System

Abstract

An object of a system according to an embodiment is to quickly and accurately update Wikipedia and increase advertising revenue.SOLUTION: A system according to an embodiment includes an acting poster unit, a translation unit, and an advertisement management unit. The poster acting part acts as a poster of Wikipedia by using the generated AI. The translation unit simultaneously translates the article generated by the acting poster unit into a plurality of languages. The advertisement management part inserts a pay advertisement in the article translated by the translation part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, Wikipedia was slow to update and had issues with being inaccurate and incomplete.

[0005] The system according to the embodiment aims to update Wikipedia quickly and accurately and increase advertising revenue. [Means for solving the problem]

[0006] The system according to the embodiment includes a contributor proxy unit, a translation unit, and an advertising management unit. The contributor proxy unit acts on behalf of Wikipedia contributors using a generation AI. The translation unit simultaneously translates articles generated by the contributor proxy unit into multiple languages. The advertising management unit places paid advertisements in the articles translated by the translation unit. [Effects of the Invention]

[0007] The system according to the embodiment can update Wikipedia quickly and accurately, thereby increasing advertising revenue. [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 Wikipedia proxy posting system according to an embodiment of the present invention is a system in which a generation AI acts on behalf of Wikipedia contributors, updating articles and posting new articles, and also adds functions such as simultaneous global translation and paid advertising. As a result, the Wikipedia proxy posting system can solve issues such as slow Wikipedia updates, lack of accuracy, and insufficient comprehensiveness, and can generate advertising revenue.

[0029] A Wikipedia posting agent system according to an embodiment includes a contributor agent unit, a translation unit, and an advertising management unit. The contributor agent unit uses a generation AI to act on behalf of Wikipedia contributors, updating articles and posting new articles. For example, the generation AI collects the latest information on the Internet and creates articles based on reliable sources. The generation AI also analyzes the latest research papers and news articles to generate accurate and comprehensive articles. For example, the generation AI receives a prompt such as, "Please update an article about the latest science and technology," and generates an article. The translation unit simultaneously translates the generated article into multiple languages. For example, an article generated in English is automatically translated into Japanese, French, Chinese, etc., and posted to each language version of Wikipedia simultaneously. For example, the translation unit takes into account the cultural background and nuances of each language when the generation AI translates, providing a more natural translation. The advertising management unit places paid advertisements in the generated articles. For example, advertisers can place advertisements on pages related to specific keywords or categories. For example, the advertising management unit places advertisements for medical device manufacturers in articles about medicine. This allows the Wikipedia submission proxy system to solve problems such as Wikipedia's slow updates, lack of accuracy, and incompleteness, and to earn advertising revenue.

[0030] Before generating an article using the generation AI, the contributor agent unit can analyze related past Wikipedia articles or editing history and provide feedback to maintain the consistency of the article. For example, before the generation AI generates an article, the contributor agent unit analyzes related past Wikipedia articles and provides feedback to maintain the consistency of the article. For example, it generates a new article by referring to the style and tone of past articles. For past Wikipedia articles, for example, it analyzes the content and editing history of the article. For editing history, for example, it analyzes the frequency of editing and editor information. For example, it provides feedback such as suggested corrections and comments. This makes it possible to provide feedback to maintain the consistency of the article.

[0031] The poster agent unit can automatically request an expert review of an article generated using the generative AI and revise the article based on that feedback. For example, the poster agent unit automatically requests an expert review of an article generated by the generative AI and revise the article based on that feedback. For example, when an article about science and technology is generated, it requests a scientist or researcher to review it. An expert is, for example, a person with expertise in a field or a person with certified qualifications. The review, for example, evaluates the accuracy and comprehensiveness of the content. The feedback, for example, provides suggestions for revisions or comments. This allows the article to be revised based on the expert feedback.

[0032] The poster agent unit can automatically insert related images or videos into articles generated using the generation AI, providing visually rich content. For example, the poster agent unit adds a function to automatically insert related images and videos when the generation AI generates an article. For example, images and videos of experiments are inserted into articles about science and technology. Images include, for example, photographs, illustrations, graphs, etc. Videos include, for example, short videos and animations. Content consists of, for example, text, images, videos, etc. This makes it possible to provide visually rich content.

[0033] The poster agent unit can automatically add related links to articles generated using the generation AI based on the user's past browsing history or interests. For example, the poster agent unit uses the generation AI to analyze the user's past browsing history and automatically add links related to the article. For example, the poster agent unit inserts links related to articles the user has previously viewed into a new article. The browsing history is collected using, for example, cookies or browser history. Interests are identified using, for example, past search history or click history. Related links are selected, for example, from highly relevant articles or external links. This allows related links to be automatically added based on the user's past browsing history and interests.

[0034] When translating using generative AI, the translation unit can provide more natural translations based on the cultural background and nuances of each language. For example, when generative AI translates, the translation unit adds a function that takes into account the cultural background and nuances of each language. For example, Japanese translations can reflect Japanese culture and customs. Cultural background, for example, takes into account local customs and historical background. Nuance, for example, takes into account the subtle meanings and context of words. Natural translations are based on, for example, fluency and grammatical accuracy. This allows for more natural translations to be provided by taking into account the cultural background and nuances of each language.

[0035] The translation unit can automatically request reviews from native speakers of each language for the translated article and improve translation accuracy based on the feedback. The translation unit, for example, builds a system that automatically requests reviews from native speakers of each language for the translated article. For example, when an English article is translated into Japanese, a review is requested from a native speaker of Japanese. The native speaker is, for example, a native speaker or a language expert. The review evaluates, for example, the accuracy and fluency of the content. The feedback provides, for example, correction suggestions and comments. This allows translation accuracy to be improved based on reviews by native speakers of each language.

[0036] The translation unit adds a voice readout function for each language to the translated article, thereby accommodating users who are visually impaired or have difficulty reading and writing. The translation unit, for example, adds a voice readout function to the translated article, thereby accommodating users who are visually impaired or have difficulty reading and writing. For example, an English article is translated into Japanese and the Japanese article is read aloud. The voice readout function uses, for example, text-to-speech software or speech synthesis technology. Users who are visually impaired use, for example, audio guides or Braille. Users who have difficulty reading and writing use, for example, audio guides or simple language. This makes it possible to accommodate users who are visually impaired or have difficulty reading and writing.

[0037] The translation unit automatically generates sign language videos in each language for the translated article, making it possible to accommodate hearing-impaired people. For example, the translation unit adds a function to automatically generate sign language videos in each language for the translated article. For example, an English article is translated into Japanese, and a sign language video corresponding to the Japanese article is generated. The sign language video is created, for example, using a sign language interpreter or sign language animation. Hearing-impaired people use, for example, sign language interpreters and subtitles. This makes it possible to accommodate hearing-impaired people.

[0038] The advertisement management unit can dynamically change the display position or content of advertisements based on the user's browsing history or interests, thereby maximizing the effectiveness of advertisements. The advertisement management unit, for example, dynamically changes the display position of advertisements based on the user's browsing history. For example, it adjusts the position at which advertisements related to the content of pages the user has previously viewed are displayed. The display position may be, for example, the top of a page or a sidebar. The content may be, for example, a text advertisement or a banner advertisement. The browsing history is collected using, for example, cookies or browser history. The interests are identified using, for example, past search history or click history. The effectiveness of advertisements is evaluated based on, for example, click rate or conversion rate. This makes it possible to maximize the effectiveness of advertisements.

[0039] The advertising management unit can monitor the click rates and conversion rates of advertisements in real time and automatically replace ineffective advertisements. The advertising management unit, for example, adds a function to monitor the click rates and conversion rates of advertisements in real time and automatically replace ineffective advertisements. For example, an advertisement with a low click rate is replaced with another advertisement. The click rate is measured, for example, based on the number of clicks or the number of impressions. The conversion rate is measured, for example, based on the number of purchases or the number of registrations. An ineffective advertisement is, for example, an advertisement with a low click rate or conversion rate. Replacement is performed, for example, by selecting a new advertisement or changing the display. This makes it possible to automatically replace ineffective advertisements.

[0040] The ad management unit provides a platform that allows advertisers to create and manage their own ads, thereby improving the customizability of ads. The ad management unit provides, for example, a platform that allows advertisers to create and manage their own ads. For example, it provides an interface that allows advertisers to freely customize the design and content of ads. The platform provides, for example, ad creation tools and a management dashboard. Customizability is evaluated based on, for example, the ability to change the ad design and targeting settings. This allows advertisers to provide a platform that allows them to create and manage their own ads, thereby improving the customizability of ads.

[0041] The advertising management unit can increase social value by adding a function to donate a portion of advertising revenue to a charity or social contribution activity selected by the user. The advertising management unit, for example, adds a function to donate a portion of advertising revenue to a charity or social contribution activity selected by the user. For example, it provides an interface that allows the user to select a donation destination. The charity is selected based on, for example, a certified NPO or a donation platform. The social contribution activity is selected based on, for example, environmental protection activities or educational support. Donations are made based on, for example, monetary donations or donations in kind. Social value is evaluated based on, for example, social impact and sustainability. In this way, social value can be increased by donating a portion of advertising revenue to a charity or social contribution activity.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] When generating articles using the generation AI, the poster agent can analyze the user's past posting history and customize the article based on the user's style and preferences. For example, it can analyze the writing style and tone of articles posted by the user in the past and reflect this in the new article. For example, the past posting history can be analyzed to determine the content and frequency of articles posted by the user. Customization can be achieved by, for example, adjusting the writing style or using specific keywords. This allows articles to be generated based on the user's style and preferences.

[0044] When generating articles using generative AI, the Author Agency can automatically cite data from other reliable sources to improve the credibility of the article. For example, for articles about science and technology, the agency cites the latest research papers and official announcements. Sources are selected based on, for example, academic databases and government agency websites. Citations are made using, for example, appropriate citation formats. This allows the credibility of the article to be improved by citing data from other reliable sources.

[0045] The author agent can add relevant interactive elements to articles generated using the generation AI to increase user engagement. For example, interactive graphs and simulations can be inserted into articles about science and technology. Interactive elements are graphs and animations that users can manipulate by clicking and dragging. Engagement is evaluated based on, for example, the user's time spent on the page and the number of clicks. This makes it possible to increase user engagement by adding relevant interactive elements.

[0046] The poster agent can automatically display personalized recommended articles based on the user's past browsing history and interests in articles generated using the generation AI. For example, if a user has viewed many science and technology articles in the past, the latest related science and technology articles will be recommended. Browsing history is collected, for example, using cookies or browser history. Interests are identified, for example, using past search history or click history. Recommended articles are selected, for example, based on highly relevant or popular articles. This makes it possible to automatically display personalized recommended articles based on the user's past browsing history and interests.

[0047] When translating using generative AI, the translation unit can automatically recognize technical and specialized terms in each language and provide appropriate translations. For example, for medical articles, medical terms are accurately translated. Technical terms are, for example, terms and phrases used in specific fields. Technical terms are, for example, terms related to science, technology, and engineering. Appropriate translations are performed based on, for example, technical dictionaries and glossaries. This allows the system to automatically recognize technical and specialized terms in each language and provide appropriate translations.

[0048] The translation unit automatically requests reviews from native speakers of each language for the translated article, and can improve translation accuracy based on the feedback. For example, when an English article is translated into Japanese, a review is requested from a native Japanese speaker. A native speaker may be, for example, a native speaker or a language expert. The review may evaluate, for example, the accuracy and fluency of the content. The feedback may provide, for example, correction suggestions or comments. This allows translation accuracy to be improved based on reviews from native speakers of each language.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The contributor agent uses generation AI to act on behalf of Wikipedia contributors, updating articles and posting new ones. The generation AI collects the latest information from the internet and creates articles based on reliable sources. The generation AI also analyzes the latest research papers and news articles to generate accurate and comprehensive articles. For example, it receives a prompt such as "Please update articles about the latest science and technology," and generates an article. Step 2: The translation department translates the generated article into multiple languages ​​simultaneously. For example, an article generated in English can be automatically translated into Japanese, French, Chinese, etc., and posted to each language's Wikipedia page simultaneously. The translation department takes into account the cultural background and nuances of each language to provide a more natural translation. Step 3: The advertising management unit places paid advertisements in the generated articles. For example, advertisers can place advertisements on pages related to specific keywords or categories. The advertising management unit places advertisements for medical device manufacturers in articles related to medical care.

[0051] (Example 2) The Wikipedia proxy posting system according to an embodiment of the present invention is a system in which a generation AI acts on behalf of Wikipedia contributors, updating articles and posting new articles, and also adds functions such as simultaneous global translation and paid advertising. As a result, the Wikipedia proxy posting system can solve issues such as slow Wikipedia updates, lack of accuracy, and insufficient comprehensiveness, and can generate advertising revenue.

[0052] A Wikipedia posting agent system according to an embodiment includes a contributor agent unit, a translation unit, and an advertising management unit. The contributor agent unit uses a generation AI to act on behalf of Wikipedia contributors, updating articles and posting new articles. For example, the generation AI collects the latest information on the Internet and creates articles based on reliable sources. The generation AI also analyzes the latest research papers and news articles to generate accurate and comprehensive articles. For example, the generation AI receives a prompt such as, "Please update an article about the latest science and technology," and generates an article. The translation unit simultaneously translates the generated article into multiple languages. For example, an article generated in English is automatically translated into Japanese, French, Chinese, etc., and posted to each language version of Wikipedia simultaneously. For example, the translation unit takes into account the cultural background and nuances of each language when the generation AI translates, providing a more natural translation. The advertising management unit places paid advertisements in the generated articles. For example, advertisers can place advertisements on pages related to specific keywords or categories. For example, the advertising management unit places advertisements for medical device manufacturers in articles about medicine. This allows the Wikipedia submission proxy system to solve problems such as Wikipedia's slow updates, lack of accuracy, and incompleteness, and to earn advertising revenue.

[0053] When generating an article using the generation AI, the poster agent unit can use the emotion estimation function to adjust the tone and style of the article based on the user's emotions. For example, when the generation AI generates an article, the poster agent unit analyzes the user's emotions in real time and adjusts the tone and style of the article based on those emotions. For example, if the user has positive emotions, the tone of the article is brightened, and if the user has negative emotions, the tone is calmed. The emotion estimation function estimates the user's emotions using, for example, an emotion analysis algorithm or natural language processing technology. The user's emotions are estimated using, for example, text analysis or facial expression recognition. The tone and style of the article can be adjusted to, for example, formal, casual, emotional, etc. This allows the tone and style of the article to be adjusted according to the user's emotions.

[0054] Before generating an article using the generation AI, the contributor agent unit can analyze related past Wikipedia articles or editing history and provide feedback to maintain the consistency of the article. For example, before the generation AI generates an article, the contributor agent unit analyzes related past Wikipedia articles and provides feedback to maintain the consistency of the article. For example, it generates a new article by referring to the style and tone of past articles. For past Wikipedia articles, for example, it analyzes the content and editing history of the article. For editing history, for example, it analyzes the frequency of editing and editor information. For example, it provides feedback such as suggested corrections and comments. This makes it possible to provide feedback to maintain the consistency of the article.

[0055] The poster agent unit can automatically request an expert review of an article generated using the generative AI and revise the article based on that feedback. For example, the poster agent unit automatically requests an expert review of an article generated by the generative AI and revise the article based on that feedback. For example, when an article about science and technology is generated, it requests a scientist or researcher to review it. An expert is, for example, a person with expertise in a field or a person with certified qualifications. The review, for example, evaluates the accuracy and comprehensiveness of the content. The feedback, for example, provides suggestions for revisions or comments. This allows the article to be revised based on the expert feedback.

[0056] The poster agent unit can automatically insert related images or videos into articles generated using the generation AI, providing visually rich content. For example, the poster agent unit adds a function to automatically insert related images and videos when the generation AI generates an article. For example, images and videos of experiments are inserted into articles about science and technology. Images include, for example, photographs, illustrations, graphs, etc. Videos include, for example, short videos and animations. Content consists of, for example, text, images, videos, etc. This makes it possible to provide visually rich content.

[0057] The poster agent unit can automatically add related links to articles generated using the generation AI based on the user's past browsing history or interests. For example, the poster agent unit uses the generation AI to analyze the user's past browsing history and automatically add links related to the article. For example, the poster agent unit inserts links related to articles the user has previously viewed into a new article. The browsing history is collected using, for example, cookies or browser history. Interests are identified using, for example, past search history or click history. Related links are selected, for example, from highly relevant articles or external links. This allows related links to be automatically added based on the user's past browsing history and interests.

[0058] The poster proxy unit can use the emotion estimation function to monitor users' emotional reactions to articles generated by the generation AI in real time and make adjustments to elicit positive reactions. The poster proxy unit, for example, uses the emotion estimation function to monitor users' emotional reactions to articles generated by the generation AI in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The emotion estimation function estimates emotions using, for example, an emotion analysis algorithm or natural language processing technology. Emotional reactions are collected using, for example, text analysis or facial expression recognition. Adjustments can be made, for example, by changing the content or tone of the article. This makes it possible to monitor users' emotional reactions in real time and make adjustments to elicit positive reactions.

[0059] When translating using generative AI, the translation unit can provide more natural translations based on the cultural background and nuances of each language. For example, when generative AI translates, the translation unit adds a function that takes into account the cultural background and nuances of each language. For example, Japanese translations can reflect Japanese culture and customs. Cultural background, for example, takes into account local customs and historical background. Nuance, for example, takes into account the subtle meanings and context of words. Natural translations are based on, for example, fluency and grammatical accuracy. This allows for more natural translations to be provided by taking into account the cultural background and nuances of each language.

[0060] The translation unit can automatically request reviews from native speakers of each language for the translated article and improve translation accuracy based on the feedback. The translation unit, for example, builds a system that automatically requests reviews from native speakers of each language for the translated article. For example, when an English article is translated into Japanese, a review is requested from a native speaker of Japanese. The native speaker is, for example, a native speaker or a language expert. The review evaluates, for example, the accuracy and fluency of the content. The feedback provides, for example, correction suggestions and comments. This allows translation accuracy to be improved based on reviews by native speakers of each language.

[0061] The translation unit analyzes users' emotional reactions to the translated article, and if there are many negative reactions, it can re-translate that part. The translation unit adds a function to, for example, analyze users' emotional reactions to the translated article in real time, and if there are many negative reactions, re-translate that part. For example, it analyzes user comments and feedback. Emotional reactions are collected using, for example, text analysis or facial expression recognition. Negative reactions are based on, for example, negative comments or low ratings. Re-translation involves, for example, correcting the translation or translating again. This makes it possible to analyze users' emotional reactions and re-translate parts that have many negative reactions.

[0062] The translation unit adds a voice readout function for each language to the translated article, thereby accommodating users who are visually impaired or have difficulty reading and writing. The translation unit, for example, adds a voice readout function to the translated article, thereby accommodating users who are visually impaired or have difficulty reading and writing. For example, an English article is translated into Japanese and the Japanese article is read aloud. The voice readout function uses, for example, text-to-speech software or speech synthesis technology. Users who are visually impaired use, for example, audio guides or Braille. Users who have difficulty reading and writing use, for example, audio guides or simple language. This makes it possible to accommodate users who are visually impaired or have difficulty reading and writing.

[0063] The translation unit automatically generates sign language videos in each language for the translated article, making it possible to accommodate hearing-impaired people. For example, the translation unit adds a function to automatically generate sign language videos in each language for the translated article. For example, an English article is translated into Japanese, and a sign language video corresponding to the Japanese article is generated. The sign language video is created, for example, using a sign language interpreter or sign language animation. Hearing-impaired people use, for example, sign language interpreters and subtitles. This makes it possible to accommodate hearing-impaired people.

[0064] The translation unit uses an emotion estimation function to collect users' emotional reactions to the translated article, thereby continuously improving the translation accuracy for each language. The translation unit, for example, uses the emotion estimation function to collect users' emotional reactions to the translated article. For example, the translation unit analyzes the user's facial expressions and voice and calculates an emotion score. The emotion estimation function, for example, estimates emotions using an emotion analysis algorithm or natural language processing technology. The emotional reactions are collected using text analysis or facial expression recognition, for example. The translation accuracy is evaluated based on, for example, accuracy and fluency. This allows the translation accuracy for each language to continuously improve.

[0065] The advertisement management unit can dynamically change the display position or content of advertisements based on the user's browsing history or interests, thereby maximizing the effectiveness of advertisements. The advertisement management unit, for example, dynamically changes the display position of advertisements based on the user's browsing history. For example, it adjusts the position at which advertisements related to the content of pages the user has previously viewed are displayed. The display position may be, for example, the top of a page or a sidebar. The content may be, for example, a text advertisement or a banner advertisement. The browsing history is collected using, for example, cookies or browser history. The interests are identified using, for example, past search history or click history. The effectiveness of advertisements is evaluated based on, for example, click rate or conversion rate. This makes it possible to maximize the effectiveness of advertisements.

[0066] The advertising management unit can monitor the click rates and conversion rates of advertisements in real time and automatically replace ineffective advertisements. The advertising management unit, for example, adds a function to monitor the click rates and conversion rates of advertisements in real time and automatically replace ineffective advertisements. For example, an advertisement with a low click rate is replaced with another advertisement. The click rate is measured, for example, based on the number of clicks or the number of impressions. The conversion rate is measured, for example, based on the number of purchases or the number of registrations. An ineffective advertisement is, for example, an advertisement with a low click rate or conversion rate. Replacement is performed, for example, by selecting a new advertisement or changing the display. This makes it possible to automatically replace ineffective advertisements.

[0067] The advertisement management unit can analyze users' emotional reactions to the content of advertisements and automatically filter out advertisements that have many negative reactions. The advertisement management unit, for example, analyzes users' emotional reactions to the content of advertisements in real time and automatically filters out advertisements that have many negative reactions. For example, it analyzes users' facial expressions and comments. Emotional reactions are collected using, for example, text analysis or facial expression recognition. Negative reactions are based on, for example, negative comments or low ratings. Filtering is performed, for example, by stopping the display or changing the content. In this way, advertisements that have many negative reactions can be automatically filtered out.

[0068] The ad management unit provides a platform that allows advertisers to create and manage their own ads, thereby improving the customizability of ads. The ad management unit provides, for example, a platform that allows advertisers to create and manage their own ads. For example, it provides an interface that allows advertisers to freely customize the design and content of ads. The platform provides, for example, ad creation tools and a management dashboard. Customizability is evaluated based on, for example, the ability to change the ad design and targeting settings. This allows advertisers to provide a platform that allows them to create and manage their own ads, thereby improving the customizability of ads.

[0069] The advertising management unit can increase social value by adding a function to donate a portion of advertising revenue to a charity or social contribution activity selected by the user. The advertising management unit, for example, adds a function to donate a portion of advertising revenue to a charity or social contribution activity selected by the user. For example, it provides an interface that allows the user to select a donation destination. The charity is selected based on, for example, a certified NPO or a donation platform. The social contribution activity is selected based on, for example, environmental protection activities or educational support. Donations are made based on, for example, monetary donations or donations in kind. Social value is evaluated based on, for example, social impact and sustainability. In this way, social value can be increased by donating a portion of advertising revenue to a charity or social contribution activity.

[0070] The advertisement management unit can use the emotion estimation function to monitor the user's emotional response to the advertisement in real time and suggest advertisement content that will elicit a positive response. The advertisement management unit, for example, uses the emotion estimation function to monitor the user's emotional response to the advertisement in real time. For example, the advertisement management unit analyzes the user's facial expressions and voice and calculates an emotion score. The emotion estimation function estimates emotions using, for example, an emotion analysis algorithm or natural language processing technology. The emotional responses are collected using, for example, text analysis or facial expression recognition. The advertisement content is selected based on, for example, text advertisements or banner advertisements. Suggestions are made, for example, based on the user's interests or past data. This makes it possible to monitor the user's emotional response to the advertisement in real time and suggest advertisement content that will elicit a positive response.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] When generating articles using the generation AI, the poster agent can analyze the user's past posting history and customize the article based on the user's style and preferences. For example, it can analyze the writing style and tone of articles posted by the user in the past and reflect this in the new article. For example, the past posting history can be analyzed to determine the content and frequency of articles posted by the user. Customization can be achieved by, for example, adjusting the writing style or using specific keywords. This allows articles to be generated based on the user's style and preferences.

[0073] When generating articles using the generation AI, the poster agent can use the emotion estimation function to predict the impact that the content of the article will have on the user's emotions and adjust the content to have a positive impact. For example, if the user is feeling stressed, it can add relaxing content. The emotion estimation function, for example, analyzes the user's past behavior and feedback to estimate their emotions. The content can be adjusted by adding positive anecdotes or words of encouragement, for example. This allows the content of the article to be adjusted to have a positive impact on the user's emotions.

[0074] When generating articles using generative AI, the Author Agency can automatically cite data from other reliable sources to improve the credibility of the article. For example, for articles about science and technology, the agency cites the latest research papers and official announcements. Sources are selected based on, for example, academic databases and government agency websites. Citations are made using, for example, appropriate citation formats. This allows the credibility of the article to be improved by citing data from other reliable sources.

[0075] When generating articles using the generation AI, the author agent can use the emotion estimation function to predict what emotional response the article content will evoke in a specific target audience and adjust the content to suit the target audience. For example, articles aimed at younger generations can be given a casual tone or pop culture elements added. The emotion estimation function, for example, analyzes the target audience's age group and interests to estimate their emotions. Content adjustments can be made, for example, by changing the wording or examples to suit the target audience. This makes it possible to generate articles that are effective for a specific target audience.

[0076] The author agent can add relevant interactive elements to articles generated using the generation AI to increase user engagement. For example, interactive graphs and simulations can be inserted into articles about science and technology. Interactive elements are graphs and animations that users can manipulate by clicking and dragging. Engagement is evaluated based on, for example, the user's time spent on the page and the number of clicks. This makes it possible to increase user engagement by adding relevant interactive elements.

[0077] The poster agent can automatically display personalized recommended articles based on the user's past browsing history and interests in articles generated using the generation AI. For example, if a user has viewed many science and technology articles in the past, the latest related science and technology articles will be recommended. Browsing history is collected, for example, using cookies or browser history. Interests are identified, for example, using past search history or click history. Recommended articles are selected, for example, based on highly relevant or popular articles. This makes it possible to automatically display personalized recommended articles based on the user's past browsing history and interests.

[0078] The poster agent uses the emotion estimation function to monitor users' emotional reactions to articles generated by the generation AI in real time, and if there are many negative reactions, it can automatically modify those parts. For example, if a user expresses dissatisfaction with a part of an article, that part will be regenerated. The emotion estimation function, for example, analyzes the user's facial expressions and voice to estimate emotions. Emotional reactions are collected using, for example, text analysis and facial expression recognition. Modifications are made, for example, by changing the content of the article or adjusting the tone. This makes it possible to monitor users' emotional reactions in real time and automatically modify parts that receive many negative reactions.

[0079] When translating using generative AI, the translation unit can automatically recognize technical and specialized terms in each language and provide appropriate translations. For example, for medical articles, medical terms are accurately translated. Technical terms are, for example, terms and phrases used in specific fields. Technical terms are, for example, terms related to science, technology, and engineering. Appropriate translations are performed based on, for example, technical dictionaries and glossaries. This allows the system to automatically recognize technical and specialized terms in each language and provide appropriate translations.

[0080] The translation unit automatically requests reviews from native speakers of each language for the translated article, and can improve translation accuracy based on the feedback. For example, when an English article is translated into Japanese, a review is requested from a native Japanese speaker. A native speaker may be, for example, a native speaker or a language expert. The review may evaluate, for example, the accuracy and fluency of the content. The feedback may provide, for example, correction suggestions or comments. This allows translation accuracy to be improved based on reviews from native speakers of each language.

[0081] The translation unit can use an emotion estimation function to collect the user's emotional reactions to the translated article and continuously improve the translation accuracy for each language. For example, the emotion estimation function analyzes the user's facial expressions and voice and calculates an emotion score. The emotion estimation function estimates emotions using, for example, an emotion analysis algorithm or natural language processing technology. The emotional reactions are collected using, for example, text analysis or facial expression recognition. The translation accuracy is evaluated based on, for example, accuracy and fluency. This allows the translation accuracy for each language to continuously improve.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The contributor agent uses generation AI to act on behalf of Wikipedia contributors, updating articles and posting new ones. The generation AI collects the latest information from the internet and creates articles based on reliable sources. The generation AI also analyzes the latest research papers and news articles to generate accurate and comprehensive articles. For example, it receives a prompt such as "Please update articles about the latest science and technology," and generates an article. Step 2: The translation department translates the generated article into multiple languages ​​simultaneously. For example, an article generated in English can be automatically translated into Japanese, French, Chinese, etc., and posted to each language's Wikipedia page simultaneously. The translation department takes into account the cultural background and nuances of each language to provide a more natural translation. Step 3: The advertising management unit places paid advertisements in the generated articles. For example, advertisers can place advertisements on pages related to specific keywords or categories. The advertising management unit places advertisements for medical device manufacturers in articles related to medical care.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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, in order to avoid confusion and to 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.

[0150] 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]

[0151] 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 contributor agent department that uses generative AI to act on behalf of Wikipedia contributors; a translation unit that simultaneously translates the article generated by the poster proxy unit into multiple languages; an advertisement management unit that places paid advertisements in the articles translated by the translation unit; A system characterized by:

2. The poster agent unit: When generating articles using the generative AI, the tone and style of the article are adjusted based on the user's emotions.

2. The system of claim 1.

3. The poster agent unit: The article generated using the generative AI is automatically inserted with relevant images or videos to provide visually rich content.

2. The system of claim 1.

4. The translation unit When translating using the generative AI, it provides more natural translations based on the cultural background and nuances of each language.

2. The system of claim 1.

5. The advertisement management unit Dynamically change the display position or content of advertisements based on the user's browsing history or interests to maximize advertising effectiveness 2. The system of claim 1.

6. The poster agent unit: The AI ​​will monitor users' emotional reactions to the articles it generates in real time and make adjustments to elicit positive responses.

2. The system of claim 1.

7. The translation unit Analyze users' emotional reactions to the translated article, and if there are many negative reactions, retranslate that part.

2. The system of claim 1.

8. The advertisement management unit Analyze users' emotional reactions to the content of ads and automatically filter out ads with a high number of negative reactions.

2. The system of claim 1.

Images