing the user to select scenes and advance the story. For example, the manga generation unit converts the manga generated by a generation AI into an interactive format, allowing the user to select scenes and advance the story. For example, the manga generation unit generates a manga in which the story branches depending on the options the user selects. This makes it possible to provide a more immersive experience by providing an interactive manga in which the user can select scenes and advance the story.

[0072] The manga generation unit can monitor the user's emotional response to the manga generated using the emotion estimation function in real time and change or add scenes according to the emotion. For example, the manga generation unit uses the emotion estimation function to monitor the user's emotional response to the generated manga in real time and change or add scenes according to the emotion. For example, if the user expresses positive emotion, a positive scene is added. This makes it possible to provide a more personalized experience by monitoring the user's emotional response in real time and changing or adding scenes according to the emotion.

[0073] The manga generation unit can convert the generated manga into an animation format and provide it as moving content. For example, the generation AI converts manga-format images into animation format and provides it as moving content. For example, it animates manga scenes and distributes them as videos. By converting the generated manga into animation format, it is possible to provide visually appealing moving content.

[0074] The cartoon generation unit can automatically translate summaries of news articles into different languages ​​and provide summaries from an international perspective. For example, the cartoon generation unit uses a generation AI to automatically translate summaries of news articles into different languages ​​and provide summaries from an international perspective. For example, it translates into multiple languages ​​such as English, French, and Chinese, and generates summaries in each language. This makes it possible to provide summaries from an international perspective by automatically translating summaries of news articles into different languages.

[0075] The cartoon generation unit converts the summarized news article into an audio format and can distribute it as a podcast or audio news. The cartoon generation unit, for example, uses a generation AI to convert the summarized news article into an audio format and distribute it as a podcast or audio news. For example, the summarized news article is converted into audio using speech synthesis technology and distributed as a podcast. In this way, by converting the summarized news article into an audio format, it can be distributed as a podcast or audio news.

[0076] The cartoon generation unit can use the emotion estimation function to collect users' emotional reactions to summarized news articles and adjust the content of the summary to match the user's emotions. For example, the generation AI in the cartoon generation unit uses the emotion estimation function to collect users' emotional reactions to summarized news articles and adjust the content of the summary to match the user's emotions. For example, if there are many positive emotional reactions, a positive summary is provided. In this way, by collecting users' emotional reactions to summarized news articles and adjusting the content of the summary to match the user's emotions, a more personalized summary can be provided.

[0077] The manga generation unit can learn from the pictures provided by the user and generate new pictures that match the content of the news article. For example, the manga generation unit uses a generation AI to learn from the pictures provided by the user and generate new pictures that match the content of the news article. For example, it generates pictures that are optimal for news articles based on pictures provided by the user. In this way, by learning from the pictures provided by the user and generating new pictures that match the content of the news article, it is possible to provide more diverse and attractive manga.

[0078] The manga generation unit can evaluate the quality of drawings provided by the user and prioritize the use of high-quality drawings as training data. For example, the manga generation unit can have a generation AI evaluate the quality of drawings provided by the user and prioritize the use of high-quality drawings as training data. For example, the generation AI can evaluate the resolution and design details and select high-quality drawings. This allows the quality of the generated manga to be improved by evaluating the quality of drawings provided by the user and prioritize the use of high-quality drawings as training data.

[0079] The cartoon generation unit can use the emotion estimation function to analyze the emotional tone of the illustration provided by the user and select the illustration that is most suitable for the news article. For example, the generation AI in the cartoon generation unit uses the emotion estimation function to analyze the emotional tone of the illustration provided by the user and select the illustration that is most suitable for the news article. For example, an illustration with a positive emotional tone is selected for positive news. This allows for the provision of more appropriate visuals by analyzing the emotional tone of the illustration provided by the user and selecting the illustration that is most suitable for the news article.

[0080] The manga generation unit can introduce a mechanism that automatically evaluates drawings provided by users, selects excellent drawings, and returns rewards. For example, the manga generation unit introduces a mechanism whereby the generation AI automatically evaluates drawings provided by users, selects excellent drawings, and returns rewards. For example, the reward is determined based on the quality and popularity of the drawing. This can increase users' motivation to participate by introducing a mechanism whereby the generation AI automatically evaluates drawings provided by users, selects excellent drawings, and returns rewards.

[0081] The manga generation unit provides a platform where other users can rate drawings provided by users and can return rewards based on the ratings. The manga generation unit, for example, provides a platform where other users can rate drawings provided by users using a generation AI and can return rewards based on the ratings. For example, users can rate and comment on drawings. This provides a platform where other users can rate drawings provided by users and can return rewards based on the ratings, thereby increasing users' motivation to participate.

[0082] The manga generation unit can use the emotion estimation function to collect other users' emotional reactions to a picture provided by the user and reflect them in the evaluation. For example, the generation AI in the manga generation unit uses the emotion estimation function to collect other users' emotional reactions to a picture provided by the user and reflect them in the evaluation. For example, a picture with a high number of positive emotional reactions is given a high rating. In this way, by collecting other users' emotional reactions to a picture provided by the user and reflecting them in the evaluation, a more appropriate evaluation can be made.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The news article summarization unit can customize summaries based on the user's interests. For example, if a user is interested in a particular topic, the summaries will prioritize information related to that topic. It can also analyze the user's browsing history and summarize news related to the user's areas of interest. This allows the news summaries to be more relevant to the user.

[0085] The news article summarization unit can use the emotion estimation function to change the style of the summary based on the emotional tone of the news article. For example, it can generate a bright-toned summary for a positive news article and a calm-toned summary for a negative news article. It can also adjust the length and level of detail of the summary depending on the emotional tone. This allows the unit to provide a summary that matches the emotional tone of the news article, thereby providing content that readers can more easily empathize with.

[0086] The news article summarization unit can be equipped with a function to evaluate the reliability of news articles when generating summaries. For example, it can adjust the content of the summary based on the reliability score of the news source. It can also generate summaries taking into account the publication date and author reliability of the news article. This allows for the provision of highly reliable news summaries.

[0087] The news article summarization unit can use the emotion estimation function to change the order of summaries based on the emotional tone of the news article. For example, it can summarize positive information first in a positive news article and negative information first in a negative news article. It can also change the emphasis of the summary depending on the emotional tone. This allows the unit to provide a summary that matches the emotional tone of the news article, making it easier for readers to understand.

[0088] The cartoon generator can customize different art styles to suit the content of the news article. For example, it can use a realistic art style for political news and a cartoon-like art style for entertainment news. It can also adjust the color and design depending on the genre of the news article. This allows it to provide visually appealing cartoons that match the content of the news article.

[0089] The cartoon generator can use its emotion estimation function to select character expressions and poses based on the emotional tone of a news article. For example, it can use a smiling character for positive news and a sad character for negative news. It can also adjust the character's movements and gestures according to the emotional tone. This allows it to provide a cartoon that is visually easy to empathize with by using characters based on the emotional tone of the news article.

[0090] The manga generation unit can generate multiple scenes sequentially based on the content of a news article, creating a manga with a storyline. For example, it can depict the main events of a news article in order to form a story. It can also generate scenes with an understanding of the context by referencing background information about the news article and related past news. This allows the creation of manga with a storyline, providing more compelling content for readers.

[0091] The term dictionary reference unit uses the emotion estimation function to select accurate terms and information based on the emotional tone of a news article, preventing hallucination. For example, it selects positive terms for positive news and negative terms for negative news. It can also adjust the criteria for term selection depending on the emotional tone. This allows it to prevent hallucination and provide accurate information by selecting accurate terms and information based on the emotional tone of a news article.

[0092] The terminology dictionary reference section can compare the content of news articles with multiple reliable sources to eliminate misinformation. For example, it checks the content of news articles based on major news sites and official announcements to eliminate misinformation. It can also update the content of news articles in real time to reflect the latest information. This allows it to compare the content with multiple reliable sources to eliminate misinformation and provide accurate news.

[0093] The terminology dictionary reference unit can implement a process for expert review to confirm the accuracy of the generated images and information. For example, economic news can be reviewed by economists and financial experts. It is also possible to have appropriate experts review depending on the genre of the news article. This allows the accuracy of the generated images and information to be confirmed and their reliability to be improved by having them reviewed by experts.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The news article summarization unit summarizes the news article. For example, the generative AI analyzes the content of the news article, extracts key points, and generates a summary. It also extracts key facts and events from long news articles to create a concise summary. Step 2: The cartoon generation unit generates a cartoon based on the news article summarized by the news article summarization unit. For example, the generation AI generates a cartoon-style image based on the summarized news article, selects an image according to the content of the news article, and depicts an appropriate scene. Step 3: The dictionary reference section references the dictionary to ensure the accuracy of the cartoons generated by the cartoon generation section. For example, the generation AI retrieves economic and technical terms used in news articles from the dictionary and generates images based on them. It also retrieves terms and information related to news articles from the dictionary and generates images based on them.

[0096] 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.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0098] 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.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0140] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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. [Explanation of symbols]

[0163] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A section summarizing news articles, a cartoon generation unit that generates a cartoon based on the news article summarized by the news article summarization unit; a term dictionary reference unit that references a term dictionary to ensure the accuracy of the cartoon generated by the cartoon generation unit; A system characterized by:

2. The news article summary section Cross-reference multiple news sources and prioritize reliable information for inclusion in the summary The system of claim 1 .

3. The manga generation section Depending on the content of the news article, multiple scenes are generated consecutively to create a story-like manga. The system of claim 1 .

4. The glossary reference section is Cross-check the content of the news article with multiple reliable sources to eliminate misinformation The system of claim 1 .

5. The news article summary section Analyze the emotional tone of an article using a sentiment estimation function and generate a sentiment-based summary. The system of claim 1 .

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