system

Abstract

The system according to this embodiment aims to efficiently generate fictional articles and display advertisements. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and an advertising unit. The reception unit receives input from the user. The generation unit generates fictional articles based on the input received by the reception unit. The advertising unit places advertisements in the fictional articles generated by the generation unit.

Description

Technical Field

[0006] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to efficiently generate fictional articles and place advertisements. <已删除内容><已删除内容>The system according to the embodiment aims to efficiently generate fictional articles and place advertisements.

Means for Solving the Problems

Effects of the Invention

[0007] The system according to this embodiment can efficiently generate fictional articles and display advertisements. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The fictional news site system according to an embodiment of the present invention is a system that creates attractive synergistic effects by combining the popular fictional content "Fictional News" with large-scale language model (LLM) technology. The fictional news site system uses the LLM to generate unfounded information by utilizing a phenomenon called "Hallucination." While this phenomenon is generally considered problematic, in the production of "Fictional News," it is an advantage because the goal is to provide information that does not actually exist but has a believable quality. Next, revenue is generated by placing advertisements on the generated fictional articles. For example, companies that possess data from news sites are ideally positioned for this business model. The plan is to place advertisements on article pages and generate advertising revenue. This makes it easy to acquire training data and eliminates the need to pursue high accuracy, thus reducing production costs. Furthermore, the production of fictional news is made more efficient, allowing for the provision of more engaging content. Specifically, the fictional articles generated by the LLM are automatically distributed and provided to users. This mechanism allows users to enjoy "news that seems to exist but doesn't." For example, the LLM can generate a fictional article stating that "a new planet has been discovered," and advertising revenue can be generated by placing advertisements related to that article. Furthermore, LLM can generate fictional articles about "future technologies becoming a reality," and by displaying advertisements related to these articles, advertising revenue can be generated. In this way, by utilizing LLM technology, the production of satirical news can be made more efficient, and advertising revenue can be generated. In addition, it can provide attractive content to users, making it highly likely to be a successful business model. Thus, the satirical news system can provide attractive content to users and generate advertising revenue.

[0029] The fictional newspaper system according to this embodiment comprises a reception unit, a generation unit, and an advertising unit. The reception unit receives input from the user. User input includes, but is not limited to, text input, voice input, and image input. The reception unit provides, for example, an interface for receiving text input. The reception unit may also use a microphone and voice recognition technology for receiving voice input. Furthermore, the reception unit may also use a camera and image recognition technology for receiving image input. For example, the reception unit receives text data entered by the user and passes it to the generation unit. The generation unit generates a fictional article based on the input received by the reception unit using a generation AI. The generation AI generates a fictional article using, for example, a text generation AI (e.g., LLM). The generation unit can also generate a fictional article based on user input using a generation AI. For example, the generation unit generates a fictional article based on keywords entered by the user. The generation unit has a function to check the quality of the generated fictional article. For example, the generation unit checks the grammatical accuracy and content consistency of the generated fictional article. The advertising department places advertisements on the fictional articles generated by the generation department. The advertising department automatically selects and places advertisements related to the generated fictional articles, for example. The advertising department places advertisements in order to earn advertising revenue. For example, the advertising department selects advertisements related to the generated fictional articles and places them on the article page. The advertising department can also adjust how advertisements are displayed in order to earn advertising revenue. For example, the advertising department estimates the user's emotions and adjusts how advertisements are displayed based on the estimated user emotions. As a result, the fictional newspaper system according to this embodiment can generate fictional articles based on user input and earn revenue by placing advertisements.

[0030] The reception unit receives input from users. User input includes, but is not limited to, text input, voice input, and image input. The reception unit provides, for example, an interface for receiving text input. Specifically, it provides a form that allows users to easily enter text through a web browser or mobile application. The reception unit can also use a microphone and speech recognition technology to receive voice input. Speech recognition technology includes advanced speech analysis algorithms using natural language processing (NLP) that can accurately convert the user's voice into text. Furthermore, the reception unit can also use a camera and image recognition technology to receive image input. Image recognition technology includes algorithms using computer vision and deep learning that can extract text and objects from images uploaded by users. For example, the reception unit receives text data entered by the user and passes it to the generation unit. This allows the reception unit to support diverse input formats and create an environment where users can easily provide information. Furthermore, the reception unit can temporarily store user input data and reuse it as needed. For example, it can be used to generate relevant articles based on data previously entered by the user. This allows the reception department to improve user convenience and increase the overall efficiency of the system.

[0031] The generation unit uses a generation AI to generate fictional articles based on input received by the reception unit. The generation AI uses, for example, a text generation AI (e.g., LLM) to generate fictional articles. Specifically, the generation AI generates relevant content based on keywords and phrases entered by the user. The generation AI has learned from a large amount of text data and can generate grammatically accurate and consistent sentences. The generation unit can also use the generation AI to generate fictional articles based on user input. For example, the generation unit generates fictional articles based on keywords entered by the user. The generation unit has a function to check the quality of the generated fictional articles. Specifically, the generation unit checks the grammatical accuracy and content consistency of the generated fictional articles. For grammatical checking, an algorithm using natural language processing technology is used to detect sentence structure and grammatical errors. For content consistency checking, an algorithm is used to verify the semantic consistency of the sentences generated by the generation AI. This allows the generation unit to generate and provide high-quality fictional articles to users. Furthermore, the generation unit also has a function to customize the topic and style of the generated articles. For example, if a user specifies a particular genre or tone, the system can generate articles that meet those requirements. This allows the generation unit to provide diverse content tailored to user needs, improving the overall flexibility and adaptability of the system.

[0032] The advertising department places advertisements on fictional articles generated by the content creation department. For example, the advertising department automatically selects and places advertisements relevant to the generated fictional articles. Specifically, the advertising department analyzes the content and keywords of the generated articles and selects relevant advertisements from a database. The advertising department places advertisements to earn advertising revenue. For example, the advertising department selects advertisements relevant to the generated fictional articles and places them on the article pages. The advertising department can also adjust how advertisements are displayed to earn advertising revenue. For example, the advertising department estimates the user's emotions and adjusts how advertisements are displayed based on the estimated user emotions. Facial recognition technology and voice analysis technology can be used to estimate user emotions. This allows the advertising department to display advertisements that match the user's emotions and maximize the effectiveness of the advertisements. Furthermore, the advertising department monitors the performance of advertisements and collects data to develop effective advertising strategies. For example, it analyzes the click-through rate and conversion rate of advertisements to determine the optimal advertisement placement and display method. The advertising department can also strengthen collaboration with advertisers and provide feedback to maximize the effectiveness of advertising campaigns. This allows the advertising department to maximize advertising revenue and ensure the overall economic sustainability of the system.

[0033] The generation unit can generate fictional articles using a generation AI. The generation unit generates fictional articles using, for example, a text generation AI (e.g., LLM). The generation unit can also generate fictional articles based on user input using a generation AI. For example, the generation unit generates fictional articles based on keywords entered by the user. The generation unit has a function to check the quality of the generated fictional articles. For example, the generation unit checks the grammatical accuracy and content consistency of the generated fictional articles. This makes the generation of fictional articles more efficient by using a generation AI. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input keywords entered by the user into the generation AI and have the generation AI perform the generation of fictional articles.

[0034] The generation unit can generate fictional articles based on user input using a generation AI. For example, the generation unit can generate fictional articles based on keywords entered by the user. The generation unit can also generate fictional articles based on user input using a generation AI. For example, the generation unit can input text data entered by the user into the generation AI and have the generation AI generate a fictional article. The generation unit has a function to check the quality of the generated fictional articles. For example, the generation unit checks the grammatical accuracy and content consistency of the generated fictional articles. This allows the generation unit to provide articles that are tailored to the user's interests by generating fictional articles based on user input. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without using a generation AI.

[0035] The advertising department can automatically select and display advertisements related to the generated fictional articles. For example, the advertising department selects advertisements related to the generated fictional articles and displays them on the article page. The advertising department displays advertisements to earn advertising revenue. For example, the advertising department selects advertisements related to the generated fictional articles and displays them on the article page. The advertising department can also adjust how advertisements are displayed to earn advertising revenue. For example, the advertising department estimates the user's sentiment and adjusts how advertisements are displayed based on the estimated user sentiment. This can enhance the effectiveness of advertisements by automatically selecting and displaying advertisements related to fictional articles. Some or all of the above processes in the advertising department may be performed using AI, for example, or not using AI. For example, the advertising department can input the content of the generated fictional articles into AI and have the AI ​​select relevant advertisements.

[0036] The generation unit may have a function to check the quality of the generated fictional articles. For example, the generation unit may check the grammatical accuracy and content consistency of the generated fictional articles. The generation unit may also use a generation AI to check the quality of the generated fictional articles. For example, the generation unit may have the generation AI check the grammatical accuracy of the generated fictional articles. The generation unit may also have the generation AI check the content consistency of the generated fictional articles. By checking the quality of the generated fictional articles, the quality of the articles can be maintained. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI.

[0037] The advertising department can place advertisements to generate advertising revenue. For example, the advertising department can select advertisements related to the generated fictional articles and place them on the article pages. The advertising department can also adjust how advertisements are displayed to generate advertising revenue. For example, the advertising department can estimate the user's emotions and adjust how advertisements are displayed based on the estimated user emotions. This allows the advertising department to increase revenue by placing advertisements to generate advertising revenue. Some or all of the above processes in the advertising department may be performed using AI, for example, or not using AI. For example, the advertising department can input the content of the generated fictional articles into AI and have the AI ​​select relevant advertisements.

[0038] The reception desk can analyze the user's past input history and select the optimal reception method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. The reception desk can also suggest similar input methods by referring to content the user has entered in the past. In this way, the reception desk can provide the optimal reception method by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history data into AI and have the AI ​​select the optimal reception method.

[0039] The reception unit can filter input based on the user's current areas of interest. For example, the reception unit can prioritize inputs based on topics the user has recently been interested in. The reception unit can also analyze the user's past search history and filter inputs related to their areas of interest. The reception unit can also filter inputs based on topics the user has shown interest in on social media. This allows the reception unit to receive highly relevant inputs by filtering based on the user's areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's areas of interest data into AI and have the AI ​​perform the filtering.

[0040] The reception unit can prioritize receiving inputs that are highly relevant based on the user's geographical location information. For example, the reception unit can prioritize receiving information related to the user's current location. The reception unit can also analyze the user's past location information and prioritize receiving highly relevant inputs. If the user is in a specific region, the reception unit can also prioritize receiving information related to that region. This allows the reception unit to provide users with useful information by prioritizing the reception of highly relevant inputs based on their geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into AI and have the AI ​​select highly relevant inputs.

[0041] The reception unit can analyze the user's social media activity and accept relevant inputs when receiving input. For example, the reception unit can prioritize accepting relevant inputs based on topics the user is interested in on social media. The reception unit can also analyze the user's social media activity history and accept relevant inputs. The reception unit can also accept relevant inputs based on accounts the user follows on social media. In this way, relevant inputs can be accepted by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into AI and have the AI ​​select relevant inputs.

[0042] The generation unit can adjust the level of detail of the generated fictional articles based on the user's past input. For example, if the user has previously preferred detailed information, the generation unit will generate a detailed fictional article. If the user has previously preferred concise information, the generation unit can also generate a concise fictional article. The generation unit can also analyze the user's past input and generate a fictional article with an appropriate level of detail. By adjusting the level of detail based on the user's past input, the generation unit can provide articles that match the user's preferences. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's past input data into a generation AI and have the generation AI adjust the level of detail of the fictional articles.

[0043] The generation unit can apply different generation algorithms depending on the category when generating fictional articles. For example, the generation unit can apply a generation algorithm that makes extensive use of scientific terminology and expressions to articles in the science category. The generation unit can also apply a generation algorithm that makes extensive use of humorous expressions to articles in the entertainment category. The generation unit can also apply a generation algorithm that makes extensive use of political terminology and expressions to articles in the politics category. In this way, by applying a generation algorithm according to different categories, it is possible to generate articles that are appropriate for each category. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data from different categories into a generation AI and have the generation AI execute the application of a generation algorithm appropriate to the category.

[0044] The generation unit can determine the generation priority based on the user's input timing when generating fictional articles. For example, if the user inputs during a specific time period, the generation unit will prioritize generating fictional articles related to that time period. If the user inputs during a specific event, the generation unit can also prioritize generating fictional articles related to that event. If the user inputs during a specific season, the generation unit can also prioritize generating fictional articles related to that season. By determining the generation priority based on the user's input timing, timely articles can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user input timing data into a generation AI and have the generation AI determine the generation priority.

[0045] The generation unit can improve the accuracy of its fictional articles by referring to relevant news data during the generation process. For example, the generation unit can refer to the latest news data to make the content of the fictional articles more realistic. The generation unit can also refer to past news data to enrich the background information of the fictional articles. The generation unit can also refer to relevant news data to increase the credibility of the fictional articles. In this way, the accuracy of the fictional articles can be improved by referring to relevant news data. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input news data into a generation AI and have the generation AI perform the generation of fictional articles.

[0046] The advertising department can select the most suitable advertisements based on the content of the generated fictional articles when placing advertisements. For example, the advertising department can place advertisements for science-related products and services in fictional articles in the science category. The advertising department can also place advertisements for entertainment-related products and services in fictional articles in the entertainment category. The advertising department can also place advertisements for politics-related products and services in fictional articles in the politics category. This allows for improved advertising effectiveness by selecting the most suitable advertisements based on the content of the generated fictional articles. Some or all of the above processing in the advertising department may be performed using AI, for example, or not. For example, the advertising department can input the content data of the fictional articles into an AI and have the AI ​​select the most suitable advertisements.

[0047] The advertising department can analyze a user's past click history to select ads when placing them. For example, the advertising department can prioritize displaying relevant ads based on ads the user has clicked in the past. The advertising department can also analyze a user's past click history to select ads that are likely to be of interest to the user. The advertising department can also display ads in the same category as ads the user has clicked in the past. This allows for the selection of highly relevant ads by analyzing a user's past click history. Some or all of the above processes in the advertising department may be performed using AI, for example, or not. For example, the advertising department can input user click history data into an AI and have the AI ​​perform ad selection.

[0048] The advertising department can select the most relevant advertisements based on the user's geographical location when placing ads. For example, the advertising department can prioritize displaying advertisements for products and services related to the user's current location. The advertising department can also analyze the user's past location data to select relevant advertisements. If the user is in a specific region, the advertising department can prioritize displaying advertisements related to that region. This allows for improved advertising effectiveness by selecting the most relevant advertisements based on the user's geographical location. Some or all of the above processes in the advertising department may be performed using AI, for example, or not. For example, the advertising department can input the user's geographical location data into an AI and have the AI ​​select the most relevant advertisements.

[0049] The advertising department can analyze users' social media activity when placing ads and display relevant ads. For example, the advertising department can prioritize ads related to products or services that users have shown interest in on social media. The advertising department can also analyze users' social media activity history and display relevant ads. The advertising department can also display ads related to brands that users follow on social media. This allows for the display of highly relevant ads by analyzing users' social media activity. Some or all of the above processes in the advertising department may be performed using AI, for example, or not. For example, the advertising department can input user social media activity data into AI and have the AI ​​select relevant ads.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The Fictional News system can further analyze a user's past browsing history and prioritize generating fictional articles that are likely to interest the user. For example, if a user has previously viewed many science-related articles, the generation unit will prioritize generating science-related fictional articles. Similarly, if a user has viewed many entertainment-related articles, it can generate entertainment-related fictional articles. Furthermore, if a user shows interest in a specific topic, it can generate fictional articles related to that topic. This allows the system to provide more engaging fictional articles based on the user's past browsing history.

[0052] The Fictional News system can further generate fictional articles relevant to a user's geographical location. For example, if a user is in a specific city, it can generate fictional articles related to that city. Similarly, if a user is in a specific country, it can generate fictional articles related to that country. Furthermore, if a user is traveling, it can generate fictional articles related to their travel destination. This allows the system to provide more relevant fictional articles based on the user's geographical location.

[0053] The Fictional News system can further analyze users' social media activity and generate fictional articles related to topics that users are interested in. For example, if a user frequently uses a particular hashtag on social media, it can generate fictional articles related to that hashtag. It can also generate fictional articles related to accounts that users follow. Furthermore, if a user frequently posts about a particular topic on social media, it can generate fictional articles related to that topic. This allows the system to provide more engaging fictional articles based on users' social media activity.

[0054] The Fictional News system can further analyze a user's past purchase history and display relevant advertisements. For example, it can display advertisements related to products the user has previously purchased. It can also display advertisements for products and services in the same category as the products the user has previously purchased, based on the category of products the user has previously bought. Furthermore, it can display advertisements related to the brands of products the user has previously purchased. This allows the system to provide more relevant advertisements based on the user's past purchase history.

[0055] The Fictional News system can further analyze a user's past search history and generate relevant fictional articles. For example, it can generate fictional articles related to keywords a user has previously searched for. It can also generate fictional articles related to topics a user has previously searched for. Furthermore, it can generate similar fictional articles by referencing content a user has previously searched for. This allows the system to provide more engaging fictional articles based on the user's past search history.

[0056] The Fictional News system can further analyze a user's past subscription history and generate relevant fictional articles. For example, it can generate fictional articles related to categories of articles a user has previously subscribed to. It can also generate fictional articles related to topics a user has previously subscribed to. Furthermore, it can generate similar fictional articles by referencing the content of articles a user has previously subscribed to. This allows the system to provide more engaging fictional articles based on the user's past subscription history.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The reception desk receives input from the user. User input includes text input, voice input, and image input. For example, the reception desk can provide an interface for receiving text input, a microphone and voice recognition technology for receiving voice input, and a camera and image recognition technology for receiving image input. Step 2: The generation unit generates a fictional article based on the input received by the reception unit. The generation unit uses a generation AI, for example, a text generation AI (e.g., LLM), to generate the fictional article. The generation unit has a function to generate a fictional article based on keywords entered by the user and to check the quality of the generated fictional article. For example, the generation unit checks the grammatical accuracy and content consistency of the generated fictional article. Step 3: The advertising department places advertisements on the fictional articles generated by the generation department. The advertising department automatically selects and displays advertisements relevant to the generated fictional articles. The advertising department can also adjust how the advertisements are displayed in order to generate advertising revenue. For example, the advertising department can estimate the user's sentiment and adjust how the advertisements are displayed based on the estimated user sentiment.

[0059] (Example of form 2) The fictional news site system according to an embodiment of the present invention is a system that creates attractive synergistic effects by combining the popular fictional content "Fictional News" with large-scale language model (LLM) technology. The fictional news site system uses the LLM to generate unfounded information by utilizing a phenomenon called "Hallucination." While this phenomenon is generally considered problematic, in the production of "Fictional News," it is an advantage because the goal is to provide information that does not actually exist but has a believable quality. Next, revenue is generated by placing advertisements on the generated fictional articles. For example, companies that possess data from news sites are ideally positioned for this business model. The plan is to place advertisements on article pages and generate advertising revenue. This makes it easy to acquire training data and eliminates the need to pursue high accuracy, thus reducing production costs. Furthermore, the production of fictional news is made more efficient, allowing for the provision of more engaging content. Specifically, the fictional articles generated by the LLM are automatically distributed and provided to users. This mechanism allows users to enjoy "news that seems to exist but doesn't." For example, the LLM can generate a fictional article stating that "a new planet has been discovered," and advertising revenue can be generated by placing advertisements related to that article. Furthermore, LLM can generate fictional articles about "future technologies becoming a reality," and by displaying advertisements related to these articles, advertising revenue can be generated. In this way, by utilizing LLM technology, the production of satirical news can be made more efficient, and advertising revenue can be generated. In addition, it can provide attractive content to users, making it highly likely to be a successful business model. Thus, the satirical news system can provide attractive content to users and generate advertising revenue.

[0060] The fictional newspaper system according to this embodiment comprises a reception unit, a generation unit, and an advertising unit. The reception unit receives input from the user. User input includes, but is not limited to, text input, voice input, and image input. The reception unit provides, for example, an interface for receiving text input. The reception unit may also use a microphone and voice recognition technology for receiving voice input. Furthermore, the reception unit may also use a camera and image recognition technology for receiving image input. For example, the reception unit receives text data entered by the user and passes it to the generation unit. The generation unit generates a fictional article based on the input received by the reception unit using a generation AI. The generation AI generates a fictional article using, for example, a text generation AI (e.g., LLM). The generation unit can also generate a fictional article based on user input using a generation AI. For example, the generation unit generates a fictional article based on keywords entered by the user. The generation unit has a function to check the quality of the generated fictional article. For example, the generation unit checks the grammatical accuracy and content consistency of the generated fictional article. The advertising department places advertisements on the fictional articles generated by the generation department. The advertising department automatically selects and places advertisements related to the generated fictional articles, for example. The advertising department places advertisements in order to earn advertising revenue. For example, the advertising department selects advertisements related to the generated fictional articles and places them on the article page. The advertising department can also adjust how advertisements are displayed in order to earn advertising revenue. For example, the advertising department estimates the user's emotions and adjusts how advertisements are displayed based on the estimated user emotions. As a result, the fictional newspaper system according to this embodiment can generate fictional articles based on user input and earn revenue by placing advertisements.

[0061] The reception unit receives input from users. User input includes, but is not limited to, text input, voice input, and image input. The reception unit provides, for example, an interface for receiving text input. Specifically, it provides a form that allows users to easily enter text through a web browser or mobile application. The reception unit can also use a microphone and speech recognition technology to receive voice input. Speech recognition technology includes advanced speech analysis algorithms using natural language processing (NLP) that can accurately convert the user's voice into text. Furthermore, the reception unit can also use a camera and image recognition technology to receive image input. Image recognition technology includes algorithms using computer vision and deep learning that can extract text and objects from images uploaded by users. For example, the reception unit receives text data entered by the user and passes it to the generation unit. This allows the reception unit to support diverse input formats and create an environment where users can easily provide information. Furthermore, the reception unit can temporarily store user input data and reuse it as needed. For example, it can be used to generate relevant articles based on data previously entered by the user. This allows the reception department to improve user convenience and increase the overall efficiency of the system.

[0062] The generation unit uses a generation AI to generate fictional articles based on input received by the reception unit. The generation AI uses, for example, a text generation AI (e.g., LLM) to generate fictional articles. Specifically, the generation AI generates relevant content based on keywords and phrases entered by the user. The generation AI has learned from a large amount of text data and can generate grammatically accurate and consistent sentences. The generation unit can also use the generation AI to generate fictional articles based on user input. For example, the generation unit generates fictional articles based on keywords entered by the user. The generation unit has a function to check the quality of the generated fictional articles. Specifically, the generation unit checks the grammatical accuracy and content consistency of the generated fictional articles. For grammatical checking, an algorithm using natural language processing technology is used to detect sentence structure and grammatical errors. For content consistency checking, an algorithm is used to verify the semantic consistency of the sentences generated by the generation AI. This allows the generation unit to generate and provide high-quality fictional articles to users. Furthermore, the generation unit also has a function to customize the topic and style of the generated articles. For example, if a user specifies a particular genre or tone, the system can generate articles that meet those requirements. This allows the generation unit to provide diverse content tailored to user needs, improving the overall flexibility and adaptability of the system.

[0063] The advertising department places advertisements on fictional articles generated by the content creation department. For example, the advertising department automatically selects and places advertisements relevant to the generated fictional articles. Specifically, the advertising department analyzes the content and keywords of the generated articles and selects relevant advertisements from a database. The advertising department places advertisements to earn advertising revenue. For example, the advertising department selects advertisements relevant to the generated fictional articles and places them on the article pages. The advertising department can also adjust how advertisements are displayed to earn advertising revenue. For example, the advertising department estimates the user's emotions and adjusts how advertisements are displayed based on the estimated user emotions. Facial recognition technology and voice analysis technology can be used to estimate user emotions. This allows the advertising department to display advertisements that match the user's emotions and maximize the effectiveness of the advertisements. Furthermore, the advertising department monitors the performance of advertisements and collects data to develop effective advertising strategies. For example, it analyzes the click-through rate and conversion rate of advertisements to determine the optimal advertisement placement and display method. The advertising department can also strengthen collaboration with advertisers and provide feedback to maximize the effectiveness of advertising campaigns. This allows the advertising department to maximize advertising revenue and ensure the overall economic sustainability of the system.

[0064] The generation unit can generate fictional articles using a generation AI. The generation unit generates fictional articles using, for example, a text generation AI (e.g., LLM). The generation unit can also generate fictional articles based on user input using a generation AI. For example, the generation unit generates fictional articles based on keywords entered by the user. The generation unit has a function to check the quality of the generated fictional articles. For example, the generation unit checks the grammatical accuracy and content consistency of the generated fictional articles. This makes the generation of fictional articles more efficient by using a generation AI. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input keywords entered by the user into the generation AI and have the generation AI perform the generation of fictional articles.

[0065] The generation unit can generate fictional articles based on user input using a generation AI. For example, the generation unit can generate fictional articles based on keywords entered by the user. The generation unit can also generate fictional articles based on user input using a generation AI. For example, the generation unit can input text data entered by the user into the generation AI and have the generation AI generate a fictional article. The generation unit has a function to check the quality of the generated fictional articles. For example, the generation unit checks the grammatical accuracy and content consistency of the generated fictional articles. This allows the generation unit to provide articles that are tailored to the user's interests by generating fictional articles based on user input. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without using a generation AI.

[0066] The advertising department can automatically select and display advertisements related to the generated fictional articles. For example, the advertising department selects advertisements related to the generated fictional articles and displays them on the article page. The advertising department displays advertisements to earn advertising revenue. For example, the advertising department selects advertisements related to the generated fictional articles and displays them on the article page. The advertising department can also adjust how advertisements are displayed to earn advertising revenue. For example, the advertising department estimates the user's sentiment and adjusts how advertisements are displayed based on the estimated user sentiment. This can enhance the effectiveness of advertisements by automatically selecting and displaying advertisements related to fictional articles. Some or all of the above processes in the advertising department may be performed using AI, for example, or not using AI. For example, the advertising department can input the content of the generated fictional articles into AI and have the AI ​​select relevant advertisements.

[0067] The generation unit may have a function to check the quality of the generated fictional articles. For example, the generation unit may check the grammatical accuracy and content consistency of the generated fictional articles. The generation unit may also use a generation AI to check the quality of the generated fictional articles. For example, the generation unit may have the generation AI check the grammatical accuracy of the generated fictional articles. The generation unit may also have the generation AI check the content consistency of the generated fictional articles. By checking the quality of the generated fictional articles, the quality of the articles can be maintained. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI.

[0068] The advertising department can place advertisements to generate advertising revenue. For example, the advertising department can select advertisements related to the generated fictional articles and place them on the article pages. The advertising department can also adjust how advertisements are displayed to generate advertising revenue. For example, the advertising department can estimate the user's emotions and adjust how advertisements are displayed based on the estimated user emotions. This allows the advertising department to increase revenue by placing advertisements to generate advertising revenue. Some or all of the above processes in the advertising department may be performed using AI, for example, or not using AI. For example, the advertising department can input the content of the generated fictional articles into AI and have the AI ​​select relevant advertisements.

[0069] The reception desk can estimate the user's emotions and adjust the timing of input reception based on the estimated emotions. For example, if the user is excited, the reception desk can speed up the input reception and respond quickly. If the user is relaxed, the reception desk can also delay the input reception and respond slowly. If the user is stressed, the reception desk can adjust the input reception timing and wait until the user calms down. By adjusting the input reception timing according to the user's emotions, user satisfaction can be improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0070] The reception desk can analyze the user's past input history and select the optimal reception method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. The reception desk can also suggest similar input methods by referring to content the user has entered in the past. In this way, the reception desk can provide the optimal reception method by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history data into AI and have the AI ​​select the optimal reception method.

[0071] The reception unit can filter input based on the user's current areas of interest. For example, the reception unit can prioritize inputs based on topics the user has recently been interested in. The reception unit can also analyze the user's past search history and filter inputs related to their areas of interest. The reception unit can also filter inputs based on topics the user has shown interest in on social media. This allows the reception unit to receive highly relevant inputs by filtering based on the user's areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's areas of interest data into AI and have the AI ​​perform the filtering.

[0072] The reception desk can estimate the user's emotions and determine the priority of inputs to be received based on the estimated emotions. For example, if the user is excited, the reception desk can set a high priority for the input and respond quickly. If the user is relaxed, the reception desk can also set a low priority for the input and respond slowly. If the user is stressed, the reception desk can adjust the priority of the input and wait until the user calms down. This allows for a quick and appropriate response by determining the priority of inputs according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0073] The reception unit can prioritize receiving inputs that are highly relevant based on the user's geographical location information. For example, the reception unit can prioritize receiving information related to the user's current location. The reception unit can also analyze the user's past location information and prioritize receiving highly relevant inputs. If the user is in a specific region, the reception unit can also prioritize receiving information related to that region. This allows the reception unit to provide users with useful information by prioritizing the reception of highly relevant inputs based on their geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into AI and have the AI ​​select highly relevant inputs.

[0074] The reception unit can analyze the user's social media activity and accept relevant inputs when receiving input. For example, the reception unit can prioritize accepting relevant inputs based on topics the user is interested in on social media. The reception unit can also analyze the user's social media activity history and accept relevant inputs. The reception unit can also accept relevant inputs based on accounts the user follows on social media. In this way, relevant inputs can be accepted by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into AI and have the AI ​​select relevant inputs.

[0075] The generation unit can estimate the user's emotions and adjust the expression of the fictional article based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a fictional article that uses a lot of humorous expressions. If the user is excited, the generation unit can also generate a fictional article that uses a lot of stimulating expressions. If the user is stressed, the generation unit can also generate a fictional article that uses a lot of calming expressions. In this way, by adjusting the expression of the fictional article according to the user's emotions, it is possible to provide articles that are appealing to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the expression of the fictional article.

[0076] The generation unit can adjust the level of detail of the generated fictional articles based on the user's past input. For example, if the user has previously preferred detailed information, the generation unit will generate a detailed fictional article. If the user has previously preferred concise information, the generation unit can also generate a concise fictional article. The generation unit can also analyze the user's past input and generate a fictional article with an appropriate level of detail. By adjusting the level of detail based on the user's past input, the generation unit can provide articles that match the user's preferences. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's past input data into a generation AI and have the generation AI adjust the level of detail of the fictional articles.

[0077] The generation unit can apply different generation algorithms depending on the category when generating fictional articles. For example, the generation unit can apply a generation algorithm that makes extensive use of scientific terminology and expressions to articles in the science category. The generation unit can also apply a generation algorithm that makes extensive use of humorous expressions to articles in the entertainment category. The generation unit can also apply a generation algorithm that makes extensive use of political terminology and expressions to articles in the politics category. In this way, by applying a generation algorithm according to different categories, it is possible to generate articles that are appropriate for each category. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data from different categories into a generation AI and have the generation AI execute the application of a generation algorithm appropriate to the category.

[0078] The generation unit can estimate the user's emotions and adjust the length of the fictional article based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short fictional article. If the user is relaxed, the generation unit can also generate a long fictional article. If the user is excited, the generation unit can also generate a fictional article of an appropriate length. In this way, by adjusting the length of the fictional article according to the user's emotions, it is possible to provide articles that are appropriate to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the length of the fictional article.

[0079] The generation unit can determine the generation priority based on the user's input timing when generating fictional articles. For example, if the user inputs during a specific time period, the generation unit will prioritize generating fictional articles related to that time period. If the user inputs during a specific event, the generation unit can also prioritize generating fictional articles related to that event. If the user inputs during a specific season, the generation unit can also prioritize generating fictional articles related to that season. By determining the generation priority based on the user's input timing, timely articles can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user input timing data into a generation AI and have the generation AI determine the generation priority.

[0080] The generation unit can improve the accuracy of its fictional articles by referring to relevant news data during the generation process. For example, the generation unit can refer to the latest news data to make the content of the fictional articles more realistic. The generation unit can also refer to past news data to enrich the background information of the fictional articles. The generation unit can also refer to relevant news data to increase the credibility of the fictional articles. In this way, the accuracy of the fictional articles can be improved by referring to relevant news data. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input news data into a generation AI and have the generation AI perform the generation of fictional articles.

[0081] The advertising department can estimate a user's emotions and adjust how ads are displayed based on those emotions. For example, if a user is relaxed, the advertising department can display visually appealing ads. If a user is excited, the advertising department can also display stimulating ads. If a user is stressed, the advertising department can also display calming ads. This allows for increased advertising effectiveness by adjusting how ads are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advertising department may be performed using AI or not. For example, the advertising department can input user emotion data into a generative AI and have the generative AI adjust how ads are displayed.

[0082] The advertising department can select the most suitable advertisements based on the content of the generated fictional articles when placing advertisements. For example, the advertising department can place advertisements for science-related products and services in fictional articles in the science category. The advertising department can also place advertisements for entertainment-related products and services in fictional articles in the entertainment category. The advertising department can also place advertisements for politics-related products and services in fictional articles in the politics category. This allows for improved advertising effectiveness by selecting the most suitable advertisements based on the content of the generated fictional articles. Some or all of the above processing in the advertising department may be performed using AI, for example, or not. For example, the advertising department can input the content data of the fictional articles into an AI and have the AI ​​select the most suitable advertisements.

[0083] The advertising department can analyze a user's past click history to select ads when placing them. For example, the advertising department can prioritize displaying relevant ads based on ads the user has clicked in the past. The advertising department can also analyze a user's past click history to select ads that are likely to be of interest to the user. The advertising department can also display ads in the same category as ads the user has clicked in the past. This allows for the selection of highly relevant ads by analyzing a user's past click history. Some or all of the above processes in the advertising department may be performed using AI, for example, or not. For example, the advertising department can input user click history data into an AI and have the AI ​​perform ad selection.

[0084] The advertising department can estimate the user's emotions and prioritize ads based on those emotions. For example, if the user is relaxed, the advertising department might prioritize displaying visually appealing ads. If the user is excited, the advertising department might prioritize displaying stimulating ads. If the user is stressed, the advertising department might prioritize displaying calming ads. This allows for increased ad effectiveness by prioritizing ads according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advertising department may be performed using AI or not. For example, the advertising department could input user emotion data into a generative AI and have the generative AI determine ad prioritization.

[0085] The advertising department can select the most relevant advertisements based on the user's geographical location when placing ads. For example, the advertising department can prioritize displaying advertisements for products and services related to the user's current location. The advertising department can also analyze the user's past location data to select relevant advertisements. If the user is in a specific region, the advertising department can prioritize displaying advertisements related to that region. This allows for improved advertising effectiveness by selecting the most relevant advertisements based on the user's geographical location. Some or all of the above processes in the advertising department may be performed using AI, for example, or not. For example, the advertising department can input the user's geographical location data into an AI and have the AI ​​select the most relevant advertisements.

[0086] The advertising department can analyze users' social media activity when placing ads and display relevant ads. For example, the advertising department can prioritize ads related to products or services that users have shown interest in on social media. The advertising department can also analyze users' social media activity history and display relevant ads. The advertising department can also display ads related to brands that users follow on social media. This allows for the display of highly relevant ads by analyzing users' social media activity. Some or all of the above processes in the advertising department may be performed using AI, for example, or not. For example, the advertising department can input user social media activity data into AI and have the AI ​​select relevant ads.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The Fictional News system can further analyze a user's past browsing history and prioritize generating fictional articles that are likely to interest the user. For example, if a user has previously viewed many science-related articles, the generation unit will prioritize generating science-related fictional articles. Similarly, if a user has viewed many entertainment-related articles, it can generate entertainment-related fictional articles. Furthermore, if a user shows interest in a specific topic, it can generate fictional articles related to that topic. This allows the system to provide more engaging fictional articles based on the user's past browsing history.

[0089] The Fictional News system can further estimate the user's emotions and adjust the tone of the fictional articles based on those emotions. For example, if the user is sad, the generator can produce a fictional article with a humorous tone to soothe the user's mood. If the user is excited, the generator can produce a fictional article with a stimulating tone. Furthermore, if the user is relaxed, the generator can produce a fictional article with a calm tone. By providing fictional articles with a tone that matches the user's emotions, user satisfaction can be improved.

[0090] The Fictional News system can further generate fictional articles relevant to a user's geographical location. For example, if a user is in a specific city, it can generate fictional articles related to that city. Similarly, if a user is in a specific country, it can generate fictional articles related to that country. Furthermore, if a user is traveling, it can generate fictional articles related to their travel destination. This allows the system to provide more relevant fictional articles based on the user's geographical location.

[0091] The Fictional News system can further analyze users' social media activity and generate fictional articles related to topics that users are interested in. For example, if a user frequently uses a particular hashtag on social media, it can generate fictional articles related to that hashtag. It can also generate fictional articles related to accounts that users follow. Furthermore, if a user frequently posts about a particular topic on social media, it can generate fictional articles related to that topic. This allows the system to provide more engaging fictional articles based on users' social media activity.

[0092] The Fictional Newspaper system can further estimate the user's emotions and adjust the timing of ad display based on those emotions. For example, if the user is relaxed, the advertising department will delay displaying the ad, showing it after the user has finished reading the article. If the user is excited, the advertising department will advance the timing of the ad, even displaying it in the middle of the article. Furthermore, if the user is stressed, the advertising department can temporarily refrain from displaying the ad and wait until the user calms down. In this way, the effectiveness of ads can be enhanced by adjusting the timing of ad display according to the user's emotions.

[0093] The Fictional News system can further analyze a user's past purchase history and display relevant advertisements. For example, it can display advertisements related to products the user has previously purchased. It can also display advertisements for products and services in the same category as the products the user has previously purchased, based on the category of products the user has previously bought. Furthermore, it can display advertisements related to the brands of products the user has previously purchased. This allows the system to provide more relevant advertisements based on the user's past purchase history.

[0094] The Fictional Newspaper system can further estimate the user's emotions and adjust the content of advertisements based on those emotions. For example, if the user is relaxed, the advertising department will display visually appealing advertisements. If the user is excited, the advertising department may display stimulating advertisements. Furthermore, if the user is stressed, the advertising department may display calming advertisements. In this way, the effectiveness of advertisements can be enhanced by adjusting the content of advertisements according to the user's emotions.

[0095] The Fictional News system can further analyze a user's past search history and generate relevant fictional articles. For example, it can generate fictional articles related to keywords a user has previously searched for. It can also generate fictional articles related to topics a user has previously searched for. Furthermore, it can generate similar fictional articles by referencing content a user has previously searched for. This allows the system to provide more engaging fictional articles based on the user's past search history.

[0096] The Fictional News system can also estimate the user's emotions and adjust the timing of fictional article delivery based on those emotions. For example, if the user is relaxed, the generation unit will deliver the article slowly, giving the user time to enjoy it. If the user is excited, the generation unit will deliver the article quickly to maintain the user's excitement. Furthermore, if the user is stressed, the generation unit can temporarily withhold article delivery and wait until the user calms down. By adjusting the timing of article delivery according to the user's emotions, the system can improve user satisfaction.

[0097] The Fictional News system can further analyze a user's past subscription history and generate relevant fictional articles. For example, it can generate fictional articles related to categories of articles a user has previously subscribed to. It can also generate fictional articles related to topics a user has previously subscribed to. Furthermore, it can generate similar fictional articles by referencing the content of articles a user has previously subscribed to. This allows the system to provide more engaging fictional articles based on the user's past subscription history.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The reception desk receives input from the user. User input includes text input, voice input, and image input. For example, the reception desk can provide an interface for receiving text input, a microphone and voice recognition technology for receiving voice input, and a camera and image recognition technology for receiving image input. Step 2: The generation unit generates a fictional article based on the input received by the reception unit. The generation unit uses a generation AI, for example, a text generation AI (e.g., LLM), to generate the fictional article. The generation unit has a function to generate a fictional article based on keywords entered by the user and to check the quality of the generated fictional article. For example, the generation unit checks the grammatical accuracy and content consistency of the generated fictional article. Step 3: The advertising department places advertisements on the fictional articles generated by the generation department. The advertising department automatically selects and displays advertisements relevant to the generated fictional articles. The advertising department can also adjust how the advertisements are displayed in order to generate advertising revenue. For example, the advertising department can estimate the user's sentiment and adjust how the advertisements are displayed based on the estimated user sentiment.

[0100] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0101] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0102] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0103] Each of the multiple elements described above, including the reception unit, generation unit, and advertising unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and accepts text and voice input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates fictional articles using LLM. The advertising unit is implemented by the output device 40 of the smart device 14 and displays advertisements related to the generated fictional articles. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0110] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0111] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0112] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0113] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0114] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0116] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0118] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the reception unit, generation unit, and advertising unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives voice input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates fictional articles using LLM. The advertising unit is implemented by the speaker 240 of the smart glasses 214 and provides advertisements related to the generated fictional articles in voice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0124] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the reception unit, generation unit, and advertising unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives voice input from the user. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates fictional articles using LLM. The advertising unit is implemented by, for example, the display 343 of the headset terminal 314 and displays advertisements related to the generated fictional articles. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] As shown in Figure 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.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0140] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0144] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the reception unit, generation unit, and advertising unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives voice input from the user. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates fictional articles using LLM. The advertising unit is implemented by, for example, the speaker 240 of the robot 414 and provides advertisements related to the generated fictional articles in voice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0153] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0155] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0156] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0157] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0161] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0162] 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.

[0163] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0164] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0165] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0166] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0168] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0169] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0170] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0171] (Note 1) A reception area that receives input from users, A generation unit that generates a fictional article based on the input received by the reception unit, The system comprises an advertising unit that places advertisements in fictional articles generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Generating fictional articles using AI The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generative AI generates fictional articles based on user input. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned advertising department, Automatically selects and displays advertisements related to the generated fictional articles. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is It includes a function to check the quality of the generated fictional articles. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned advertising department, We place ads to earn advertising revenue. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past input history to select the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving input, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving input, the system prioritizes accepting inputs that are highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving input, the system analyzes the user's social media activity and accepts relevant input. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates user sentiment and adjusts the way fictional articles are presented based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating fictional articles, the level of detail is adjusted based on the user's past input. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating fictional articles, different generation algorithms are applied depending on the category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the fictional article based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating fictional articles, the generation priority is determined based on the timing of user input. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating fictional articles, we refer to relevant news data to improve the accuracy of the generation. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned advertising department, It estimates the user's emotions and adjusts how ads are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned advertising department, When placing an ad, the system selects the most suitable ad based on the content of the generated fictional article. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned advertising department, When displaying ads, the system analyzes the user's past click history to select the most suitable ads. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned advertising department, It estimates user sentiment and prioritizes ads based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned advertising department, When displaying ads, the system selects the most suitable ads based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned advertising department, When displaying ads, the system analyzes users' social media activity to display relevant ads. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that receives input from users, A generation unit that generates a fictional article based on the input received by the reception unit, The system comprises an advertising unit that places advertisements in fictional articles generated by the generation unit. A system characterized by the following features.

2. The generating unit is Generating fictional articles using AI. The system according to feature 1.

3. The generating unit is The AI ​​generates fictional articles based on user input. The system according to feature 1.

4. The aforementioned advertising department, Automatically selects and displays advertisements related to the generated fictional articles. The system according to feature 1.

5. The generating unit is It includes a function to check the quality of the generated fictional articles. The system according to feature 1.

6. The aforementioned advertising department, We place ads to earn advertising revenue. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past input history to select the optimal reception method. The system according to feature 1.

9. The aforementioned reception unit is When receiving input, filtering is performed based on the user's current areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of input to accept based on the estimated user emotions. The system according to feature 1.

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