System

Abstract

To provide a system that generates page layouts and contents based on user requests.SOLUTION: A system according to an embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives user requests. The analysis unit analyzes the requests received by the reception unit. The generation unit generates page layouts and contents based on the requests analyzed by the analysis unit. The provision unit provides the page layouts and contents generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technologies, the page layout and content of Web services are fixed, making it difficult to customize them to meet individual user needs.

[0005] The system according to the embodiment aims to generate page layout and content according to the user's request. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a request from a user. The analysis unit analyzes the request received by the reception unit. The generation unit generates a page layout and content based on the request analyzed by the analysis unit. The provision unit provides the page layout and content generated by the generation unit. Effect of the Invention

[0007] The system according to the embodiment can generate page layout and content according to the user's wishes. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A web page generation system according to an embodiment of the present invention is a system that accepts a user's request in natural language and generates a web page that matches the user's preferences at a high level based on the request. In this web page generation system, a user inputs a request in natural language, a generation AI analyzes the request, and generates a page layout and content that matches the user's preferences. This mechanism allows a user to easily create a web page that matches his or her preferences. For example, a user inputs a request such as "I want a simple and easy-to-read layout" or "I want the latest news to be displayed at the top." This request is input to the generation AI. Next, the generation AI analyzes the input request. The generation AI understands the request in natural language and generates a page layout and content that matches the user's preferences. For example, based on the request "I want a simple and easy-to-read layout," a layout with a simple design is generated, and based on the request "I want the latest news to be displayed at the top," content that displays the latest news at the top is generated. The generated page layout and content are customized to the user's preferences. For example, if a user requests "I want a lot of images," the generation AI generates a layout that uses a lot of images. Also, if a user requests "I want the text to be larger," the generation AI generates a layout with larger text. This mechanism allows users to easily create web pages that suit their preferences. For example, if a user requests a "simple, easy-to-read layout," the generation AI will generate a simple, easy-to-read layout, and if the user requests that the "latest news be displayed at the top," the generation AI will generate content that displays the latest news at the top. This allows users to easily create web pages that suit their preferences. This allows the web page generation system to generate highly customized web pages based on the user's requests.

[0029] The Web page generation system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a request from a user. The user's request may be in a text format, a voice format, an image format, or the like, but is not limited to such examples. The reception unit receives a request input by the user in a natural language, for example, as text data. The reception unit can also receive a user's request using a voice input. For example, the voice data is converted into text data using a voice recognition technology. The reception unit can also analyze image data to extract the user's request. For example, the text data is extracted from the image data using an image recognition technology. The analysis unit uses a generation AI to analyze the request received by the reception unit. The analysis is performed, for example, using a natural language processing technology, but is not limited to such examples. For example, the generation AI analyzes the request using a text generation AI (for example, LLM). The analysis unit can also analyze image data using an image analysis technology. The analysis unit can also analyze voice data using a voice analysis technology. For example, the generation AI converts voice data into text data and analyzes the text data. The generation unit uses the generation AI to generate a page layout and content based on the request analyzed by the analysis unit. The generation is performed, for example, based on the user's request, but is not limited to such an example. For example, the generation AI generates a layout with a simple design based on the user's request. The generation unit can also generate content that displays the latest news at the top based on the user's request. The generation unit can also generate a layout that uses many images based on the user's request. For example, the generation AI generates a layout that uses many images based on the user's request. The generation unit can also generate a layout with larger characters based on the user's request. For example, the generation AI generates a layout with larger characters based on the user's request. The provision unit provides the page layout and content generated by the generation unit. The provision is performed, for example, in real time, but is not limited to such an example. For example, the provision unit provides the generated page layout and content to the user in real time.The providing unit may also allow the user to confirm and modify the generated page layout and content. The providing unit may also store and manage the generated page layout and content. For example, the providing unit may store and manage the generated page layout and content in a database. In this way, the web page generation system according to the embodiment may generate a highly customized web page based on a user's request.

[0030] The reception unit receives a user's request. The user's request may be in a text format, a voice format, an image format, or the like, but is not limited to these examples. The reception unit receives, for example, a request input by the user in natural language as text data. Specifically, when the user inputs a request regarding the design or contents of a Web page, the user can freely write the request in a text box. The reception unit can also receive the user's request using voice input. For example, voice data is converted into text data using voice recognition technology. Voice recognition technology can analyze the contents of what the user speaks through a microphone in real time and convert it into accurate text data. Furthermore, the reception unit can analyze image data to extract the user's request. For example, image recognition technology is used to extract text data from image data. Image recognition technology automatically detects specific elements or text from images or screenshots uploaded by the user and recognizes this as a request. This allows the reception unit to efficiently receive user requests in various formats and smoothly hand them over to the next processing stage. Furthermore, the reception unit provides an environment in which the user can easily input requests through a user interface. For example, in the case of voice input, a simple operation is provided where the user can start voice input by simply clicking the microphone. In addition, in the case of image input, a function is provided where the image can be uploaded by drag and drop. This allows the user to intuitively input their requests, making the system easier to use.

[0031] The analysis unit uses the generation AI to analyze the request received by the reception unit. The analysis is performed, for example, using natural language processing technology, but is not limited to such an example. Specifically, the generation AI analyzes the request using a text generation AI (for example, LLM). The LLM has learned a large amount of text data and can understand the user's request and generate an appropriate analysis result. For example, if a user inputs "I want to create a web page with a simple design," the LLM analyzes the request "simple design" and extracts specific design elements. The analysis unit can also analyze image data using image analysis technology. Image analysis technology detects objects and text in an image and recognizes it as a request. For example, if an image uploaded by a user has "latest news" written on it, the image analysis technology detects this text and extracts the user's request that the latest news be displayed at the top. The analysis unit can also analyze voice data using voice analysis technology. For example, the generation AI converts voice data into text data and analyzes the text data. The analysis of voice data combines voice recognition technology and natural language processing technology, making it possible to accurately understand the user's voice request and provide appropriate analysis results. This allows the analysis unit to analyze user requests in a variety of formats with high accuracy and provide the information necessary for the next generation stage. Furthermore, the analysis unit can perform more accurate analysis by utilizing past data and user history. For example, by referring to what kind of web pages users with similar requests have generated in the past, it can provide more appropriate analysis results.

[0032] The generation unit uses the generation AI to generate a page layout and content based on the request analyzed by the analysis unit. The generation is performed, for example, based on the user's request, but is not limited to such an example. Specifically, the generation AI generates a layout with a simple design based on the user's request. For example, if the user requests "to create a web page with a simple design," the generation AI eliminates unnecessary decoration and generates an intuitive and easy-to-use layout. The generation unit can also generate content that displays the latest news at the top based on the user's request. For example, the latest news articles are obtained using a news API and placed on the top page. The generation unit can also generate a layout that uses a lot of images based on the user's request. For example, if the user requests "to create a design that uses a lot of images," the generation AI generates a layout that includes an image gallery and a slideshow. Furthermore, the generation unit can also generate a layout with larger characters based on the user's request. For example, if the user requests "to make the characters larger," the generation AI generates a layout with larger font size and improved visibility. This allows the generation unit to quickly generate customized web pages that meet the various requests of users. In addition, the generator can also display a preview of the generated layout and content for the user to review and modify, allowing the user to check the generated web page in real time and make modifications as necessary.

[0033] The providing unit provides the page layout and content generated by the generating unit. The provision is performed, for example, in real time, but is not limited to such an example. Specifically, the providing unit provides the generated page layout and content to the user in real time. For example, the generated web page is immediately published so that the user can confirm its contents. The providing unit can also enable the user to confirm and modify the generated page layout and content. For example, the user can preview the generated web page and modify text and images as necessary. The providing unit can also save and manage the generated page layout and content. For example, the providing unit saves and manages the generated page layout and content in a database. This allows the user to access the generated web page at any time and make modifications and updates as necessary. Furthermore, the providing unit can make the generated web page compatible with multiple devices and platforms. For example, the display on different devices such as PCs, smartphones, and tablets can be optimized so that the user can comfortably view it from any device. This allows the providing unit to provide high convenience and flexibility to the user and maximize the utility value of the generated web page.

[0034] The generating unit can generate a layout with a simple design based on the user's request. The simple design includes, for example, a limited number of colors, a simple layout, and the like, but is not limited to such examples. The generating unit generates, for example, a layout with a limited number of colors based on the user's request. The generating unit can also generate a simple layout. For example, the generating unit generates a simple design that eliminates unnecessary decoration based on the user's request. The generating unit can also generate a layout with high visibility based on the user's request. For example, the generating unit generates a layout in which the character size and line spacing are adjusted based on the user's request. This makes it possible to generate a layout with a simple design according to the user's request. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using a generating AI, or may be performed without using a generating AI. For example, the generating unit can input the user's request to the generating AI, and the generating AI can generate a layout with a simple design.

[0035] The generating unit can generate content that displays the latest news at the top based on the user's request. The latest news includes, for example, a news source, an update frequency, and the like, but is not limited to such examples. For example, the generating unit obtains the latest news from a specific news source based on the user's request, and generates content to display it at the top. The generating unit can also generate content that displays the latest news at the top based on the update frequency of the news. For example, the generating unit generates content that displays news updated daily at the top based on the user's request. The generating unit can also generate content that displays news of a specific category at the top based on the user's request. For example, the generating unit generates content that displays sports news at the top based on the user's request. This makes it possible to generate content that displays the latest news at the top according to the user's request. Part or all of the above-mentioned processing in the generating unit may be performed using, for example, a generating AI, or may be performed without using a generating AI. For example, the generating unit can input the user's request to the generating AI, and the generating AI can generate content that displays the latest news at the top.

[0036] The generating unit can generate a layout using multiple images based on the user's request. The layout using multiple images includes, for example, the number of images, an arrangement pattern, and the like, but is not limited to such examples. For example, the generating unit generates a layout using a specific number of images based on the user's request. The generating unit can also generate a layout using multiple images based on the arrangement pattern of the images. For example, the generating unit generates a layout in which images are arranged in a grid based on the user's request. The generating unit can also generate a layout in which images are displayed in a slideshow format based on the user's request. For example, the generating unit generates a layout in which images are displayed in a slideshow format in which images are displayed sequentially based on the user's request. This makes it possible to generate a layout using many images according to the user's request. Part or all of the above-mentioned processing in the generating unit may be performed using, for example, a generating AI, or may be performed without using a generating AI. For example, the generating unit can input the user's request to the generating AI, and the generating AI can generate a layout using multiple images.

[0037] The generating unit can generate a layout in which characters are enlarged based on the user's request. The layout in which characters are enlarged includes, for example, font size, line spacing, etc., but is not limited to such examples. The generating unit generates a layout using a specific font size based on the user's request, for example. The generating unit can also generate a layout in which characters are enlarged by adjusting line spacing. For example, the generating unit generates a layout in which line spacing is widened based on the user's request. The generating unit can also generate a layout using a specific font style based on the user's request. For example, the generating unit generates a layout using bold and italics based on the user's request. This makes it possible to generate a layout in which characters are enlarged according to the user's request. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using a generating AI, or may be performed without using a generating AI. For example, the generating unit can input the user's request to the generating AI, and the generating AI can generate a layout in which characters are enlarged.

[0038] The providing unit can provide the generated page layout and content to the user in real time. Real time includes, for example, a delay time, an update frequency, and the like, but is not limited to such examples. For example, the providing unit provides the generated page layout and content to the user without a delay time. The providing unit can also update the generated page layout and content at a high frequency. For example, the providing unit updates the page layout and content in real time based on the user's request. The providing unit can also update the page layout and content at a specific time interval based on the user's request. For example, the providing unit provides a page layout and content that are updated every minute based on the user's request. This makes it possible to provide the generated page layout and content in real time. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs the generated page layout and content to AI, and the AI ​​can provide them in real time.

[0039] The providing unit can enable the user to confirm and modify the generated page layout and content. The confirmation and modification includes, for example, an interface, a range that can be modified, and the like, but is not limited to such examples. The providing unit provides, for example, an interface for the user to confirm the generated page layout and content. The providing unit can also provide an interface for the user to modify the generated page layout and content. For example, the providing unit provides an interface that allows the user to modify text and images. The providing unit can also provide an interface that allows the user to change the layout. For example, the providing unit provides an interface that allows the user to change the layout by drag and drop. This allows the user to confirm and modify the generated page layout and content. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the generated page layout and content to AI, and the AI ​​can provide an interface for confirmation and modification.

[0040] The providing unit can store and manage the generated page layout and content. The storage and management includes, for example, the type of database, storage period, etc., but is not limited to such examples. The providing unit, for example, stores the generated page layout and content in a database. The providing unit can also manage the generated page layout and content based on a specific storage period. For example, the providing unit stores the generated page layout and content for a certain period based on a user's request. The providing unit can also manage the generated page layout and content by category. For example, the providing unit classifies and stores the generated page layout and content by category based on a user's request. This makes it possible to store and manage the generated page layout and content. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs the generated page layout and content to AI, and the AI ​​can store and manage them.

[0041] The reception unit can analyze the user's past request history and select an appropriate reception method. For example, the reception unit preferentially suggests a request reception method that the user has frequently used in the past. The reception unit can also predict the optimal reception timing from the user's past request history. Furthermore, the reception unit can provide an optimal reception interface based on the user's past request history. This makes it possible to select an optimal reception method based on the user's past request history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's past request history into an AI, which can select an optimal reception method.

[0042] The reception unit can filter requests based on the user's current project or field of interest when receiving the request. The reception unit, for example, preferentially receives requests related to a project currently in progress by the user. The reception unit can also filter related requests based on the user's field of interest. Furthermore, the reception unit can adjust the method of receiving requests according to the progress of the user's current project. This makes it possible to filter requests based on the user's current project or field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's project information into an AI, and the AI ​​can filter the requests.

[0043] The reception unit can preferentially receive requests with high relevance in consideration of the geographical location information of the user when receiving a request. For example, when the user is in a specific area, the reception unit preferentially receives requests related to the area. The reception unit can also filter related requests based on the user's current location. Furthermore, the reception unit can provide an optimal request reception method based on the geographical location information of the user. This makes it possible to preferentially receive requests with high relevance based on the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the geographical location information of the user to an AI, which can filter the requests.

[0044] The reception unit can analyze the user's social media activity and receive related requests when receiving a request. The reception unit preferentially receives related requests based on, for example, the user's social media activity. The reception unit can also analyze the user's social media posting content and provide an optimal request reception method. Furthermore, the reception unit can also receive related requests by referring to the activity content of the user's social media followers and friends. This makes it possible to receive related requests based on the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into AI, which can analyze and receive the request.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the request during the analysis. For example, the analysis unit performs a detailed analysis on an important request. The analysis unit can also perform a standard analysis on an ordinary request. Furthermore, the analysis unit can also perform a quick analysis that focuses on the main points on an urgent request. This makes it possible to adjust the level of detail of the analysis based on the importance of the request. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance data of the request to an AI, which can adjust the level of detail of the analysis.

[0046] The analysis unit can apply different analysis algorithms depending on the category of the request during analysis. For example, the analysis unit applies a specialized analysis algorithm to a technical request. Also, the analysis unit can apply a visual analysis algorithm to a request related to design. Furthermore, the analysis unit can apply a content-specific analysis algorithm to a request related to content. This makes it possible to apply an optimal analysis algorithm depending on the category of the request. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input request category data to AI, and the AI ​​can apply an optimal analysis algorithm.

[0047] The analysis unit can determine the priority of the analysis based on the time of submission of the request during the analysis. For example, the analysis unit prioritizes the analysis of the most recently submitted request. Also, the analysis unit can postpone requests submitted earlier. Furthermore, the analysis unit can adjust the analysis schedule based on the submission time. This makes it possible to determine the priority of the analysis based on the time of submission of the request. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input data on the time of submission of the request to an AI, which can determine the priority of the analysis.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the requests during analysis. For example, the analysis unit prioritizes analysis of requests with high relevance. Also, the analysis unit can postpone requests with low relevance. Furthermore, the analysis unit can adjust the schedule of analysis based on the relevance of the requests. This makes it possible to adjust the order of analysis based on the relevance of the requests. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input relevance data of requests to an AI, which can adjust the order of analysis.

[0049] The generation unit can improve the accuracy of generation by considering the interrelationships of requests at the time of generation. For example, when multiple requests are related, the generation unit integrates them to generate an optimal layout. The generation unit can also analyze the interrelationships of requests and generate a layout without contradictions. Furthermore, the generation unit can also perform optimal content arrangement by considering the interrelationships of requests. As a result, the accuracy of generation is improved by considering the interrelationships of requests. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs interrelationship data of requests to the generation AI, which can perform optimal layout and content arrangement.

[0050] The generation unit can generate the request while taking into consideration the attribute information of the person who submitted the request. The generation unit generates an appropriate layout, for example, according to the age of the person who submitted the request. The generation unit can also preferentially display related content according to the occupation of the person who submitted the request. Furthermore, the generation unit can generate optimal layouts and content based on the interests of the person who submitted the request. This allows optimal generation based on the attribute information of the person who submitted the request. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the attribute information of the person who submitted the request to the generation AI, which can generate optimal layouts and content.

[0051] The generation unit can perform generation in consideration of the geographic distribution of requests. For example, when a user is in a specific area, the generation unit preferentially displays content related to the area. The generation unit can also analyze the geographic distribution of requests and generate an optimal layout. Furthermore, the generation unit can also filter related content based on the geographic distribution. This allows optimal generation based on the geographic distribution of requests. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input geographic distribution data to the generation AI, which can generate an optimal layout and content.

[0052] The generation unit can improve the accuracy of generation by referring to literature related to the request at the time of generation. The generation unit can, for example, refer to literature related to the request and generate an optimal layout. The generation unit can also improve the accuracy of the content based on the related literature. Furthermore, the generation unit can filter the related literature based on the content of the request and perform optimal generation. As a result, the accuracy of generation is improved by referring to the literature related to the request. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input related literature data to the generation AI, which can generate an optimal layout and content.

[0053] The providing unit can select the optimal display method by referring to the user's past operation history at the time of providing. For example, the providing unit preferentially provides a display method used by the user in the past. In addition, the providing unit can predict the optimal display method from the user's past operation history. Furthermore, the providing unit can also provide an optimal display interface based on the user's past operation history. This makes it possible to select the optimal display method based on the user's past operation history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's operation history data to AI, and the AI ​​can select the optimal display method.

[0054] The providing unit can optimize the storage method of the generated page layout and content at the time of providing. The providing unit, for example, stores the generated page layout and content in cloud storage. The providing unit can also store the generated page layout and content in local storage. Furthermore, the providing unit can also store the generated page layout and content in distributed storage. This can optimize the storage method of the generated page layout and content. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input storage method data to AI, and the AI ​​can select the optimal storage method.

[0055] The providing unit can select the optimal display method in consideration of the user's device information at the time of providing. For example, when the user is using a smartphone, the providing unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, when the user is using a smart watch, the providing unit can also provide a simple and highly visible display method. This makes it possible to select the optimal display method based on the user's device information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's device information into AI, and the AI ​​can select the optimal display method.

[0056] The providing unit can optimize the management method of the generated page layout and content at the time of providing. The providing unit provides, for example, a system for centrally managing the generated page layout and content. The providing unit can also provide a system for managing the generated page layout and content by category. Furthermore, the providing unit can also provide a system for tagging and managing the generated page layout and content. This makes it possible to optimize the management method of the generated page layout and content. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input management method data to AI, and the AI ​​can select the optimal management method.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The analysis unit can analyze the user's past behavioral patterns and improve the accuracy of analyzing requests. For example, it analyzes what requests the user has made in the past and performs more accurate analysis of similar requests. The analysis unit can also automatically adjust the priority of requests based on the user's behavioral patterns. Furthermore, the analysis unit can learn the user's behavioral patterns and predict future requests. This makes it possible to improve the accuracy of analysis based on the user's past behavioral patterns.

[0059] The providing unit can select an optimal display method in consideration of the remaining battery level of the user's device. For example, when the remaining battery level is low, a lightweight display method can be provided. Also, when the remaining battery level is sufficient, a rich display method can be provided. Furthermore, the amount of content to be displayed can be adjusted according to the remaining battery level. In this way, it is possible to select an optimal display method based on the remaining battery level of the user's device.

[0060] When analyzing a request, the analysis unit can adjust the speed of the analysis taking into account the user's current network conditions. For example, if the network is slow, a simplified analysis can be performed. On the other hand, if the network is fast, a detailed analysis can be performed. Furthermore, the method of providing the analysis results can be adjusted according to the network conditions. In this way, the analysis speed can be adjusted based on the user's network conditions.

[0061] The providing unit can analyze the user's past feedback and select the optimal providing method. For example, the providing method that the user has preferred in the past is provided preferentially. Also, the optimal providing timing can be predicted from the user's past feedback. Furthermore, the type of content to be provided can be adjusted based on the user's past feedback. In this way, the optimal providing method can be selected based on the user's past feedback.

[0062] When analyzing a request, the analysis unit can adjust the analysis load taking into account the processing power of the user's device. For example, for a device with low processing power, a light analysis can be performed. For a device with high processing power, a detailed analysis can be performed. Furthermore, the method of providing the analysis results can be adjusted according to the processing power of the device. This makes it possible to adjust the analysis load based on the processing power of the user's device.

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The reception unit receives a user's request. The user's request may be in a text format, a voice format, an image format, or the like. For example, a request input by the user in natural language is received as text data. In addition, voice data can be converted into text data using voice recognition technology, and text data can be extracted from image data using image recognition technology. Step 2: The analysis unit uses the generation AI to analyze the request received by the reception unit. The analysis is performed using natural language processing technology, image analysis technology, and voice analysis technology. For example, the generation AI analyzes the request using a text generation AI (e.g., LLM), converts the voice data into text data, and analyzes it. Step 3: The generation unit uses the generation AI to generate page layouts and content based on the requests analyzed by the analysis unit. Generation is based on user requests, and can include layouts with simple designs, content that displays the latest news at the top, layouts that use a lot of images, layouts with larger text, etc. Step 4: The provider provides the page layout and content generated by the generator. The provider provides the page layout and content in real time, allowing the user to check and modify it, or store and manage the generated page layout and content in a database.

[0065] (Example 2) A web page generation system according to an embodiment of the present invention is a system that accepts a user's request in natural language and generates a web page that matches the user's preferences at a high level based on the request. In this web page generation system, a user inputs a request in natural language, a generation AI analyzes the request, and generates a page layout and content that matches the user's preferences. This mechanism allows a user to easily create a web page that matches his or her preferences. For example, a user inputs a request such as "I want a simple and easy-to-read layout" or "I want the latest news to be displayed at the top." This request is input to the generation AI. Next, the generation AI analyzes the input request. The generation AI understands the request in natural language and generates a page layout and content that matches the user's preferences. For example, based on the request "I want a simple and easy-to-read layout," a layout with a simple design is generated, and based on the request "I want the latest news to be displayed at the top," content that displays the latest news at the top is generated. The generated page layout and content are customized to the user's preferences. For example, if a user requests "I want a lot of images," the generation AI generates a layout that uses a lot of images. Also, if a user requests "I want the text to be larger," the generation AI generates a layout with larger text. This mechanism allows users to easily create web pages that suit their preferences. For example, if a user requests a "simple, easy-to-read layout," the generation AI will generate a simple, easy-to-read layout, and if the user requests that the "latest news be displayed at the top," the generation AI will generate content that displays the latest news at the top. This allows users to easily create web pages that suit their preferences. This allows the web page generation system to generate highly customized web pages based on the user's requests.

[0066] The Web page generation system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a request from a user. The user's request may be in a text format, a voice format, an image format, or the like, but is not limited to such examples. The reception unit receives a request input by the user in a natural language, for example, as text data. The reception unit can also receive a user's request using a voice input. For example, the voice data is converted into text data using a voice recognition technology. The reception unit can also analyze image data to extract the user's request. For example, the text data is extracted from the image data using an image recognition technology. The analysis unit uses a generation AI to analyze the request received by the reception unit. The analysis is performed, for example, using a natural language processing technology, but is not limited to such examples. For example, the generation AI analyzes the request using a text generation AI (for example, LLM). The analysis unit can also analyze image data using an image analysis technology. The analysis unit can also analyze voice data using a voice analysis technology. For example, the generation AI converts voice data into text data and analyzes the text data. The generation unit uses the generation AI to generate a page layout and content based on the request analyzed by the analysis unit. The generation is performed, for example, based on the user's request, but is not limited to such an example. For example, the generation AI generates a layout with a simple design based on the user's request. The generation unit can also generate content that displays the latest news at the top based on the user's request. The generation unit can also generate a layout that uses many images based on the user's request. For example, the generation AI generates a layout that uses many images based on the user's request. The generation unit can also generate a layout with larger characters based on the user's request. For example, the generation AI generates a layout with larger characters based on the user's request. The provision unit provides the page layout and content generated by the generation unit. The provision is performed, for example, in real time, but is not limited to such an example. For example, the provision unit provides the generated page layout and content to the user in real time.The providing unit may also allow the user to confirm and modify the generated page layout and content. The providing unit may also store and manage the generated page layout and content. For example, the providing unit may store and manage the generated page layout and content in a database. In this way, the web page generation system according to the embodiment may generate a highly customized web page based on a user's request.

[0067] The reception unit receives a user's request. The user's request may be in a text format, a voice format, an image format, or the like, but is not limited to these examples. The reception unit receives, for example, a request input by the user in natural language as text data. Specifically, when the user inputs a request regarding the design or contents of a Web page, the user can freely write the request in a text box. The reception unit can also receive the user's request using voice input. For example, voice data is converted into text data using voice recognition technology. Voice recognition technology can analyze the contents of what the user speaks through a microphone in real time and convert it into accurate text data. Furthermore, the reception unit can analyze image data to extract the user's request. For example, image recognition technology is used to extract text data from image data. Image recognition technology automatically detects specific elements or text from images or screenshots uploaded by the user and recognizes this as a request. This allows the reception unit to efficiently receive user requests in various formats and smoothly hand them over to the next processing stage. Furthermore, the reception unit provides an environment in which the user can easily input requests through a user interface. For example, in the case of voice input, a simple operation is provided where the user can start voice input by simply clicking the microphone. In addition, in the case of image input, a function is provided where the image can be uploaded by drag and drop. This allows the user to intuitively input their requests, making the system easier to use.

[0068] The analysis unit uses the generation AI to analyze the request received by the reception unit. The analysis is performed, for example, using natural language processing technology, but is not limited to such an example. Specifically, the generation AI analyzes the request using a text generation AI (for example, LLM). The LLM has learned a large amount of text data and can understand the user's request and generate an appropriate analysis result. For example, if a user inputs "I want to create a web page with a simple design," the LLM analyzes the request "simple design" and extracts specific design elements. The analysis unit can also analyze image data using image analysis technology. Image analysis technology detects objects and text in an image and recognizes it as a request. For example, if an image uploaded by a user has "latest news" written on it, the image analysis technology detects this text and extracts the user's request that the latest news be displayed at the top. The analysis unit can also analyze voice data using voice analysis technology. For example, the generation AI converts voice data into text data and analyzes the text data. The analysis of voice data combines voice recognition technology and natural language processing technology, making it possible to accurately understand the user's voice request and provide appropriate analysis results. This allows the analysis unit to analyze user requests in a variety of formats with high accuracy and provide the information necessary for the next generation stage. Furthermore, the analysis unit can perform more accurate analysis by utilizing past data and user history. For example, by referring to what kind of web pages users with similar requests have generated in the past, it can provide more appropriate analysis results.

[0069] The generation unit uses the generation AI to generate a page layout and content based on the request analyzed by the analysis unit. The generation is performed, for example, based on the user's request, but is not limited to such an example. Specifically, the generation AI generates a layout with a simple design based on the user's request. For example, if the user requests "to create a web page with a simple design," the generation AI eliminates unnecessary decoration and generates an intuitive and easy-to-use layout. The generation unit can also generate content that displays the latest news at the top based on the user's request. For example, the latest news articles are obtained using a news API and placed on the top page. The generation unit can also generate a layout that uses a lot of images based on the user's request. For example, if the user requests "to create a design that uses a lot of images," the generation AI generates a layout that includes an image gallery and a slideshow. Furthermore, the generation unit can also generate a layout with larger characters based on the user's request. For example, if the user requests "to make the characters larger," the generation AI generates a layout with larger font size and improved visibility. This allows the generation unit to quickly generate customized web pages that meet the various requests of users. In addition, the generator can also display a preview of the generated layout and content for the user to review and modify, allowing the user to check the generated web page in real time and make modifications as necessary.

[0070] The providing unit provides the page layout and content generated by the generating unit. The provision is performed, for example, in real time, but is not limited to such an example. Specifically, the providing unit provides the generated page layout and content to the user in real time. For example, the generated web page is immediately published so that the user can confirm its contents. The providing unit can also enable the user to confirm and modify the generated page layout and content. For example, the user can preview the generated web page and modify text and images as necessary. The providing unit can also save and manage the generated page layout and content. For example, the providing unit saves and manages the generated page layout and content in a database. This allows the user to access the generated web page at any time and make modifications and updates as necessary. Furthermore, the providing unit can make the generated web page compatible with multiple devices and platforms. For example, the display on different devices such as PCs, smartphones, and tablets can be optimized so that the user can comfortably view it from any device. This allows the providing unit to provide high convenience and flexibility to the user and maximize the utility value of the generated web page.

[0071] The generating unit can generate a layout with a simple design based on the user's request. The simple design includes, for example, a limited number of colors, a simple layout, and the like, but is not limited to such examples. The generating unit generates, for example, a layout with a limited number of colors based on the user's request. The generating unit can also generate a simple layout. For example, the generating unit generates a simple design that eliminates unnecessary decoration based on the user's request. The generating unit can also generate a layout with high visibility based on the user's request. For example, the generating unit generates a layout in which the character size and line spacing are adjusted based on the user's request. This makes it possible to generate a layout with a simple design according to the user's request. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using a generating AI, or may be performed without using a generating AI. For example, the generating unit can input the user's request to the generating AI, and the generating AI can generate a layout with a simple design.

[0072] The generating unit can generate content that displays the latest news at the top based on the user's request. The latest news includes, for example, a news source, an update frequency, and the like, but is not limited to such examples. For example, the generating unit obtains the latest news from a specific news source based on the user's request, and generates content to display it at the top. The generating unit can also generate content that displays the latest news at the top based on the update frequency of the news. For example, the generating unit generates content that displays news updated daily at the top based on the user's request. The generating unit can also generate content that displays news of a specific category at the top based on the user's request. For example, the generating unit generates content that displays sports news at the top based on the user's request. This makes it possible to generate content that displays the latest news at the top according to the user's request. Part or all of the above-mentioned processing in the generating unit may be performed using, for example, a generating AI, or may be performed without using a generating AI. For example, the generating unit can input the user's request to the generating AI, and the generating AI can generate content that displays the latest news at the top.

[0073] The generating unit can generate a layout using multiple images based on the user's request. The layout using multiple images includes, for example, the number of images, an arrangement pattern, and the like, but is not limited to such examples. For example, the generating unit generates a layout using a specific number of images based on the user's request. The generating unit can also generate a layout using multiple images based on the arrangement pattern of the images. For example, the generating unit generates a layout in which images are arranged in a grid based on the user's request. The generating unit can also generate a layout in which images are displayed in a slideshow format based on the user's request. For example, the generating unit generates a layout in which images are displayed in a slideshow format in which images are displayed sequentially based on the user's request. This makes it possible to generate a layout using many images according to the user's request. Part or all of the above-mentioned processing in the generating unit may be performed using, for example, a generating AI, or may be performed without using a generating AI. For example, the generating unit can input the user's request to the generating AI, and the generating AI can generate a layout using multiple images.

[0074] The generating unit can generate a layout in which characters are enlarged based on the user's request. The layout in which characters are enlarged includes, for example, font size, line spacing, etc., but is not limited to such examples. The generating unit generates a layout using a specific font size based on the user's request, for example. The generating unit can also generate a layout in which characters are enlarged by adjusting line spacing. For example, the generating unit generates a layout in which line spacing is widened based on the user's request. The generating unit can also generate a layout using a specific font style based on the user's request. For example, the generating unit generates a layout using bold and italics based on the user's request. This makes it possible to generate a layout in which characters are enlarged according to the user's request. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using a generating AI, or may be performed without using a generating AI. For example, the generating unit can input the user's request to the generating AI, and the generating AI can generate a layout in which characters are enlarged.

[0075] The providing unit can provide the generated page layout and content to the user in real time. Real time includes, for example, a delay time, an update frequency, and the like, but is not limited to such examples. For example, the providing unit provides the generated page layout and content to the user without a delay time. The providing unit can also update the generated page layout and content at a high frequency. For example, the providing unit updates the page layout and content in real time based on the user's request. The providing unit can also update the page layout and content at a specific time interval based on the user's request. For example, the providing unit provides a page layout and content that are updated every minute based on the user's request. This makes it possible to provide the generated page layout and content in real time. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs the generated page layout and content to AI, and the AI ​​can provide them in real time.

[0076] The providing unit can enable the user to confirm and modify the generated page layout and content. The confirmation and modification includes, for example, an interface, a range that can be modified, and the like, but is not limited to such examples. The providing unit provides, for example, an interface for the user to confirm the generated page layout and content. The providing unit can also provide an interface for the user to modify the generated page layout and content. For example, the providing unit provides an interface that allows the user to modify text and images. The providing unit can also provide an interface that allows the user to change the layout. For example, the providing unit provides an interface that allows the user to change the layout by drag and drop. This allows the user to confirm and modify the generated page layout and content. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the generated page layout and content to AI, and the AI ​​can provide an interface for confirmation and modification.

[0077] The providing unit can store and manage the generated page layout and content. The storage and management includes, for example, the type of database, storage period, etc., but is not limited to such examples. The providing unit, for example, stores the generated page layout and content in a database. The providing unit can also manage the generated page layout and content based on a specific storage period. For example, the providing unit stores the generated page layout and content for a certain period based on a user's request. The providing unit can also manage the generated page layout and content by category. For example, the providing unit classifies and stores the generated page layout and content by category based on a user's request. This makes it possible to store and manage the generated page layout and content. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs the generated page layout and content to AI, and the AI ​​can store and manage them.

[0078] The reception unit can estimate the user's emotion and adjust the timing of receiving the request based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit delays the timing of receiving the request to relax the user. Also, when the user is relaxed, the reception unit can immediately receive the request. Furthermore, when the user is in a hurry, the reception unit can quickly receive the request. This makes it possible to adjust the timing of receiving the request according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to an AI, which estimates the emotion and adjusts the timing of receiving the request.

[0079] The reception unit can analyze the user's past request history and select an appropriate reception method. For example, the reception unit preferentially suggests a request reception method that the user has frequently used in the past. The reception unit can also predict the optimal reception timing from the user's past request history. Furthermore, the reception unit can provide an optimal reception interface based on the user's past request history. This makes it possible to select an optimal reception method based on the user's past request history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's past request history into an AI, which can select an optimal reception method.

[0080] The reception unit can filter requests based on the user's current project or field of interest when receiving the request. The reception unit, for example, preferentially receives requests related to a project currently in progress by the user. The reception unit can also filter related requests based on the user's field of interest. Furthermore, the reception unit can adjust the method of receiving requests according to the progress of the user's current project. This makes it possible to filter requests based on the user's current project or field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's project information into an AI, and the AI ​​can filter the requests.

[0081] The reception unit can estimate the user's emotion and determine the priority of the requests to be received based on the estimated user's emotion. For example, when the user is stressed, the reception unit preferentially receives important requests. Also, when the user is relaxed, the reception unit can receive requests with normal priority. Furthermore, when the user is in a hurry, the reception unit can also receive urgent requests with the highest priority. This makes it possible to determine the priority of requests according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to an AI, which can estimate the emotion and determine the priority of requests.

[0082] The reception unit can preferentially receive requests with high relevance in consideration of the geographical location information of the user when receiving a request. For example, when the user is in a specific area, the reception unit preferentially receives requests related to the area. The reception unit can also filter related requests based on the user's current location. Furthermore, the reception unit can provide an optimal request reception method based on the geographical location information of the user. This makes it possible to preferentially receive requests with high relevance based on the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the geographical location information of the user to an AI, which can filter the requests.

[0083] The reception unit can analyze the user's social media activity and receive related requests when receiving a request. The reception unit preferentially receives related requests based on, for example, the user's social media activity. The reception unit can also analyze the user's social media posting content and provide an optimal request reception method. Furthermore, the reception unit can also receive related requests by referring to the activity content of the user's social media followers and friends. This makes it possible to receive related requests based on the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into AI, which can analyze and receive the request.

[0084] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible analysis result. In addition, when the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, when the user is in a hurry, the analysis unit can also provide an analysis result that covers the main points. This makes it possible to adjust the expression method of the analysis according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to an AI, which estimates the emotion and adjusts the expression method of the analysis.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the request during the analysis. For example, the analysis unit performs a detailed analysis on an important request. The analysis unit can also perform a standard analysis on an ordinary request. Furthermore, the analysis unit can also perform a quick analysis that focuses on the main points on an urgent request. This makes it possible to adjust the level of detail of the analysis based on the importance of the request. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance data of the request to an AI, which can adjust the level of detail of the analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the category of the request during analysis. For example, the analysis unit applies a specialized analysis algorithm to a technical request. Also, the analysis unit can apply a visual analysis algorithm to a request related to design. Furthermore, the analysis unit can apply a content-specific analysis algorithm to a request related to content. This makes it possible to apply an optimal analysis algorithm depending on the category of the request. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input request category data to AI, and the AI ​​can apply an optimal analysis algorithm.

[0087] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is in a hurry, the analysis unit provides a short and to the point analysis result. In addition, when the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, when the user is excited, the analysis unit can also provide a visually stimulating analysis result. This allows the length of the analysis to be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to an AI, which estimates the emotion and adjusts the length of the analysis.

[0088] The analysis unit can determine the priority of the analysis based on the time of submission of the request during the analysis. For example, the analysis unit prioritizes the analysis of the most recently submitted request. Also, the analysis unit can postpone requests submitted earlier. Furthermore, the analysis unit can adjust the analysis schedule based on the submission time. This makes it possible to determine the priority of the analysis based on the time of submission of the request. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input data on the time of submission of the request to an AI, which can determine the priority of the analysis.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the requests during analysis. For example, the analysis unit prioritizes analysis of requests with high relevance. Also, the analysis unit can postpone requests with low relevance. Furthermore, the analysis unit can adjust the schedule of analysis based on the relevance of the requests. This makes it possible to adjust the order of analysis based on the relevance of the requests. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input relevance data of requests to an AI, which can adjust the order of analysis.

[0090] The generation unit can estimate the user's emotion and determine the priority of the page layout and contents to be generated based on the estimated user's emotion. For example, when the user is relaxed, the generation unit prioritizes a visually attractive layout. Also, when the user is in a hurry, the generation unit can prioritize important content. Furthermore, when the user is excited, the generation unit can also prioritize a visually stimulating layout. This makes it possible to determine the priority of the page layout and contents to be generated according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into an AI, which estimates the emotion and determines the priority of the page layout and contents.

[0091] The generation unit can improve the accuracy of generation by considering the interrelationships of requests at the time of generation. For example, when multiple requests are related, the generation unit integrates them to generate an optimal layout. The generation unit can also analyze the interrelationships of requests and generate a layout without contradictions. Furthermore, the generation unit can also perform optimal content arrangement by considering the interrelationships of requests. As a result, the accuracy of generation is improved by considering the interrelationships of requests. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs interrelationship data of requests to the generation AI, which can perform optimal layout and content arrangement.

[0092] The generation unit can generate the request while taking into consideration the attribute information of the person who submitted the request. The generation unit generates an appropriate layout, for example, according to the age of the person who submitted the request. The generation unit can also preferentially display related content according to the occupation of the person who submitted the request. Furthermore, the generation unit can generate optimal layouts and content based on the interests of the person who submitted the request. This allows optimal generation based on the attribute information of the person who submitted the request. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the attribute information of the person who submitted the request to the generation AI, which can generate optimal layouts and content.

[0093] The generation unit can estimate the user's emotion and adjust the page layout and the display method of the content to be generated based on the estimated user's emotion. For example, when the user is nervous, the generation unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the generation unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the generation unit can also provide a display method that focuses on the main points. This makes it possible to adjust the page layout and the display method of the content to be generated according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to an AI, which estimates the emotion and adjusts the display method.

[0094] The generation unit can perform generation in consideration of the geographic distribution of requests. For example, when a user is in a specific area, the generation unit preferentially displays content related to the area. The generation unit can also analyze the geographic distribution of requests and generate an optimal layout. Furthermore, the generation unit can also filter related content based on the geographic distribution. This allows optimal generation based on the geographic distribution of requests. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input geographic distribution data to the generation AI, which can generate an optimal layout and content.

[0095] The generation unit can improve the accuracy of generation by referring to literature related to the request at the time of generation. The generation unit can, for example, refer to literature related to the request and generate an optimal layout. The generation unit can also improve the accuracy of the content based on the related literature. Furthermore, the generation unit can filter the related literature based on the content of the request and perform optimal generation. As a result, the accuracy of generation is improved by referring to the literature related to the request. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input related literature data to the generation AI, which can generate an optimal layout and content.

[0096] The providing unit can estimate the user's emotion and adjust the page layout and the display method of the content to be provided based on the estimated user's emotion. For example, when the user is nervous, the providing unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the providing unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. This makes it possible to adjust the page layout and the display method of the content to be provided according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to an AI, which can estimate the emotion and adjust the display method.

[0097] The providing unit can select the optimal display method by referring to the user's past operation history at the time of providing. For example, the providing unit preferentially provides a display method used by the user in the past. In addition, the providing unit can predict the optimal display method from the user's past operation history. Furthermore, the providing unit can also provide an optimal display interface based on the user's past operation history. This makes it possible to select the optimal display method based on the user's past operation history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's operation history data to AI, and the AI ​​can select the optimal display method.

[0098] The providing unit can optimize the storage method of the generated page layout and content at the time of providing. The providing unit, for example, stores the generated page layout and content in cloud storage. The providing unit can also store the generated page layout and content in local storage. Furthermore, the providing unit can also store the generated page layout and content in distributed storage. This can optimize the storage method of the generated page layout and content. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input storage method data to AI, and the AI ​​can select the optimal storage method.

[0099] The providing unit can estimate the user's emotion and adjust the operation procedure of the page layout and the content to be provided based on the estimated user's emotion. For example, when the user is nervous, the providing unit can provide a simple and intuitive operation procedure. Furthermore, when the user is relaxed, the providing unit can provide a detailed operation procedure. Furthermore, when the user is in a hurry, the providing unit can also provide a procedure that allows the user to operate quickly. This makes it possible to adjust the operation procedure of the page layout and the content to be provided according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to an AI, which can estimate the emotion and adjust the operation procedure.

[0100] The providing unit can select the optimal display method in consideration of the user's device information at the time of providing. For example, when the user is using a smartphone, the providing unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, when the user is using a smart watch, the providing unit can also provide a simple and highly visible display method. This makes it possible to select the optimal display method based on the user's device information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's device information into AI, and the AI ​​can select the optimal display method.

[0101] The providing unit can optimize the management method of the generated page layout and content at the time of providing. The providing unit provides, for example, a system for centrally managing the generated page layout and content. The providing unit can also provide a system for managing the generated page layout and content by category. Furthermore, the providing unit can also provide a system for tagging and managing the generated page layout and content. This makes it possible to optimize the management method of the generated page layout and content. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input management method data to AI, and the AI ​​can select the optimal management method.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The analysis unit can analyze the user's past behavioral patterns and improve the accuracy of analyzing requests. For example, it analyzes what requests the user has made in the past and performs more accurate analysis of similar requests. The analysis unit can also automatically adjust the priority of requests based on the user's behavioral patterns. Furthermore, the analysis unit can learn the user's behavioral patterns and predict future requests. This makes it possible to improve the accuracy of analysis based on the user's past behavioral patterns.

[0104] The generation unit can estimate the user's emotion and adjust the color tones of the generated page layout based on the estimated user's emotion. For example, if the user is relaxed, a layout with calm color tones can be generated. If the user is excited, a layout with vivid color tones can be generated. Furthermore, if the user is stressed, a layout with calm color tones can be generated. In this way, the color tones of the page layout can be adjusted according to the user's emotion.

[0105] The providing unit can select an optimal display method in consideration of the remaining battery level of the user's device. For example, when the remaining battery level is low, a lightweight display method can be provided. Also, when the remaining battery level is sufficient, a rich display method can be provided. Furthermore, the amount of content to be displayed can be adjusted according to the remaining battery level. In this way, it is possible to select an optimal display method based on the remaining battery level of the user's device.

[0106] The reception unit can estimate the user's emotion and adjust the method of receiving a request based on the estimated user's emotion. For example, if the user is nervous, a simple interface can be provided. If the user is relaxed, a detailed interface can be provided. Furthermore, if the user is in a hurry, an interface that quickly receives a request can be provided. In this way, the method of receiving a request can be adjusted according to the user's emotion.

[0107] When analyzing a request, the analysis unit can adjust the speed of the analysis taking into account the user's current network conditions. For example, if the network is slow, a simplified analysis can be performed. On the other hand, if the network is fast, a detailed analysis can be performed. Furthermore, the method of providing the analysis results can be adjusted according to the network conditions. In this way, the analysis speed can be adjusted based on the user's network conditions.

[0108] The generation unit can estimate the user's emotion and adjust the type of content to be generated based on the estimated user's emotion. For example, when the user is relaxed, entertainment content is preferentially generated. When the user is stressed, relaxation content can be generated. Furthermore, when the user is excited, active content can also be generated. This makes it possible to adjust the type of content to be generated according to the user's emotion.

[0109] The providing unit can analyze the user's past feedback and select the optimal providing method. For example, the providing method that the user has preferred in the past is provided preferentially. Also, the optimal providing timing can be predicted from the user's past feedback. Furthermore, the type of content to be provided can be adjusted based on the user's past feedback. In this way, the optimal providing method can be selected based on the user's past feedback.

[0110] The reception unit can estimate the user's emotions and filter the received request contents based on the estimated user's emotions. For example, when the user is stressed, only important requests are received. When the user is relaxed, all requests can be received. Furthermore, when the user is in a hurry, urgent requests can be given priority. In this way, the received request contents can be filtered according to the user's emotions.

[0111] When analyzing a request, the analysis unit can adjust the analysis load taking into account the processing power of the user's device. For example, for a device with low processing power, a light analysis can be performed. For a device with high processing power, a detailed analysis can be performed. Furthermore, the method of providing the analysis results can be adjusted according to the processing power of the device. This makes it possible to adjust the analysis load based on the processing power of the user's device.

[0112] The generation unit can estimate the user's emotion and adjust the interactivity of the generated page layout based on the estimated user's emotion. For example, if the user is relaxed, a layout including many interactive elements can be generated. If the user is nervous, a simple layout with less interactivity can be generated. Furthermore, if the user is in a hurry, an interactive layout that can be operated quickly can be generated. In this way, the interactivity of the page layout can be adjusted according to the user's emotion.

[0113] The process flow of the second embodiment will be briefly described below.

[0114] Step 1: The reception unit receives a user's request. The user's request may be in a text format, a voice format, an image format, or the like. For example, a request input by the user in natural language is received as text data. In addition, voice data can be converted into text data using voice recognition technology, and text data can be extracted from image data using image recognition technology. Step 2: The analysis unit uses the generation AI to analyze the request received by the reception unit. The analysis is performed using natural language processing technology, image analysis technology, and voice analysis technology. For example, the generation AI analyzes the request using a text generation AI (e.g., LLM), converts the voice data into text data, and analyzes it. Step 3: The generation unit uses the generation AI to generate page layouts and content based on the requests analyzed by the analysis unit. Generation is based on user requests, and can include layouts with simple designs, content that displays the latest news at the top, layouts that use a lot of images, layouts with larger text, etc. Step 4: The provider provides the page layout and content generated by the generator. The provider provides the page layout and content in real time, allowing the user to check and modify it, or store and manage the generated page layout and content in a database.

[0115] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0117] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and receives the user's request as text data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the request using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a page layout and content based on the analyzed request. The provision unit is realized, for example, by the output device 40 of the smart device 14, and provides the generated page layout and content to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0126] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0129] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0131] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives the user's request as voice data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the request using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a page layout and content based on the analyzed request. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the generated page layout and content to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0137] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0142] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0145] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and receives the user's request as voice data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the request using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a page layout and content based on the analyzed request. The provision unit is realized, for example, by the display 343 of the headset type terminal 314 and provides the generated page layout and content to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0158] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0159] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0160] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0162] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0163] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0167] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives the user's request as voice data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the request using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a page layout and content based on the analyzed request. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the generated page layout and content to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0168] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0169] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0170] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0171] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0172] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0173] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0176] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0177] In addition, 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 upon request from the data processing device 12.

[0178] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0179] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0180] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0181] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0182] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0183] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0184] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0185] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0186] (Appendix 1) A reception unit that receives a request from a user; an analysis unit that analyzes the request received by the reception unit; a generation unit that generates a page layout and contents based on the requirements analyzed by the analysis unit; a providing unit that provides the page layout and content generated by the generating unit. A system characterized by: (Appendix 2) The generation unit is Generate simple layout designs based on user requests 2. The system of claim 1, (Appendix 3) The generation unit is Generate content that displays the latest news at the top based on user requests 2. The system of claim 1, (Appendix 4) The generation unit is Generate layouts using multiple images based on user requests 2. The system of claim 1, (Appendix 5) The generation unit is Generate a layout with enlarged text based on user requests 2. The system of claim 1, (Appendix 6) The providing unit is Serve generated page layouts and content to users in real time 2. The system of claim 1, (Appendix 7) The providing unit is Allow users to review and modify the generated page layout and content 2. The system of claim 1, (Appendix 8) The providing unit is Store and manage generated page layouts and content 2. The system of claim 1, (Appendix 9) The reception unit is To estimate a user's emotions and adjust the timing of receiving requests based on the estimated user's emotions 2. The system of claim 1, (Appendix 10) The reception unit is Analyze the user's past request history and select the appropriate reception method 2. The system of claim 1, (Appendix 11) The reception unit is Filter requests based on your current projects and interests 2. The system of claim 1, (Appendix 12) The reception unit is Estimate the user's emotions and prioritize requests based on the estimated user emotions. 2. The system of claim 1, (Appendix 13) The reception unit is When accepting requests, the system takes into account the user's geographic location information to prioritize requests that are more relevant to the user. 2. The system of claim 1, (Appendix 14) The reception unit is When receiving your request, analyze your social media activity to receive related requests. 2. The system of claim 1, (Appendix 15) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, adjust the level of detail based on the importance of the request 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of request 2. The system of claim 1, (Appendix 18) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 19) The analysis unit is During the analysis, we will prioritize the analysis based on when the request was submitted. 2. The system of claim 1, (Appendix 20) The analysis unit is During analysis, adjust the order of analysis based on the relevance of requests 2. The system of claim 1, (Appendix 21) The generation unit is Infer user sentiment and prioritize page layout and content based on the inferred user sentiment 2. The system of claim 1, (Appendix 22) The generation unit is During generation, the accuracy of generation is improved by considering the correlation of requests. 2. The system of claim 1, (Appendix 23) The generation unit is When generating a request, the attribute information of the request submitter is taken into consideration. 2. The system of claim 1, (Appendix 24) The generation unit is Infer user sentiment and adjust the page layout and content display method based on the inferred user sentiment 2. The system of claim 1, (Appendix 25) The generation unit is When generating, the geographical distribution of requests is taken into account. 2. The system of claim 1, (Appendix 26) The generation unit is During generation, the accuracy of the generation is improved by referring to related literature of the request. 2. The system of claim 1, (Appendix 27) The providing unit is Infer user sentiment and adjust the page layout and content presentation based on the inferred sentiment 2. The system of claim 1, (Appendix 28) The providing unit is When providing the information, the optimal display method is selected by referring to the user's past operation history. 2. The system of claim 1, (Appendix 29) The providing unit is Optimize how generated page layouts and content are stored at serving time 2. The system of claim 1, (Appendix 30) The providing unit is Infer user emotions and adjust page layout and content navigation based on the inferred user emotions 2. The system of claim 1, (Appendix 31) The providing unit is When providing the content, the optimal display method is selected taking into account the user's device information. 2. The system of claim 1, (Appendix 32) The providing unit is Optimize how generated page layouts and content are managed at delivery time 2. The system of claim 1, [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives a request from a user; an analysis unit that analyzes the request received by the reception unit; a generation unit that generates a page layout and contents based on the requirements analyzed by the analysis unit; a providing unit that provides the page layout and content generated by the generating unit. A system characterized by:

2. The generation unit is Generate simple layout designs based on user requests 2. The system of claim 1 .

3. The generation unit is Generate content that displays the latest news at the top based on user requests 2. The system of claim 1 .

4. The generation unit is Generate layouts using multiple images based on user requests 2. The system of claim 1 .

5. The generation unit is Generate a layout with enlarged text based on user requests 2. The system of claim 1 .

6. The providing unit is The page layout and content generated by the generating unit are provided to the user in real time.

2. The system of claim 1 .

7. The providing unit is Allows a user to confirm and modify the page layout and content generated by the generating unit 2. The system of claim 1 .

8. The providing unit is The page layout and contents generated by the generating unit are stored and managed.

2. The system of claim 1 .

Images