system

Abstract

The system according to this embodiment aims to provide an environment in which even users with limited IT knowledge can easily build and operate a website. [Solution] The system according to the embodiment comprises a domain management unit, a server management unit, a CMS construction unit, a design generation unit, and an SEO setting unit. The domain management unit acquires and applies domains. The server management unit arranges servers based on the domains acquired by the domain management unit. The CMS construction unit builds a CMS environment on the servers arranged by the server management unit. The design generation unit generates a site design based on the CMS environment built by the CMS construction unit. The SEO setting unit sets SEO based on the design generated by the design generation unit.

Description

Technical Field

[0004] ,

[0006] , , ,

[0005] , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is necessary to combine a plurality of operations and services required for site construction, which is difficult to operate for those with little IT knowledge.

[0005] [[ID=​​​​​​The system according to the embodiment comprises a domain management unit, a server management unit, a CMS construction unit, a design generation unit, and an SEO setting unit. The domain management unit acquires and applies domains. The server management unit arranges servers based on the domains acquired by the domain management unit. The CMS construction unit builds a CMS environment on the servers arranged by the server management unit. The design generation unit generates a site design based on the CMS environment built by the CMS construction unit. The SEO setting unit configures SEO based on the design generated by the design generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide an environment in which even users with limited IT knowledge can easily build and operate a website. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The website construction system according to an embodiment of the present invention is a fully managed service that utilizes generative AI to enable even those unfamiliar with IT to easily build and operate websites. Conventionally, building a website required many tasks such as domain acquisition, server preparation, CMS environment setup, site design, and SEO management, all of which required specialized knowledge. The present invention centralizes these tasks and provides an environment in which users can easily build and operate websites. First, users simply select a domain, plan, and whether or not to use a CMS, and the domain acquisition and application, server arrangement, and CMS environment setup are automatically performed. This eliminates the need for users to search for instructions on the internet, manually enter record values ​​as in the past, or log in to the server. Next, for site design and SEO management, the system uses generative AI to generate designs and provides a no-code tool. This allows users to easily create their desired designs even without knowledge. Furthermore, the system uses generative AI to generate appropriate tags from the page content, enabling a certain level of effectiveness without requiring SEO settings. This mechanism removes many barriers for people who want to operate a website, allowing them to concentrate on writing pages. We provide an environment that allows for centralized management and operation, ensuring security and low costs. Specifically, it consists of the following steps: First, the user selects a domain, plan, and whether or not to use a CMS. Next, the generation AI automatically acquires and applies the domain, arranges the server, and sets up the CMS environment. Furthermore, the generation AI generates the site design and provides a no-code tool. Finally, the generation AI generates appropriate tags from the page content and sets up SEO. This system allows users to intuitively build and operate websites without complex operations. For example, simply by the user selecting a domain, the generation AI automatically acquires and applies the domain, arranges the server, and sets up the CMS environment. In addition, the generation AI generates the site design and provides a no-code tool, allowing users to easily create their desired design even without technical knowledge.Furthermore, the generation AI generates appropriate tags from the page content and performs SEO settings, allowing users to operate an effective website even without SEO knowledge. In this way, the present invention provides a fully managed service that utilizes generation AI to enable even those unfamiliar with IT to easily build and operate websites, thereby eliminating many barriers for those who want to operate a site and providing an environment where they can concentrate on writing pages. As a result, the website building system can provide an environment in which users can easily build and operate websites.

[0029] The website construction system according to this embodiment comprises a domain management unit, a server management unit, a CMS construction unit, a design generation unit, and an SEO setting unit. The domain management unit acquires and applies domains. For example, the domain management unit automatically acquires and applies a domain selected by the user. The domain management unit can acquire and apply domains based on the selection criteria and application procedures of the domain registrar. The server management unit arranges servers based on the domains acquired by the domain management unit. For example, the server management unit selects servers based on criteria such as server performance, cost, and reliability in order to arrange the most suitable server. The server management unit can arrange servers according to the server arrangement procedures. The CMS construction unit builds a CMS environment on the servers arranged by the server management unit. For example, the CMS construction unit builds a CMS environment based on criteria such as the type of CMS to be used, installation procedures, and configuration methods. The CMS construction unit can build a CMS environment according to the CMS installation procedures. The design generation unit generates the site design based on the CMS environment built by the CMS construction unit. The design generation unit generates designs based on criteria such as the design tools to be used and the selection criteria for design templates. The design generation unit can generate designs using generation AI. The SEO setting unit performs SEO settings based on the designs generated by the design generation unit. The SEO setting unit performs SEO settings based on criteria such as the SEO tools to be used, setting items, and evaluation criteria. The SEO setting unit can perform SEO settings using generation AI. As a result, the website construction system according to the embodiment can provide an environment in which users can easily build and operate websites.

[0030] The Domain Management Department handles domain acquisition and application. Specifically, it has the function to automatically acquire and apply domains selected by the user. The Domain Management Department can acquire and apply domains based on the selection criteria and application procedures of domain registrars. For example, when a user enters their desired domain name, the Domain Management Department uses the APIs of multiple domain registrars to check the availability of that domain and select the most suitable registrar. Selection criteria include price, reliability, and support system. If the domain is available, the Domain Management Department automatically purchases the domain and applies it to the user's account. Furthermore, the Domain Management Department also automatically configures the DNS settings of the acquired domain and works with the Server Management Department to set the appropriate DNS records. This allows users to easily acquire domains and apply them to their websites without going through complex procedures. The Domain Management Department also supports domain renewal and transfer procedures, enabling users to manage their domains long-term. For example, when a domain's expiration date approaches, it sends a notification to the user prompting them to set up automatic renewal. This prevents domain expiration and supports the continuous operation of the website.

[0031] The Server Management Department arranges servers based on domains acquired by the Domain Management Department. Specifically, to arrange the optimal server, they select servers based on criteria such as performance, cost, and reliability. The Server Management Department selects the best server from multiple cloud service providers and hosting services, providing a server environment that meets the user's needs. For example, they select high-performance servers for websites with high traffic and low-cost servers for users who prioritize cost. The Server Management Department arranges servers according to the server arrangement procedure and performs the necessary settings. Specifically, they automatically perform initial server setup, security settings, and backup settings, creating an environment where users can immediately operate their websites. Furthermore, the Server Management Department also monitors and maintains the servers, constantly monitoring their operational status and responding quickly if any abnormalities occur. For example, if the server resource utilization becomes high, they automatically add resources to maintain server performance. They also perform regular backups to support data protection and recovery. In this way, the Server Management Department provides a highly reliable server environment so that users can operate their websites with peace of mind.

[0032] The CMS Construction Department builds the CMS environment on servers arranged by the Server Management Department. Specifically, they build the CMS environment based on criteria such as the type of CMS to be used, installation procedures, and configuration methods. The CMS Construction Department automatically installs the CMS selected by the user (e.g., WordPress, Joomla, Drupal, etc.) and performs initial setup. The installation procedure includes database configuration, installation of necessary plugins, and theme application. The CMS Construction Department automates these procedures, allowing users to use the CMS environment without any hassle. Furthermore, the CMS Construction Department also supports customization according to user needs. For example, they install plugins to add specific functions and customize themes. They also perform CMS security settings and performance optimization, ensuring that users can operate their websites safely and comfortably. In this way, the CMS Construction Department enables users to easily build a CMS environment and efficiently manage website content.

[0033] The design generation unit generates website designs based on the CMS environment built by the CMS construction unit. Specifically, it generates designs based on criteria such as the design tools to be used and the selection criteria for design templates. The design generation unit can generate designs using a generation AI. The generation AI proposes and customizes design templates according to the user's requests and industry. For example, based on keywords, color preferences, and layout requests entered by the user, the generation AI generates the optimal design. The generation AI learns from past design data and trend information, providing designs that take into account the latest design styles and usability. Furthermore, the design generation unit applies the generated design to the CMS, allowing users to immediately publish their websites. The design generation unit also provides an interface that allows users to fine-tune the design, supporting detailed customization. In this way, the design generation unit enables users to easily generate professional designs and apply them to their websites.

[0034] The SEO settings unit configures SEO based on the design generated by the design generation unit. Specifically, it configures SEO based on criteria such as the SEO tools used, settings items, and evaluation criteria. The SEO settings unit can configure SEO using a generation AI. The generation AI analyzes the website's content and structure and proposes optimal SEO settings. For example, it automatically performs keyword selection, meta tag settings, and internal link optimization. The generation AI learns search engine algorithms and provides settings based on the latest SEO trends. Furthermore, the SEO settings unit monitors website performance and regularly evaluates SEO. Based on the evaluation results, it proposes necessary improvements and supports users in continuously optimizing their SEO. In this way, the SEO settings unit provides effective SEO settings that allow users to improve their search engine rankings and increase website traffic.

[0035] The no-code tool provider provides no-code tools. For example, the no-code tool provider provides no-code tools with drag-and-drop functionality. The no-code tool provider can provide template types and customization options. The no-code tool provider enables users to easily create desired designs without prior knowledge. The no-code tool provider can provide no-code tools using generative AI. This allows users to easily create desired designs without prior knowledge. Some or all of the above-described processes in the no-code tool provider may be performed using AI, or not. For example, the no-code tool provider can input a no-code tool into a generative AI based on user input and have the generative AI provide the no-code tool.

[0036] The page content generation unit generates page content. The page content generation unit generates page content based on, for example, methods for generating text, images, and videos. The page content generation unit can generate page content using a generation AI. The page content generation unit makes it easy for users to generate page content. The page content generation unit can generate page content using a generation AI. This allows users to easily generate page content. Some or all of the above-described processes in the page content generation unit may be performed using, for example, AI, or without AI. For example, the page content generation unit can input page content to a generation AI based on user input and have the generation AI execute the generation of page content.

[0037] The tag generation unit generates appropriate tags. The tag generation unit generates appropriate tags based on selection criteria, such as meta tags and keyword tags. The tag generation unit can generate appropriate tags using generation AI. The tag generation unit is designed to be effective to some extent even without SEO settings. The tag generation unit can generate appropriate tags using generation AI. This allows it to be effective to some extent even without SEO settings. Some or all of the above-described processes in the tag generation unit may be performed using AI, for example, or without AI. For example, the tag generation unit can input the page content into the generation AI and have the generation AI generate appropriate tags.

[0038] The domain management unit can automatically acquire and apply domains selected by the user. The domain management unit can automatically acquire and apply domains based, for example, on how the API is used or how automation tools are configured. The domain management unit makes it easy for users to acquire and apply domains. The domain management unit can automatically acquire and apply domains using a generative AI. This allows users to easily acquire and apply domains. Some or all of the above-described processes in the domain management unit may be performed using AI, or not. For example, the domain management unit can input the domain selected by the user into a generative AI and have the generative AI acquire and apply the domain.

[0039] The Server Management Department can arrange the optimal server based on the domain acquired by the Domain Management Department. The Server Management Department arranges the optimal server based on criteria such as server performance, cost, and reliability. The Server Management Department makes it easy for users to arrange the optimal server. The Server Management Department can arrange the optimal server using a generative AI. This makes it easy for users to arrange the optimal server. Some or all of the above processes in the Server Management Department may be performed using AI, or not using AI. For example, the Server Management Department can input the domain acquired by the Domain Management Department into a generative AI and have the generative AI arrange the optimal server.

[0040] The CMS Construction Department can build a CMS environment on a server arranged by the Server Management Department. The CMS Construction Department builds the CMS environment based on criteria such as the type of CMS to be used, installation procedures, and configuration methods. The CMS Construction Department makes it easy for users to build a CMS environment. The CMS Construction Department can build a CMS environment using a generation AI. This allows users to easily build a CMS environment. Some or all of the above-described processes in the CMS Construction Department may be performed using AI, or not. For example, the CMS Construction Department can input a server arranged by the Server Management Department into the generation AI and have the generation AI execute the construction of the CMS environment.

[0041] The domain management unit can analyze a user's past domain acquisition history and select the optimal acquisition method. For example, the domain management unit can analyze patterns in domains previously acquired by the user and acquire new domains using similar patterns. The domain management unit can also prioritize domain acquisition methods that have been successful for the user in the past. The domain management unit can also suggest the most efficient acquisition method based on the user's past domain acquisition history. This allows the optimal domain acquisition method to be selected based on the user's past history. Some or all of the above processes in the domain management unit may be performed using AI, for example, or not. For example, the domain management unit can input the user's past domain acquisition history into a generating AI and have the generating AI select the optimal acquisition method.

[0042] The domain management unit can filter domains based on the user's current projects and areas of interest when acquiring them. For example, the domain management unit can prioritize acquiring domains related to the user's current ongoing projects. The domain management unit can also filter highly relevant domains based on the user's areas of interest. The domain management unit can also suggest the most suitable domains according to the progress of the user's projects. This allows the user to acquire the most suitable domains based on their projects and areas of interest. Some or all of the above processing in the domain management unit may be performed using AI, for example, or not using AI. For example, the domain management unit can input the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0043] The domain management unit can prioritize acquiring domains that are highly relevant to the user, taking into account the user's geographical location information. For example, the domain management unit can prioritize acquiring domains related to the user's location. The domain management unit can also prioritize acquiring domains related to the user's business area. The domain management unit can also prioritize acquiring domains related to the user's target market. This allows the system to acquire the most suitable domain based on the user's geographical location information. Some or all of the above processing in the domain management unit may be performed using AI, for example, or without AI. For example, the domain management unit can input the user's geographical location information into a generating AI and have the generating AI acquire highly relevant domains.

[0044] The domain management unit can analyze a user's social media activity and acquire relevant domains when acquiring a domain. For example, the domain management unit can suggest relevant domains based on the user's social media activity. The domain management unit can also acquire relevant domains based on the user's followers' areas of interest. The domain management unit can also acquire relevant domains based on the user's social media trends. This allows the system to acquire the most suitable domains based on the user's social media activity. Some or all of the above processes in the domain management unit may be performed using AI, for example, or without AI. For example, the domain management unit can input the user's social media activity into a generating AI and have the generating AI acquire relevant domains.

[0045] The server management department can analyze a user's past server usage history and select the optimal arrangement method. For example, the server management department can analyze patterns of servers previously used by the user and arrange new servers using similar patterns. The server management department can also prioritize server arrangement methods that have been successful for the user in the past. The server management department can also propose the most efficient arrangement method based on the user's past server usage history. This allows for the selection of the optimal server arrangement method based on the user's past history. Some or all of the above processes in the server management department may be performed using AI, for example, or without AI. For example, the server management department can input the user's past server usage history into a generating AI and have the generating AI select the optimal arrangement method.

[0046] The server management unit can filter servers based on the user's current projects and areas of interest when arranging servers. For example, the server management unit can prioritize servers related to the user's current projects. The server management unit can also filter servers based on the user's areas of interest to ensure they are relevant. The server management unit can also suggest the most suitable server depending on the progress of the user's project. This ensures that the optimal server is arranged based on the user's projects and areas of interest. Some or all of the above processes in the server management unit may be performed using AI, for example, or not. For example, the server management unit can input the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0047] The server management department can prioritize the allocation of servers that are highly relevant to the user, taking into account the user's geographical location information when arranging servers. For example, the server management department can prioritize servers related to the user's location. The server management department can also prioritize servers related to the user's business area. The server management department can also prioritize servers related to the user's target market. This allows the server management department to allocate the optimal server based on the user's geographical location information. Some or all of the above processing in the server management department may be performed using AI, for example, or not using AI. For example, the server management department can input the user's geographical location information into a generating AI and have the generating AI arrange highly relevant servers.

[0048] The server management unit can analyze a user's social media activity and arrange relevant servers when arranging servers. For example, the server management unit can suggest relevant servers based on the user's social media activity. The server management unit can also arrange relevant servers based on the user's followers' areas of interest. The server management unit can also arrange relevant servers based on the user's social media trends. This allows for the arrangement of the optimal server based on the user's social media activity. Some or all of the above processes in the server management unit may be performed using AI, for example, or not using AI. For example, the server management unit can input the user's social media activity into a generating AI and have the generating AI arrange the relevant servers.

[0049] The CMS construction unit can analyze the user's past CMS usage history and select the optimal construction method. For example, the CMS construction unit can analyze the patterns of CMSs the user has used in the past and build a new CMS environment using similar patterns. The CMS construction unit can also prioritize selecting CMS construction methods that have been successful for the user in the past. The CMS construction unit can also propose the most efficient construction method based on the user's past CMS usage history. This allows for the selection of the optimal CMS construction method based on the user's past history. Some or all of the above processes in the CMS construction unit may be performed using AI, for example, or not using AI. For example, the CMS construction unit can input the user's past CMS usage history into a generating AI and have the generating AI select the optimal construction method.

[0050] The CMS construction unit can filter CMS environments based on the user's current projects and areas of interest. For example, the CMS construction unit can prioritize building CMS environments related to the user's current projects. The CMS construction unit can also filter highly relevant CMS environments based on the user's areas of interest. The CMS construction unit can also suggest the optimal CMS environment according to the progress of the user's projects. This allows for the construction of an optimal CMS environment based on the user's projects and areas of interest. Some or all of the above processing in the CMS construction unit may be performed using AI, for example, or without AI. For example, the CMS construction unit can input the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0051] The CMS construction unit can prioritize the construction of highly relevant CMS environments by considering the user's geographical location information when building a CMS environment. For example, the CMS construction unit can prioritize the construction of CMS environments related to the user's location. The CMS construction unit can also prioritize the construction of CMS environments related to the user's business area. The CMS construction unit can also prioritize the construction of CMS environments related to the user's target market. This allows for the construction of an optimal CMS environment based on the user's geographical location information. Some or all of the above processing in the CMS construction unit may be performed using AI, for example, or without AI. For example, the CMS construction unit can input the user's geographical location information into a generating AI and have the generating AI execute the construction of highly relevant CMS environments.

[0052] The CMS construction unit can analyze a user's social media activity and construct a relevant CMS environment when building a CMS environment. For example, the CMS construction unit can suggest a relevant CMS environment based on the user's social media activity. The CMS construction unit can also construct a relevant CMS environment based on the user's followers' areas of interest. The CMS construction unit can also construct a relevant CMS environment based on the user's social media trends. This allows for the construction of an optimal CMS environment based on the user's social media activity. Some or all of the above processing in the CMS construction unit may be performed using AI, for example, or without AI. For example, the CMS construction unit can input the user's social media activity into a generating AI and have the generating AI execute the construction of the relevant CMS environment.

[0053] The design generation unit can adjust the level of detail in the design based on the importance of the site during the design generation process. For example, the design generation unit can generate a detailed and professional design for an important business site. For a personal blog, it can generate a simple and user-friendly design. For an event site, it can generate a visually appealing and interactive design. This allows for the optimal level of detail in the design to be adjusted according to the importance of the site. Some or all of the above processes in the design generation unit may be performed using AI, for example, or not. For example, the design generation unit can input the importance of the site into the generation AI and have the generation AI perform the adjustment of the level of detail in the design.

[0054] The design generation unit can apply different design algorithms depending on the site category during design generation. For example, in the case of an e-commerce site, the design generation unit can apply a design algorithm specialized for product display. In the case of a news site, the design generation unit can also apply a design algorithm that prioritizes the visibility of information. In the case of a portfolio site, the design generation unit can also apply a design algorithm that enhances the appeal of the works. This allows for the application of the optimal design algorithm according to the site category. Some or all of the above-described processes in the design generation unit may be performed using AI, for example, or without AI. For example, the design generation unit can input the site category into a generation AI and have the generation AI execute the application of the design algorithm.

[0055] The design generation unit can determine design priorities based on the site's submission deadline during design generation. For example, the design generation unit will prioritize generating designs for sites with approaching deadlines. For sites with ample time before submission, the design generation unit can prioritize designs for other sites. If the submission deadline is unknown, the design generation unit can also determine priorities based on the importance of the site. This allows for the determination of the optimal design priority according to the site's submission deadline. Some or all of the above processes in the design generation unit may be performed using AI, for example, or not. For example, the design generation unit can input the site's submission deadline into a generation AI and have the generation AI determine the design priorities.

[0056] The design generation unit can adjust the order of designs based on the relevance of the sites during the design generation process. For example, the design generation unit can prioritize designing highly relevant sites. It can also postpone designing less relevant sites. If the relevance is unknown, the design generation unit can determine the order based on the importance of the sites. This allows for the optimal design order to be adjusted according to the relevance of the sites. Some or all of the above processes in the design generation unit may be performed using AI, for example, or not using AI. For example, the design generation unit can input the relevance of the sites into a generation AI and have the generation AI perform the adjustment of the design order.

[0057] The SEO settings section can adjust the level of detail in SEO settings based on the importance of the site during the SEO setup process. For example, the SEO settings section can perform detailed and professional SEO settings for important business sites. For personal blogs, it can perform basic SEO settings. For event sites, it can perform SEO settings tailored to specific events. This allows for the adjustment of the optimal level of detail in SEO settings according to the importance of the site. Some or all of the above processes in the SEO settings section may be performed using AI, for example, or not. For example, the SEO settings section can input the importance of the site into a generating AI and have the generating AI perform the adjustment of the level of detail in the SEO settings.

[0058] The SEO settings unit can apply different SEO algorithms depending on the site's category during SEO configuration. For example, for an e-commerce site, the SEO settings unit can apply an SEO algorithm specifically tailored to product pages. For a news site, the SEO settings unit can also apply an SEO algorithm that prioritizes article readability. For a portfolio site, the SEO settings unit can also apply an SEO algorithm that highlights the appeal of the works. This allows for the application of the optimal SEO algorithm for each site category. Some or all of the above-described processes in the SEO settings unit may be performed using AI, for example, or without AI. For example, the SEO settings unit can input the site's category into a generating AI and have the generating AI execute the application of the SEO algorithm.

[0059] The SEO settings unit can determine the priority of SEO settings based on the site's submission date when configuring SEO. For example, the SEO settings unit will prioritize SEO settings for sites with approaching deadlines. For sites with ample time before submission, the SEO settings unit can prioritize SEO settings for other sites. If the submission date is unknown, the SEO settings unit can determine priority based on the importance of the site. This allows for the determination of the optimal SEO setting priority according to the site's submission date. Some or all of the above processes in the SEO settings unit may be performed using AI, for example, or not. For example, the SEO settings unit can input the site's submission date into a generating AI and have the generating AI determine the priority of the SEO settings.

[0060] The SEO settings unit can adjust the order of SEO settings based on the relevance of the sites during the SEO setup process. For example, the SEO settings unit can prioritize highly relevant sites. It can also postpone less relevant sites. If relevance is unknown, the SEO settings unit can determine the order based on the importance of the sites. This allows for the optimal adjustment of the SEO settings order according to the relevance of the sites. Some or all of the above processes in the SEO settings unit may be performed using AI, for example, or not. For example, the SEO settings unit can input the relevance of the sites into a generating AI and have the generating AI perform the adjustment of the SEO settings order.

[0061] The no-code tool provider can analyze the user's past tool usage history to select the optimal delivery method when providing no-code tools. For example, the no-code tool provider can analyze patterns of tools the user has used in the past and provide new tools using similar patterns. The no-code tool provider can also prioritize selecting tool usage methods that the user has successfully used in the past. The no-code tool provider can also propose the most efficient delivery method based on the user's past tool usage history. This allows for the selection of the optimal no-code tool delivery method based on the user's past history. Some or all of the above processing in the no-code tool provider may be performed using AI, for example, or without AI. For example, the no-code tool provider can input the user's past tool usage history into a generating AI and have the generating AI select the optimal delivery method.

[0062] The no-code tool provider can filter no-code tools based on the user's current projects and areas of interest when providing them. For example, the no-code tool provider can prioritize providing tools related to the user's current ongoing projects. The no-code tool provider can also filter highly relevant tools based on the user's areas of interest. The no-code tool provider can also suggest the most suitable tools according to the progress of the user's projects. This allows the provider to offer the most suitable no-code tools based on the user's projects and areas of interest. Some or all of the above processing in the no-code tool provider may be performed using AI, for example, or not using AI. For example, the no-code tool provider can input the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0063] The no-code tool provider can prioritize providing highly relevant tools by considering the user's geographical location when providing no-code tools. For example, the no-code tool provider can prioritize providing tools related to the user's location. The no-code tool provider can also prioritize providing tools related to the user's business area. The no-code tool provider can also prioritize providing tools related to the user's target market. This allows the provider to offer the most suitable no-code tool based on the user's geographical location. Some or all of the above processing in the no-code tool provider may be performed using AI, for example, or without AI. For example, the no-code tool provider can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing highly relevant tools.

[0064] The no-code tool provider can analyze the user's social media activity and provide relevant tools when providing no-code tools. For example, the no-code tool provider can suggest relevant tools based on the user's social media activity. The no-code tool provider can also provide relevant tools based on the user's followers' areas of interest. The no-code tool provider can also provide relevant tools based on the user's social media trends. This allows the provider to offer the most suitable no-code tool based on the user's social media activity. Some or all of the above processing in the no-code tool provider may be performed using AI, for example, or without AI. For example, the no-code tool provider can input the user's social media activity into a generating AI and have the generating AI perform the provision of relevant tools.

[0065] The page content generation unit can adjust the level of detail of content based on the importance of the site when generating page content. For example, for an important business site, the page content generation unit can generate detailed and professional content. For a personal blog, the page content generation unit can also generate simple and approachable content. For an event site, the page content generation unit can also generate visually appealing and interactive content. This allows for the optimal level of detail of content to be adjusted according to the importance of the site. Some or all of the above processing in the page content generation unit may be performed using AI, for example, or not. For example, the page content generation unit can input the importance of the site into the generation AI and have the generation AI perform the adjustment of the level of detail of the content.

[0066] The page content generation unit can apply different content generation algorithms depending on the site category when generating page content. For example, in the case of an e-commerce site, the page content generation unit can apply a content generation algorithm specialized for product descriptions. In the case of a news site, the page content generation unit can also apply a content generation algorithm that prioritizes the readability of articles. In the case of a portfolio site, the page content generation unit can also apply a content generation algorithm that highlights the appeal of the works. This allows for the application of the optimal content generation algorithm according to the site category. Some or all of the above-described processes in the page content generation unit may be performed using AI, for example, or without AI. For example, the page content generation unit can input the site category into a generation AI and have the generation AI execute the application of the content generation algorithm.

[0067] The page content generation unit can determine content priority based on the submission deadline of each site when generating page content. For example, the page content generation unit will prioritize content for sites with approaching deadlines. If a site has ample time before its submission deadline, the page content generation unit can prioritize content from other sites. If the submission deadline is unknown, the page content generation unit can also determine priority based on the importance of each site. This allows for the determination of the optimal content priority according to the submission deadline of each site. Some or all of the above processes in the page content generation unit may be performed using AI, for example, or not. For example, the page content generation unit can input the submission deadline of each site into a generation AI and have the generation AI determine the content priority.

[0068] The page content generation unit can adjust the order of content based on the relevance of the sites when generating page content. For example, the page content generation unit can prioritize generating content for highly relevant sites. The page content generation unit can also postpone generating content for less relevant sites. If the relevance is unknown, the page content generation unit can also determine the order based on the importance of the sites. This allows for the optimal content order to be adjusted according to the relevance of the sites. Some or all of the above processing in the page content generation unit may be performed using AI, for example, or without AI. For example, the page content generation unit can input the relevance of the sites into a generation AI and have the generation AI perform the adjustment of the content order.

[0069] The tag generation unit can adjust the level of detail of tags based on the importance of the site during tag generation. For example, the tag generation unit can generate detailed and professional tags for important business sites. For personal blogs, it can generate simple and user-friendly tags. For event sites, it can generate visually appealing and interactive tags. This allows for optimal adjustment of tag detail according to the importance of the site. Some or all of the above processes in the tag generation unit may be performed using AI, for example, or not. For example, the tag generation unit can input the importance of the site into the generation AI and have the generation AI perform the adjustment of tag detail.

[0070] The tag generation unit can apply different tag generation algorithms depending on the site category when generating tags. For example, in the case of an e-commerce site, the tag generation unit can apply a tag generation algorithm specialized for product descriptions. In the case of a news site, the tag generation unit can also apply a tag generation algorithm that prioritizes the readability of articles. In the case of a portfolio site, the tag generation unit can also apply a tag generation algorithm that highlights the appeal of the works. This allows the application of the optimal tag generation algorithm according to the site category. Some or all of the above processing in the tag generation unit may be performed using AI, for example, or without AI. For example, the tag generation unit can input the site category into a generation AI and have the generation AI execute the application of the tag generation algorithm.

[0071] The tag generation unit can determine the priority of tags based on the submission deadline of the site during tag generation. For example, the tag generation unit will generate tags with the highest priority for sites with approaching deadlines. If a site has ample time before its submission deadline, the tag generation unit can also prioritize tags from other sites. If the submission deadline is unknown, the tag generation unit can also determine the priority based on the importance of the site. This allows for the determination of the optimal tag priority according to the submission deadline of the site. Some or all of the above processes in the tag generation unit may be performed using AI, for example, or without AI. For example, the tag generation unit can input the submission deadline of the site into a generation AI and have the generation AI perform the determination of tag priority.

[0072] The tag generation unit can adjust the order of tags based on the relevance of the sites during tag generation. For example, the tag generation unit can prioritize tag generation for highly relevant sites. The tag generation unit can also postpone tag generation for less relevant sites. If relevance is unknown, the tag generation unit can determine the order based on the importance of the sites. This allows for the optimal order of tags to be adjusted according to the relevance of the sites. Some or all of the above processes in the tag generation unit may be performed using AI, for example, or without AI. For example, the tag generation unit can input the relevance of the sites into a generation AI and have the generation AI perform the adjustment of the tag order.

[0073] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0074] The website building system may further include a history analysis unit that analyzes the user's past website building history and proposes the optimal building method. The history analysis unit, for example, analyzes patterns in websites the user has built in the past and builds a new website using similar patterns. It can also prioritize suggesting building methods that have been successful for the user in the past. It can also suggest the most efficient building method based on the user's past history. This allows for the selection of the optimal website building method based on the user's past history. Some or all of the above processing in the history analysis unit may be performed using AI or not. For example, the history analysis unit can input the user's past website building history into a generating AI and have the generating AI propose the optimal building method.

[0075] The website building system may further include a domain filtering unit that prioritizes the acquisition of highly relevant domains, taking into account the user's geographical location information. For example, the domain filtering unit may prioritize the acquisition of domains related to the user's location. It may also prioritize the acquisition of domains related to the user's business area. It may also prioritize the acquisition of domains related to the user's target market. This allows the system to acquire the most suitable domain based on the user's geographical location information. Some or all of the above processing in the domain filtering unit may be performed using AI or not. For example, the domain filtering unit may input the user's geographical location information into a generating AI and have the generating AI acquire highly relevant domains.

[0076] The website building system may further include a social media analysis unit that analyzes the user's social media activity and retrieves relevant domains. The social media analysis unit may, for example, suggest relevant domains based on the user's social media activity. It may also retrieve relevant domains based on the user's followers' areas of interest. It may also retrieve relevant domains based on the user's social media trends. This allows for the acquisition of the most suitable domains based on the user's social media activity. Some or all of the above-described processes in the social media analysis unit may be performed using AI or not. For example, the social media analysis unit may input the user's social media activity into a generating AI and have the generating AI retrieve relevant domains.

[0077] The website building system may further include a project filtering unit that filters based on the user's current projects and areas of interest. The project filtering unit, for example, prioritizes obtaining domains related to the user's current projects. It can also filter highly relevant domains based on the user's areas of interest. It can even suggest the most suitable domains according to the user's project progress. This allows the system to obtain the most suitable domains based on the user's projects and areas of interest. Some or all of the above processing in the project filtering unit may be performed using AI or not. For example, the project filtering unit can input the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0078] The website building system may further include a tool history analysis unit that analyzes the user's past tool usage history to select the optimal delivery method. For example, the tool history analysis unit can analyze patterns of tools the user has used in the past and provide new tools using similar patterns. It can also prioritize selecting tool usage methods that have been successful for the user in the past. Furthermore, it can propose the most efficient delivery method based on the user's past tool usage history. This allows for the selection of the optimal delivery method for no-code tools based on the user's past history. Some or all of the above-described processes in the tool history analysis unit may be performed using AI or not. For example, the tool history analysis unit can input the user's past tool usage history into a generating AI and have the generating AI select the optimal delivery method.

[0079] The following briefly describes the processing flow for example form 1.

[0080] Step 1: The Domain Management Department acquires and applies the domain. For example, it automatically acquires and applies the domain selected by the user. The Domain Management Department acquires and applies the domain based on the domain registrar's selection criteria and application procedures. Step 2: The Server Management Department arranges servers based on the domains acquired by the Domain Management Department. For example, to arrange the most suitable servers, they select servers based on criteria such as performance, cost, and reliability. The Server Management Department arranges servers according to the server arrangement procedure. Step 3: The CMS Construction Department builds the CMS environment on the server arranged by the Server Management Department. For example, they build the CMS environment based on criteria such as the type of CMS to be used, installation procedure, and configuration method. The CMS Construction Department builds the CMS environment according to the CMS installation procedure. Step 4: The design generation unit generates the site design based on the CMS environment built by the CMS construction unit. For example, it generates the design based on criteria such as the design tools to be used and the selection criteria for design templates. The design generation unit can generate the design using a generation AI. Step 5: The SEO settings section configures SEO settings based on the design generated by the design generation section. For example, it configures SEO settings based on criteria such as the SEO tools to be used, setting items, and evaluation criteria. The SEO settings section can configure SEO settings using generation AI.

[0081] (Example of form 2) The website construction system according to an embodiment of the present invention is a fully managed service that utilizes generative AI to enable even those unfamiliar with IT to easily build and operate websites. Conventionally, building a website required many tasks such as domain acquisition, server preparation, CMS environment setup, site design, and SEO management, all of which required specialized knowledge. The present invention centralizes these tasks and provides an environment in which users can easily build and operate websites. First, users simply select a domain, plan, and whether or not to use a CMS, and the domain acquisition and application, server arrangement, and CMS environment setup are automatically performed. This eliminates the need for users to search for instructions on the internet, manually enter record values ​​as in the past, or log in to the server. Next, for site design and SEO management, the system uses generative AI to generate designs and provides a no-code tool. This allows users to easily create their desired designs even without knowledge. Furthermore, the system uses generative AI to generate appropriate tags from the page content, enabling a certain level of effectiveness without requiring SEO settings. This mechanism removes many barriers for people who want to operate a website, allowing them to concentrate on writing pages. We provide an environment that allows for centralized management and operation, ensuring security and low costs. Specifically, it consists of the following steps: First, the user selects a domain, plan, and whether or not to use a CMS. Next, the generation AI automatically acquires and applies the domain, arranges the server, and sets up the CMS environment. Furthermore, the generation AI generates the site design and provides a no-code tool. Finally, the generation AI generates appropriate tags from the page content and sets up SEO. This system allows users to intuitively build and operate websites without complex operations. For example, simply by the user selecting a domain, the generation AI automatically acquires and applies the domain, arranges the server, and sets up the CMS environment. In addition, the generation AI generates the site design and provides a no-code tool, allowing users to easily create their desired design even without technical knowledge.Furthermore, the generation AI generates appropriate tags from the page content and performs SEO settings, allowing users to operate an effective website even without SEO knowledge. In this way, the present invention provides a fully managed service that utilizes generation AI to enable even those unfamiliar with IT to easily build and operate websites, thereby eliminating many barriers for those who want to operate a site and providing an environment where they can concentrate on writing pages. As a result, the website building system can provide an environment in which users can easily build and operate websites.

[0082] The website construction system according to this embodiment comprises a domain management unit, a server management unit, a CMS construction unit, a design generation unit, and an SEO setting unit. The domain management unit acquires and applies domains. For example, the domain management unit automatically acquires and applies a domain selected by the user. The domain management unit can acquire and apply domains based on the selection criteria and application procedures of the domain registrar. The server management unit arranges servers based on the domains acquired by the domain management unit. For example, the server management unit selects servers based on criteria such as server performance, cost, and reliability in order to arrange the most suitable server. The server management unit can arrange servers according to the server arrangement procedures. The CMS construction unit builds a CMS environment on the servers arranged by the server management unit. For example, the CMS construction unit builds a CMS environment based on criteria such as the type of CMS to be used, installation procedures, and configuration methods. The CMS construction unit can build a CMS environment according to the CMS installation procedures. The design generation unit generates the site design based on the CMS environment built by the CMS construction unit. The design generation unit generates designs based on criteria such as the design tools to be used and the selection criteria for design templates. The design generation unit can generate designs using generation AI. The SEO setting unit performs SEO settings based on the designs generated by the design generation unit. The SEO setting unit performs SEO settings based on criteria such as the SEO tools to be used, setting items, and evaluation criteria. The SEO setting unit can perform SEO settings using generation AI. As a result, the website construction system according to the embodiment can provide an environment in which users can easily build and operate websites.

[0083] The Domain Management Department handles domain acquisition and application. Specifically, it has the function to automatically acquire and apply domains selected by the user. The Domain Management Department can acquire and apply domains based on the selection criteria and application procedures of domain registrars. For example, when a user enters their desired domain name, the Domain Management Department uses the APIs of multiple domain registrars to check the availability of that domain and select the most suitable registrar. Selection criteria include price, reliability, and support system. If the domain is available, the Domain Management Department automatically purchases the domain and applies it to the user's account. Furthermore, the Domain Management Department also automatically configures the DNS settings of the acquired domain and works with the Server Management Department to set the appropriate DNS records. This allows users to easily acquire domains and apply them to their websites without going through complex procedures. The Domain Management Department also supports domain renewal and transfer procedures, enabling users to manage their domains long-term. For example, when a domain's expiration date approaches, it sends a notification to the user prompting them to set up automatic renewal. This prevents domain expiration and supports the continuous operation of the website.

[0084] The Server Management Department arranges servers based on domains acquired by the Domain Management Department. Specifically, to arrange the optimal server, they select servers based on criteria such as performance, cost, and reliability. The Server Management Department selects the best server from multiple cloud service providers and hosting services, providing a server environment that meets the user's needs. For example, they select high-performance servers for websites with high traffic and low-cost servers for users who prioritize cost. The Server Management Department arranges servers according to the server arrangement procedure and performs the necessary settings. Specifically, they automatically perform initial server setup, security settings, and backup settings, creating an environment where users can immediately operate their websites. Furthermore, the Server Management Department also monitors and maintains the servers, constantly monitoring their operational status and responding quickly if any abnormalities occur. For example, if the server resource utilization becomes high, they automatically add resources to maintain server performance. They also perform regular backups to support data protection and recovery. In this way, the Server Management Department provides a highly reliable server environment so that users can operate their websites with peace of mind.

[0085] The CMS Construction Department builds the CMS environment on servers arranged by the Server Management Department. Specifically, they build the CMS environment based on criteria such as the type of CMS to be used, installation procedures, and configuration methods. The CMS Construction Department automatically installs the CMS selected by the user (e.g., WordPress, Joomla, Drupal, etc.) and performs initial setup. The installation procedure includes database configuration, installation of necessary plugins, and theme application. The CMS Construction Department automates these procedures, allowing users to use the CMS environment without any hassle. Furthermore, the CMS Construction Department also supports customization according to user needs. For example, they install plugins to add specific functions and customize themes. They also perform CMS security settings and performance optimization, ensuring that users can operate their websites safely and comfortably. In this way, the CMS Construction Department enables users to easily build a CMS environment and efficiently manage website content.

[0086] The design generation unit generates website designs based on the CMS environment built by the CMS construction unit. Specifically, it generates designs based on criteria such as the design tools to be used and the selection criteria for design templates. The design generation unit can generate designs using a generation AI. The generation AI proposes and customizes design templates according to the user's requests and industry. For example, based on keywords, color preferences, and layout requests entered by the user, the generation AI generates the optimal design. The generation AI learns from past design data and trend information, providing designs that take into account the latest design styles and usability. Furthermore, the design generation unit applies the generated design to the CMS, allowing users to immediately publish their websites. The design generation unit also provides an interface that allows users to fine-tune the design, supporting detailed customization. In this way, the design generation unit enables users to easily generate professional designs and apply them to their websites.

[0087] The SEO settings unit configures SEO based on the design generated by the design generation unit. Specifically, it configures SEO based on criteria such as the SEO tools used, settings items, and evaluation criteria. The SEO settings unit can configure SEO using a generation AI. The generation AI analyzes the website's content and structure and proposes optimal SEO settings. For example, it automatically performs keyword selection, meta tag settings, and internal link optimization. The generation AI learns search engine algorithms and provides settings based on the latest SEO trends. Furthermore, the SEO settings unit monitors website performance and regularly evaluates SEO. Based on the evaluation results, it proposes necessary improvements and supports users in continuously optimizing their SEO. In this way, the SEO settings unit provides effective SEO settings that allow users to improve their search engine rankings and increase website traffic.

[0088] The no-code tool provider provides no-code tools. For example, the no-code tool provider provides no-code tools with drag-and-drop functionality. The no-code tool provider can provide template types and customization options. The no-code tool provider enables users to easily create desired designs without prior knowledge. The no-code tool provider can provide no-code tools using generative AI. This allows users to easily create desired designs without prior knowledge. Some or all of the above-described processes in the no-code tool provider may be performed using AI, or not. For example, the no-code tool provider can input a no-code tool into a generative AI based on user input and have the generative AI provide the no-code tool.

[0089] The page content generation unit generates page content. The page content generation unit generates page content based on, for example, methods for generating text, images, and videos. The page content generation unit can generate page content using a generation AI. The page content generation unit makes it easy for users to generate page content. The page content generation unit can generate page content using a generation AI. This allows users to easily generate page content. Some or all of the above-described processes in the page content generation unit may be performed using, for example, AI, or without AI. For example, the page content generation unit can input page content to a generation AI based on user input and have the generation AI execute the generation of page content.

[0090] The tag generation unit generates appropriate tags. The tag generation unit generates appropriate tags based on selection criteria, such as meta tags and keyword tags. The tag generation unit can generate appropriate tags using generation AI. The tag generation unit is designed to be effective to some extent even without SEO settings. The tag generation unit can generate appropriate tags using generation AI. This allows it to be effective to some extent even without SEO settings. Some or all of the above-described processes in the tag generation unit may be performed using AI, for example, or without AI. For example, the tag generation unit can input the page content into the generation AI and have the generation AI generate appropriate tags.

[0091] The domain management unit can automatically acquire and apply domains selected by the user. The domain management unit can automatically acquire and apply domains based, for example, on how the API is used or how automation tools are configured. The domain management unit makes it easy for users to acquire and apply domains. The domain management unit can automatically acquire and apply domains using a generative AI. This allows users to easily acquire and apply domains. Some or all of the above-described processes in the domain management unit may be performed using AI, or not. For example, the domain management unit can input the domain selected by the user into a generative AI and have the generative AI acquire and apply the domain.

[0092] The Server Management Department can arrange the optimal server based on the domain acquired by the Domain Management Department. The Server Management Department arranges the optimal server based on criteria such as server performance, cost, and reliability. The Server Management Department makes it easy for users to arrange the optimal server. The Server Management Department can arrange the optimal server using a generative AI. This makes it easy for users to arrange the optimal server. Some or all of the above processes in the Server Management Department may be performed using AI, or not using AI. For example, the Server Management Department can input the domain acquired by the Domain Management Department into a generative AI and have the generative AI arrange the optimal server.

[0093] The CMS Construction Department can build a CMS environment on a server arranged by the Server Management Department. The CMS Construction Department builds the CMS environment based on criteria such as the type of CMS to be used, installation procedures, and configuration methods. The CMS Construction Department makes it easy for users to build a CMS environment. The CMS Construction Department can build a CMS environment using a generation AI. This allows users to easily build a CMS environment. Some or all of the above-described processes in the CMS Construction Department may be performed using AI, or not. For example, the CMS Construction Department can input a server arranged by the Server Management Department into the generation AI and have the generation AI execute the construction of the CMS environment.

[0094] The domain management unit can estimate the user's emotions and adjust the timing of domain acquisition based on the estimated emotions. For example, if the user is stressed, the domain management unit will have the AI ​​acquire the domain during a time when the user can relax. If the user is in a hurry, the domain management unit can have the AI ​​acquire the domain immediately. If the user is relaxed, the domain management unit can have the AI ​​acquire the domain at the user's pace. This allows for domain acquisition at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the domain management unit may be performed using AI or not using AI. For example, the domain management unit can input user emotion data into the generative AI and have the generative AI adjust the timing of domain acquisition.

[0095] The domain management unit can analyze a user's past domain acquisition history and select the optimal acquisition method. For example, the domain management unit can analyze patterns in domains previously acquired by the user and acquire new domains using similar patterns. The domain management unit can also prioritize domain acquisition methods that have been successful for the user in the past. The domain management unit can also suggest the most efficient acquisition method based on the user's past domain acquisition history. This allows the optimal domain acquisition method to be selected based on the user's past history. Some or all of the above processes in the domain management unit may be performed using AI, for example, or not. For example, the domain management unit can input the user's past domain acquisition history into a generating AI and have the generating AI select the optimal acquisition method.

[0096] The domain management unit can filter domains based on the user's current projects and areas of interest when acquiring them. For example, the domain management unit can prioritize acquiring domains related to the user's current ongoing projects. The domain management unit can also filter highly relevant domains based on the user's areas of interest. The domain management unit can also suggest the most suitable domains according to the progress of the user's projects. This allows the user to acquire the most suitable domains based on their projects and areas of interest. Some or all of the above processing in the domain management unit may be performed using AI, for example, or not using AI. For example, the domain management unit can input the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0097] The domain management unit can estimate the user's emotions and determine the priority of domains to retrieve based on the estimated emotions. For example, if the user is stressed, the domain management unit may postpone less important domains. If the user is relaxed, the domain management unit may also prioritize retrieving high-importance domains. If the user is in a hurry, the domain management unit may immediately retrieve the most important domains. This allows for the determination of the optimal domain priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the domain management unit may be performed using AI or not. For example, the domain management unit can input user emotion data into a generative AI and have the generative AI determine the domain priority.

[0098] The domain management unit can prioritize acquiring domains that are highly relevant to the user, taking into account the user's geographical location information. For example, the domain management unit can prioritize acquiring domains related to the user's location. The domain management unit can also prioritize acquiring domains related to the user's business area. The domain management unit can also prioritize acquiring domains related to the user's target market. This allows the system to acquire the most suitable domain based on the user's geographical location information. Some or all of the above processing in the domain management unit may be performed using AI, for example, or without AI. For example, the domain management unit can input the user's geographical location information into a generating AI and have the generating AI acquire highly relevant domains.

[0099] The domain management unit can analyze a user's social media activity and acquire relevant domains when acquiring a domain. For example, the domain management unit can suggest relevant domains based on the user's social media activity. The domain management unit can also acquire relevant domains based on the user's followers' areas of interest. The domain management unit can also acquire relevant domains based on the user's social media trends. This allows the system to acquire the most suitable domains based on the user's social media activity. Some or all of the above processes in the domain management unit may be performed using AI, for example, or without AI. For example, the domain management unit can input the user's social media activity into a generating AI and have the generating AI acquire relevant domains.

[0100] The server management unit can estimate the user's emotions and adjust the timing of server deployment based on the estimated emotions. For example, if the user is stressed, the server management unit can deploy a server during a time when the user can relax. If the user is in a hurry, the server management unit can also deploy a server immediately. If the user is relaxed, the server management unit can also deploy a server at the user's pace. This allows for server deployment at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the server management unit may be performed using AI or not. For example, the server management unit can input user emotion data into a generative AI and have the generative AI adjust the timing of server deployment.

[0101] The server management department can analyze a user's past server usage history and select the optimal arrangement method. For example, the server management department can analyze patterns of servers previously used by the user and arrange new servers using similar patterns. The server management department can also prioritize server arrangement methods that have been successful for the user in the past. The server management department can also propose the most efficient arrangement method based on the user's past server usage history. This allows for the selection of the optimal server arrangement method based on the user's past history. Some or all of the above processes in the server management department may be performed using AI, for example, or without AI. For example, the server management department can input the user's past server usage history into a generating AI and have the generating AI select the optimal arrangement method.

[0102] The server management unit can filter servers based on the user's current projects and areas of interest when arranging servers. For example, the server management unit can prioritize servers related to the user's current projects. The server management unit can also filter servers based on the user's areas of interest to ensure they are relevant. The server management unit can also suggest the most suitable server depending on the progress of the user's project. This ensures that the optimal server is arranged based on the user's projects and areas of interest. Some or all of the above processes in the server management unit may be performed using AI, for example, or not. For example, the server management unit can input the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0103] The server management unit can estimate the user's emotions and determine the priority of servers to arrange based on the estimated emotions. For example, if the user is stressed, the server management unit may postpone arranging less important servers. If the user is relaxed, the server management unit may also prioritize arranging more important servers. If the user is in a hurry, the server management unit may immediately arrange the most important servers. This allows for the determination of the optimal server priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the server management unit may be performed using AI, for example, or not using AI. For example, the server management unit can input user emotion data into a generative AI and have the generative AI determine the server priorities.

[0104] The server management department can prioritize the allocation of servers that are highly relevant to the user, taking into account the user's geographical location information when arranging servers. For example, the server management department can prioritize servers related to the user's location. The server management department can also prioritize servers related to the user's business area. The server management department can also prioritize servers related to the user's target market. This allows the server management department to allocate the optimal server based on the user's geographical location information. Some or all of the above processing in the server management department may be performed using AI, for example, or not using AI. For example, the server management department can input the user's geographical location information into a generating AI and have the generating AI arrange highly relevant servers.

[0105] The server management unit can analyze a user's social media activity and arrange relevant servers when arranging servers. For example, the server management unit can suggest relevant servers based on the user's social media activity. The server management unit can also arrange relevant servers based on the user's followers' areas of interest. The server management unit can also arrange relevant servers based on the user's social media trends. This allows for the arrangement of the optimal server based on the user's social media activity. Some or all of the above processes in the server management unit may be performed using AI, for example, or not using AI. For example, the server management unit can input the user's social media activity into a generating AI and have the generating AI arrange the relevant servers.

[0106] The CMS construction unit can estimate the user's emotions and adjust the timing of CMS environment construction based on the estimated user emotions. For example, if the user is stressed, the CMS construction unit can construct the CMS environment during a time when the user is relaxed. If the user is in a hurry, the CMS construction unit can also construct the CMS environment immediately. If the user is relaxed, the CMS construction unit can also construct the CMS environment at the user's pace. This allows the CMS environment to be constructed at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the CMS construction unit may be performed using AI, for example, or not using AI. For example, the CMS construction unit can input user emotion data into a generative AI and have the generative AI adjust the timing of CMS environment construction.

[0107] The CMS construction unit can analyze the user's past CMS usage history and select the optimal construction method. For example, the CMS construction unit can analyze the patterns of CMSs the user has used in the past and build a new CMS environment using similar patterns. The CMS construction unit can also prioritize selecting CMS construction methods that have been successful for the user in the past. The CMS construction unit can also propose the most efficient construction method based on the user's past CMS usage history. This allows for the selection of the optimal CMS construction method based on the user's past history. Some or all of the above processes in the CMS construction unit may be performed using AI, for example, or not using AI. For example, the CMS construction unit can input the user's past CMS usage history into a generating AI and have the generating AI select the optimal construction method.

[0108] The CMS construction unit can filter CMS environments based on the user's current projects and areas of interest. For example, the CMS construction unit can prioritize building CMS environments related to the user's current projects. The CMS construction unit can also filter highly relevant CMS environments based on the user's areas of interest. The CMS construction unit can also suggest the optimal CMS environment according to the progress of the user's projects. This allows for the construction of an optimal CMS environment based on the user's projects and areas of interest. Some or all of the above processing in the CMS construction unit may be performed using AI, for example, or without AI. For example, the CMS construction unit can input the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0109] The CMS construction unit can estimate the user's emotions and determine the priority of the CMS environment to be built based on the estimated user emotions. For example, if the user is stressed, the CMS construction unit may postpone the construction of less important CMS environments. If the user is relaxed, the CMS construction unit may also prioritize the construction of high-priority CMS environments. If the user is in a hurry, the CMS construction unit may immediately construct the most important CMS environment. This allows for the determination of the optimal priority of CMS environments according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the CMS construction unit may be performed using AI or not using AI. For example, the CMS construction unit can input user emotion data into a generative AI and have the generative AI perform the determination of CMS environment priorities.

[0110] The CMS construction unit can prioritize the construction of highly relevant CMS environments by considering the user's geographical location information when building a CMS environment. For example, the CMS construction unit can prioritize the construction of CMS environments related to the user's location. The CMS construction unit can also prioritize the construction of CMS environments related to the user's business area. The CMS construction unit can also prioritize the construction of CMS environments related to the user's target market. This allows for the construction of an optimal CMS environment based on the user's geographical location information. Some or all of the above processing in the CMS construction unit may be performed using AI, for example, or without AI. For example, the CMS construction unit can input the user's geographical location information into a generating AI and have the generating AI execute the construction of highly relevant CMS environments.

[0111] The CMS construction unit can analyze a user's social media activity and construct a relevant CMS environment when building a CMS environment. For example, the CMS construction unit can suggest a relevant CMS environment based on the user's social media activity. The CMS construction unit can also construct a relevant CMS environment based on the user's followers' areas of interest. The CMS construction unit can also construct a relevant CMS environment based on the user's social media trends. This allows for the construction of an optimal CMS environment based on the user's social media activity. Some or all of the above processing in the CMS construction unit may be performed using AI, for example, or without AI. For example, the CMS construction unit can input the user's social media activity into a generating AI and have the generating AI execute the construction of the relevant CMS environment.

[0112] The design generation unit can estimate the user's emotions and adjust the design's expression based on those emotions. For example, if the user is relaxed, the design generation unit can generate a design with soft colors. If the user is excited, the design generation unit can also generate a design with vibrant colors. If the user is stressed, the design generation unit can also generate a design with calm colors. This allows for the optimal design expression to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the design generation unit may be performed using AI, or not. For example, the design generation unit can input user emotion data into the generative AI and have the generative AI adjust the design's expression.

[0113] The design generation unit can adjust the level of detail in the design based on the importance of the site during the design generation process. For example, the design generation unit can generate a detailed and professional design for an important business site. For a personal blog, it can generate a simple and user-friendly design. For an event site, it can generate a visually appealing and interactive design. This allows for the optimal level of detail in the design to be adjusted according to the importance of the site. Some or all of the above processes in the design generation unit may be performed using AI, for example, or not. For example, the design generation unit can input the importance of the site into the generation AI and have the generation AI perform the adjustment of the level of detail in the design.

[0114] The design generation unit can apply different design algorithms depending on the site category during design generation. For example, in the case of an e-commerce site, the design generation unit can apply a design algorithm specialized for product display. In the case of a news site, the design generation unit can also apply a design algorithm that prioritizes the visibility of information. In the case of a portfolio site, the design generation unit can also apply a design algorithm that enhances the appeal of the works. This allows for the application of the optimal design algorithm according to the site category. Some or all of the above-described processes in the design generation unit may be performed using AI, for example, or without AI. For example, the design generation unit can input the site category into a generation AI and have the generation AI execute the application of the design algorithm.

[0115] The design generation unit can estimate the user's emotions and adjust the length of the design based on the estimated emotions. For example, if the user is in a hurry, the design generation unit can generate a short, concise design. If the user is relaxed, the design generation unit can also generate a longer design that includes detailed explanations. If the user is excited, the design generation unit can also generate a design with visually stimulating effects. This allows for the optimal design length to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the design generation unit may be performed using AI or not. For example, the design generation unit can input user emotion data into the generative AI and have the generative AI adjust the length of the design.

[0116] The design generation unit can determine design priorities based on the site's submission deadline during design generation. For example, the design generation unit will prioritize generating designs for sites with approaching deadlines. For sites with ample time before submission, the design generation unit can prioritize designs for other sites. If the submission deadline is unknown, the design generation unit can also determine priorities based on the importance of the site. This allows for the determination of the optimal design priority according to the site's submission deadline. Some or all of the above processes in the design generation unit may be performed using AI, for example, or not. For example, the design generation unit can input the site's submission deadline into a generation AI and have the generation AI determine the design priorities.

[0117] The design generation unit can adjust the order of designs based on the relevance of the sites during the design generation process. For example, the design generation unit can prioritize designing highly relevant sites. It can also postpone designing less relevant sites. If the relevance is unknown, the design generation unit can determine the order based on the importance of the sites. This allows for the optimal design order to be adjusted according to the relevance of the sites. Some or all of the above processes in the design generation unit may be performed using AI, for example, or not using AI. For example, the design generation unit can input the relevance of the sites into a generation AI and have the generation AI perform the adjustment of the design order.

[0118] The SEO settings unit can estimate the user's emotions and adjust the SEO settings based on those emotions. For example, if the user is relaxed, the SEO settings unit can suggest detailed SEO settings. If the user is in a hurry, the SEO settings unit can suggest simplified SEO settings. If the user is stressed, the SEO settings unit can suggest only the most important SEO settings. This allows the SEO settings to be adjusted to the optimal level according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the SEO settings unit may be performed using AI or not. For example, the SEO settings unit can input user emotion data into a generative AI and have the generative AI adjust the SEO settings.

[0119] The SEO settings section can adjust the level of detail in SEO settings based on the importance of the site during the SEO setup process. For example, the SEO settings section can perform detailed and professional SEO settings for important business sites. For personal blogs, it can perform basic SEO settings. For event sites, it can perform SEO settings tailored to specific events. This allows for the adjustment of the optimal level of detail in SEO settings according to the importance of the site. Some or all of the above processes in the SEO settings section may be performed using AI, for example, or not. For example, the SEO settings section can input the importance of the site into a generating AI and have the generating AI perform the adjustment of the level of detail in the SEO settings.

[0120] The SEO settings unit can apply different SEO algorithms depending on the site's category during SEO configuration. For example, for an e-commerce site, the SEO settings unit can apply an SEO algorithm specifically tailored to product pages. For a news site, the SEO settings unit can also apply an SEO algorithm that prioritizes article readability. For a portfolio site, the SEO settings unit can also apply an SEO algorithm that highlights the appeal of the works. This allows for the application of the optimal SEO algorithm for each site category. Some or all of the above-described processes in the SEO settings unit may be performed using AI, for example, or without AI. For example, the SEO settings unit can input the site's category into a generating AI and have the generating AI execute the application of the SEO algorithm.

[0121] The SEO settings unit can estimate the user's emotions and determine the priority of SEO settings based on those emotions. For example, if the user is stressed, the SEO settings unit may postpone less important SEO settings. If the user is relaxed, the SEO settings unit may prioritize more important SEO settings. If the user is in a hurry, the SEO settings unit may immediately perform the most important SEO settings. This allows for the determination of the optimal priority of SEO settings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the SEO settings unit may be performed using AI or not. For example, the SEO settings unit can input user emotion data into a generative AI and have the generative AI determine the priority of SEO settings.

[0122] The SEO settings unit can determine the priority of SEO settings based on the site's submission date when configuring SEO. For example, the SEO settings unit will prioritize SEO settings for sites with approaching deadlines. For sites with ample time before submission, the SEO settings unit can prioritize SEO settings for other sites. If the submission date is unknown, the SEO settings unit can determine priority based on the importance of the site. This allows for the determination of the optimal SEO setting priority according to the site's submission date. Some or all of the above processes in the SEO settings unit may be performed using AI, for example, or not. For example, the SEO settings unit can input the site's submission date into a generating AI and have the generating AI determine the priority of the SEO settings.

[0123] The SEO settings unit can adjust the order of SEO settings based on the relevance of the sites during the SEO setup process. For example, the SEO settings unit can prioritize highly relevant sites. It can also postpone less relevant sites. If relevance is unknown, the SEO settings unit can determine the order based on the importance of the sites. This allows for the optimal adjustment of the SEO settings order according to the relevance of the sites. Some or all of the above processes in the SEO settings unit may be performed using AI, for example, or not. For example, the SEO settings unit can input the relevance of the sites into a generating AI and have the generating AI perform the adjustment of the SEO settings order.

[0124] The no-code tool provider can estimate the user's emotions and adjust how the no-code tool is delivered based on the estimated emotions. For example, if the user is stressed, the no-code tool provider can provide a simple interface. If the user is relaxed, the no-code tool provider can also provide detailed customization options. If the user is in a hurry, the no-code tool provider can also provide a fast-operating interface. This allows the provider to adjust the optimal way the no-code tool is delivered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the no-code tool provider may be performed using AI or not. For example, the no-code tool provider can input user emotion data into a generative AI and have the generative AI adjust how the no-code tool is delivered.

[0125] The no-code tool provider can analyze the user's past tool usage history to select the optimal delivery method when providing no-code tools. For example, the no-code tool provider can analyze patterns of tools the user has used in the past and provide new tools using similar patterns. The no-code tool provider can also prioritize selecting tool usage methods that the user has successfully used in the past. The no-code tool provider can also propose the most efficient delivery method based on the user's past tool usage history. This allows for the selection of the optimal no-code tool delivery method based on the user's past history. Some or all of the above processing in the no-code tool provider may be performed using AI, for example, or without AI. For example, the no-code tool provider can input the user's past tool usage history into a generating AI and have the generating AI select the optimal delivery method.

[0126] The no-code tool provider can filter no-code tools based on the user's current projects and areas of interest when providing them. For example, the no-code tool provider can prioritize providing tools related to the user's current ongoing projects. The no-code tool provider can also filter highly relevant tools based on the user's areas of interest. The no-code tool provider can also suggest the most suitable tools according to the progress of the user's projects. This allows the provider to offer the most suitable no-code tools based on the user's projects and areas of interest. Some or all of the above processing in the no-code tool provider may be performed using AI, for example, or not using AI. For example, the no-code tool provider can input the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0127] The no-code tool provider can estimate the user's emotions and prioritize no-code tools based on those emotions. For example, if the user is stressed, the no-code tool provider may postpone providing less important tools. If the user is relaxed, the no-code tool provider may prioritize providing more important tools. If the user is in a hurry, the no-code tool provider may immediately provide the most important tools. This allows for the determination of the optimal priority of no-code tools according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the no-code tool provider may be performed using AI or not. For example, the no-code tool provider can input user emotion data into a generative AI and have the generative AI determine the priority of no-code tools.

[0128] The no-code tool provider can prioritize providing highly relevant tools by considering the user's geographical location when providing no-code tools. For example, the no-code tool provider can prioritize providing tools related to the user's location. The no-code tool provider can also prioritize providing tools related to the user's business area. The no-code tool provider can also prioritize providing tools related to the user's target market. This allows the provider to offer the most suitable no-code tool based on the user's geographical location. Some or all of the above processing in the no-code tool provider may be performed using AI, for example, or without AI. For example, the no-code tool provider can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing highly relevant tools.

[0129] The no-code tool provider can analyze the user's social media activity and provide relevant tools when providing no-code tools. For example, the no-code tool provider can suggest relevant tools based on the user's social media activity. The no-code tool provider can also provide relevant tools based on the user's followers' areas of interest. The no-code tool provider can also provide relevant tools based on the user's social media trends. This allows the provider to offer the most suitable no-code tool based on the user's social media activity. Some or all of the above processing in the no-code tool provider may be performed using AI, for example, or without AI. For example, the no-code tool provider can input the user's social media activity into a generating AI and have the generating AI perform the provision of relevant tools.

[0130] The page content generation unit can estimate the user's emotions and adjust the page content generation method based on the estimated emotions. For example, if the user is relaxed, the page content generation unit can generate detailed content. If the user is in a hurry, the page content generation unit can also generate concise content. If the user is stressed, the page content generation unit can also generate visually appealing content. This allows the optimal page content generation method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the page content generation unit may be performed using AI, for example, or not using AI. For example, the page content generation unit can input user emotion data into the generative AI and have the generative AI adjust the page content generation method.

[0131] The page content generation unit can adjust the level of detail of content based on the importance of the site when generating page content. For example, for an important business site, the page content generation unit can generate detailed and professional content. For a personal blog, the page content generation unit can also generate simple and approachable content. For an event site, the page content generation unit can also generate visually appealing and interactive content. This allows for the optimal level of detail of content to be adjusted according to the importance of the site. Some or all of the above processing in the page content generation unit may be performed using AI, for example, or not. For example, the page content generation unit can input the importance of the site into the generation AI and have the generation AI perform the adjustment of the level of detail of the content.

[0132] The page content generation unit can apply different content generation algorithms depending on the site category when generating page content. For example, in the case of an e-commerce site, the page content generation unit can apply a content generation algorithm specialized for product descriptions. In the case of a news site, the page content generation unit can also apply a content generation algorithm that prioritizes the readability of articles. In the case of a portfolio site, the page content generation unit can also apply a content generation algorithm that highlights the appeal of the works. This allows for the application of the optimal content generation algorithm according to the site category. Some or all of the above-described processes in the page content generation unit may be performed using AI, for example, or without AI. For example, the page content generation unit can input the site category into a generation AI and have the generation AI execute the application of the content generation algorithm.

[0133] The page content generation unit can estimate the user's emotions and determine the priority of page content based on the estimated emotions. For example, if the user is stressed, the page content generation unit will postpone the generation of less important content. If the user is relaxed, the page content generation unit can also prioritize the generation of more important content. If the user is in a hurry, the page content generation unit can immediately generate the most important content. This allows for the determination of the optimal priority of page content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the page content generation unit may be performed using AI, or not using AI. For example, the page content generation unit can input user emotion data into a generative AI and have the generative AI determine the priority of page content.

[0134] The page content generation unit can determine content priority based on the submission deadline of each site when generating page content. For example, the page content generation unit will prioritize content for sites with approaching deadlines. If a site has ample time before its submission deadline, the page content generation unit can prioritize content from other sites. If the submission deadline is unknown, the page content generation unit can also determine priority based on the importance of each site. This allows for the determination of the optimal content priority according to the submission deadline of each site. Some or all of the above processes in the page content generation unit may be performed using AI, for example, or not. For example, the page content generation unit can input the submission deadline of each site into a generation AI and have the generation AI determine the content priority.

[0135] The page content generation unit can adjust the order of content based on the relevance of the sites when generating page content. For example, the page content generation unit can prioritize generating content for highly relevant sites. The page content generation unit can also postpone generating content for less relevant sites. If the relevance is unknown, the page content generation unit can also determine the order based on the importance of the sites. This allows for the optimal content order to be adjusted according to the relevance of the sites. Some or all of the above processing in the page content generation unit may be performed using AI, for example, or without AI. For example, the page content generation unit can input the relevance of the sites into a generation AI and have the generation AI perform the adjustment of the content order.

[0136] The tag generation unit can estimate the user's emotions and adjust the tag generation method based on the estimated emotions. For example, if the user is relaxed, the tag generation unit can generate detailed tags. If the user is in a hurry, the tag generation unit can also generate concise tags. If the user is stressed, the tag generation unit can also generate visually appealing tags. This allows the optimal tag generation method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tag generation unit may be performed using AI, for example, or not using AI. For example, the tag generation unit can input user emotion data into the generative AI and have the generative AI adjust the tag generation method.

[0137] The tag generation unit can adjust the level of detail of tags based on the importance of the site during tag generation. For example, the tag generation unit can generate detailed and professional tags for important business sites. For personal blogs, it can generate simple and user-friendly tags. For event sites, it can generate visually appealing and interactive tags. This allows for optimal adjustment of tag detail according to the importance of the site. Some or all of the above processes in the tag generation unit may be performed using AI, for example, or not. For example, the tag generation unit can input the importance of the site into the generation AI and have the generation AI perform the adjustment of tag detail.

[0138] The tag generation unit can apply different tag generation algorithms depending on the site category when generating tags. For example, in the case of an e-commerce site, the tag generation unit can apply a tag generation algorithm specialized for product descriptions. In the case of a news site, the tag generation unit can also apply a tag generation algorithm that prioritizes the readability of articles. In the case of a portfolio site, the tag generation unit can also apply a tag generation algorithm that highlights the appeal of the works. This allows the application of the optimal tag generation algorithm according to the site category. Some or all of the above processing in the tag generation unit may be performed using AI, for example, or without AI. For example, the tag generation unit can input the site category into a generation AI and have the generation AI execute the application of the tag generation algorithm.

[0139] The tag generation unit can estimate the user's emotions and determine tag priorities based on the estimated emotions. For example, if the user is stressed, the tag generation unit may postpone the generation of less important tags. If the user is relaxed, the tag generation unit may also prioritize the generation of more important tags. If the user is in a hurry, the tag generation unit may immediately generate the most important tags. This allows for the determination of optimal tag priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the tag generation unit may be performed using AI, or not using AI. For example, the tag generation unit can input user emotion data into a generative AI and have the generative AI determine tag priorities.

[0140] The tag generation unit can determine the priority of tags based on the submission deadline of the site during tag generation. For example, the tag generation unit will generate tags with the highest priority for sites with approaching deadlines. If a site has ample time before its submission deadline, the tag generation unit can also prioritize tags from other sites. If the submission deadline is unknown, the tag generation unit can also determine the priority based on the importance of the site. This allows for the determination of the optimal tag priority according to the submission deadline of the site. Some or all of the above processes in the tag generation unit may be performed using AI, for example, or without AI. For example, the tag generation unit can input the submission deadline of the site into a generation AI and have the generation AI perform the determination of tag priority.

[0141] The tag generation unit can adjust the order of tags based on the relevance of the sites during tag generation. For example, the tag generation unit can prioritize tag generation for highly relevant sites. The tag generation unit can also postpone tag generation for less relevant sites. If relevance is unknown, the tag generation unit can determine the order based on the importance of the sites. This allows for the optimal order of tags to be adjusted according to the relevance of the sites. Some or all of the above processes in the tag generation unit may be performed using AI, for example, or without AI. For example, the tag generation unit can input the relevance of the sites into a generation AI and have the generation AI perform the adjustment of the tag order.

[0142] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0143] The website building system may further include a design adjustment unit that estimates the user's emotions and adjusts the website design based on the estimated emotions. For example, if the user is relaxed, the design adjustment unit may generate a design with soft colors. If the user is excited, it may generate a design with vibrant colors. If the user is stressed, it may generate a design with calm colors. This allows the system to provide the optimal design according to the user's emotions. Emotion estimation is achieved using an emotion engine or a generative AI. Some or all of the above-described processes in the design adjustment unit may be performed using AI or not. For example, the design adjustment unit may input user emotion data into a generative AI and have the generative AI perform the design adjustments.

[0144] The website building system may further include a history analysis unit that analyzes the user's past website building history and proposes the optimal building method. The history analysis unit, for example, analyzes patterns in websites the user has built in the past and builds a new website using similar patterns. It can also prioritize suggesting building methods that have been successful for the user in the past. It can also suggest the most efficient building method based on the user's past history. This allows for the selection of the optimal website building method based on the user's past history. Some or all of the above processing in the history analysis unit may be performed using AI or not. For example, the history analysis unit can input the user's past website building history into a generating AI and have the generating AI propose the optimal building method.

[0145] The website building system may further include an SEO adjustment unit that estimates the user's emotions and adjusts the SEO settings based on those emotions. For example, if the user is relaxed, the SEO adjustment unit may suggest detailed SEO settings. If the user is in a hurry, it may suggest simplified SEO settings. If the user is stressed, it may suggest only the most important SEO settings. This allows the system to adjust the optimal SEO settings according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above-described processes in the SEO adjustment unit may be performed using AI or not. For example, the SEO adjustment unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the SEO settings.

[0146] The website building system may further include a domain filtering unit that prioritizes the acquisition of highly relevant domains, taking into account the user's geographical location information. For example, the domain filtering unit may prioritize the acquisition of domains related to the user's location. It may also prioritize the acquisition of domains related to the user's business area. It may also prioritize the acquisition of domains related to the user's target market. This allows the system to acquire the most suitable domain based on the user's geographical location information. Some or all of the above processing in the domain filtering unit may be performed using AI or not. For example, the domain filtering unit may input the user's geographical location information into a generating AI and have the generating AI acquire highly relevant domains.

[0147] The website building system may further include a tool adjustment unit that estimates the user's emotions and adjusts how the no-code tools are provided based on the estimated emotions. For example, if the user is stressed, the tool adjustment unit may provide a simple interface. If the user is relaxed, it may also provide detailed customization options. If the user is in a hurry, it may also provide a quick-operation interface. This allows the system to adjust the way the no-code tools are provided to best suit the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Some or all of the processing described above in the tool adjustment unit may be performed using AI or not. For example, the tool adjustment unit may input user emotion data into a generative AI and have the generative AI perform the adjustment of how the no-code tools are provided.

[0148] The website building system may further include a social media analysis unit that analyzes the user's social media activity and retrieves relevant domains. The social media analysis unit may, for example, suggest relevant domains based on the user's social media activity. It may also retrieve relevant domains based on the user's followers' areas of interest. It may also retrieve relevant domains based on the user's social media trends. This allows for the acquisition of the most suitable domains based on the user's social media activity. Some or all of the above-described processes in the social media analysis unit may be performed using AI or not. For example, the social media analysis unit may input the user's social media activity into a generating AI and have the generating AI retrieve relevant domains.

[0149] The website building system may further include a content adjustment unit that estimates the user's emotions and adjusts the method of generating page content based on the estimated emotions. For example, the content adjustment unit may generate detailed content when the user is relaxed, concise content when the user is in a hurry, or visually appealing content when the user is stressed. This allows the system to adjust the method of generating page content to be optimal according to the user's emotions. Emotion estimation is achieved using an emotion engine or a generative AI. Some or all of the above-described processes in the content adjustment unit may be performed using AI or not. For example, the content adjustment unit may input user emotion data into a generative AI and have the generative AI perform the adjustment of the page content generation method.

[0150] The website building system may further include a project filtering unit that filters based on the user's current projects and areas of interest. The project filtering unit, for example, prioritizes obtaining domains related to the user's current projects. It can also filter highly relevant domains based on the user's areas of interest. It can even suggest the most suitable domains according to the user's project progress. This allows the system to obtain the most suitable domains based on the user's projects and areas of interest. Some or all of the above processing in the project filtering unit may be performed using AI or not. For example, the project filtering unit can input the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0151] The website building system may further include a tag adjustment unit that estimates the user's emotions and adjusts the tag generation method based on the estimated emotions. For example, the tag adjustment unit may generate detailed tags when the user is relaxed, concise tags when the user is in a hurry, or visually appealing tags when the user is stressed. This allows the system to adjust the optimal tag generation method according to the user's emotions. Emotion estimation is achieved using an emotion engine or a generative AI. Some or all of the above-described processes in the tag adjustment unit may be performed using AI or not. For example, the tag adjustment unit may input user emotion data into a generative AI and have the generative AI adjust the tag generation method.

[0152] The website building system may further include a tool history analysis unit that analyzes the user's past tool usage history to select the optimal delivery method. For example, the tool history analysis unit can analyze patterns of tools the user has used in the past and provide new tools using similar patterns. It can also prioritize selecting tool usage methods that have been successful for the user in the past. Furthermore, it can propose the most efficient delivery method based on the user's past tool usage history. This allows for the selection of the optimal delivery method for no-code tools based on the user's past history. Some or all of the above-described processes in the tool history analysis unit may be performed using AI or not. For example, the tool history analysis unit can input the user's past tool usage history into a generating AI and have the generating AI select the optimal delivery method.

[0153] The following briefly describes the processing flow for example form 2.

[0154] Step 1: The Domain Management Department acquires and applies the domain. For example, it automatically acquires and applies the domain selected by the user. The Domain Management Department acquires and applies the domain based on the domain registrar's selection criteria and application procedures. Step 2: The Server Management Department arranges servers based on the domains acquired by the Domain Management Department. For example, to arrange the most suitable servers, they select servers based on criteria such as performance, cost, and reliability. The Server Management Department arranges servers according to the server arrangement procedure. Step 3: The CMS Construction Department builds the CMS environment on the server arranged by the Server Management Department. For example, they build the CMS environment based on criteria such as the type of CMS to be used, installation procedure, and configuration method. The CMS Construction Department builds the CMS environment according to the CMS installation procedure. Step 4: The design generation unit generates the site design based on the CMS environment built by the CMS construction unit. For example, it generates the design based on criteria such as the design tools to be used and the selection criteria for design templates. The design generation unit can generate the design using a generation AI. Step 5: The SEO settings section configures SEO settings based on the design generated by the design generation section. For example, it configures SEO settings based on criteria such as the SEO tools to be used, setting items, and evaluation criteria. The SEO settings section can configure SEO settings using generation AI.

[0155] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0156] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0157] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the domain management unit, server management unit, CMS construction unit, design generation unit, SEO setting unit, no-code tool provision unit, page content generation unit, and tag generation unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the domain management unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The server management unit is implemented by the specific processing unit 290 of the data processing unit 12. The CMS construction unit is implemented by the specific processing unit 290 of the data processing unit 12. The design generation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The SEO setting unit is implemented by the specific processing unit 290 of the data processing unit 12. The no-code tool provision unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The page content generation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The tag generation unit is implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0160] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0167] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] Each of the multiple elements described above, including the domain management unit, server management unit, CMS construction unit, design generation unit, SEO setting unit, no-code tool provision unit, page content generation unit, and tag generation unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the domain management unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The server management unit is implemented by the specific processing unit 290 of the data processing unit 12. The CMS construction unit is implemented by the specific processing unit 290 of the data processing unit 12. The design generation unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The SEO setting unit is implemented by the specific processing unit 290 of the data processing unit 12. The no-code tool provision unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The page content generation unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The tag generation unit is implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0175] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0176] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0177] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0178] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0179] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0180] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0181] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0182] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0183] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0184] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0185] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0186] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0187] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0188] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0189] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0190] Each of the multiple elements described above, including the domain management unit, server management unit, CMS construction unit, design generation unit, SEO setting unit, no-code tool provision unit, page content generation unit, and tag generation unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the domain management unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The server management unit is implemented by the specific processing unit 290 of the data processing unit 12. The CMS construction unit is implemented by the specific processing unit 290 of the data processing unit 12. The design generation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The SEO setting unit is implemented by the specific processing unit 290 of the data processing unit 12. The no-code tool provision unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The page content generation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The tag generation unit is implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0191] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0192] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0193] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0194] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0195] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0196] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0197] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0198] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0199] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0200] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0201] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0202] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0203] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0204] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0205] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0206] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0207] Each of the multiple elements described above, including the domain management unit, server management unit, CMS construction unit, design generation unit, SEO setting unit, no-code tool provision unit, page content generation unit, and tag generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the domain management unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The server management unit is implemented by the specific processing unit 290 of the data processing unit 12. The CMS construction unit is implemented by the specific processing unit 290 of the data processing unit 12. The design generation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The SEO setting unit is implemented by the specific processing unit 290 of the data processing unit 12. The no-code tool provision unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The page content generation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The tag generation unit is implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0208] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0209] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0210] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0211] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0212] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0213] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0214] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0215] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0216] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0217] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0218] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0219] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0220] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0221] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0222] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0223] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0224] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0225] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0226] (Note 1) The Domain Management Department, which handles domain acquisition and application, A server management unit arranges servers based on the domains acquired by the aforementioned domain management unit, The CMS Construction Department constructs a CMS environment on the server arranged by the Server Management Department, A design generation unit that generates a site design based on the CMS environment built by the CMS construction unit, The system includes an SEO setting unit that performs SEO settings based on the design generated by the design generation unit. A system characterized by the following features. (Note 2) It includes a no-code tool provider section that provides no-code tools. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a page content generation unit that generates page content. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a tag generation unit that generates appropriate tags. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned domain management unit, The system automatically retrieves and applies the domain selected by the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned server management unit, Based on the domain acquired by the aforementioned domain management department, the most suitable server is arranged. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned CMS construction unit is The CMS environment will be built on the server arranged by the aforementioned server management department. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned domain management unit, It estimates user sentiment and adjusts the timing of domain acquisition based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned domain management unit, Analyze the user's past domain acquisition history and select the optimal acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned domain management unit, When acquiring a domain, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned domain management unit, It estimates the user's sentiment and determines the priority of domains to retrieve based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned domain management unit, When acquiring a domain, the system prioritizes acquiring domains that are highly relevant to the user's geographical location, taking their location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned domain management unit, When acquiring a domain, the system analyzes the user's social media activity and acquires relevant domains. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned server management unit, It estimates the user's emotions and adjusts the timing of server deployment based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned server management unit, Analyze the user's past server usage history and select the optimal arrangement method. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned server management unit, When arranging servers, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned server management unit, It estimates the user's emotions and determines the priority of servers to arrange based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned server management unit, When arranging servers, the system prioritizes arranging the most relevant servers by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned server management unit, When arranging servers, we analyze the user's social media activity and arrange the relevant servers. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned CMS construction unit is It estimates user sentiment and adjusts the timing of CMS environment construction based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned CMS construction unit is We analyze users' past CMS usage history and select the optimal construction method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned CMS construction unit is When building a CMS environment, filter based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned CMS construction unit is Estimate user sentiment and prioritize the CMS environment to build based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned CMS construction unit is When building a CMS environment, prioritize building a highly relevant CMS environment by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned CMS construction unit is When building a CMS environment, analyze users' social media activity and build the relevant CMS environment accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned design generation unit, It estimates the user's emotions and adjusts the design's presentation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned design generation unit, During design generation, adjust the level of detail in the design based on the importance of the site. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned design generation unit, When generating designs, different design algorithms are applied depending on the site category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned design generation unit, It estimates the user's emotions and adjusts the design length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned design generation unit, When generating designs, prioritize designs based on the site submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned design generation unit, During design generation, the order of designs is adjusted based on the relevance of the site. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned SEO settings section is, It estimates user sentiment and adjusts SEO settings based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned SEO settings section is, When configuring SEO settings, adjust the level of detail of the SEO settings based on the importance of the site. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned SEO settings section is, When configuring SEO settings, apply different SEO algorithms depending on the site's category. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned SEO settings section is, It estimates user sentiment and determines the priority of SEO settings based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned SEO settings section is, When configuring SEO settings, prioritize SEO settings based on when the site was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned SEO settings section is, When configuring SEO settings, adjust the order of SEO settings based on the relevance of the site. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned no-code tool provider unit It estimates user sentiment and adjusts how the no-code tool is delivered based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned no-code tool provider unit When providing the no-code tool, analyze the user's past tool usage history to select the optimal provision method The system according to appended note 2, characterized by this (Appended note 40) The no-code tool provision unit When providing the no-code tool, perform filtering based on the user's current project and area of interest The system according to appended note 2, characterized by this (Appended note 41) The no-code tool provision unit Estimate the user's sentiment and determine the priority order of the no-code tool based on the estimated user sentiment The system according to appended note 2, characterized by this (Appended note 42) The no-code tool provision unit When providing the no-code tool, preferentially provide highly relevant tools considering the user's geographical location information The system according to appended note 2, characterized by this (Appended note 43) The no-code tool provision unit When providing the no-code tool, analyze the user's social media activities and provide relevant tools The system according to appended note 2, characterized by this (Appended note 44) The page content generation unit Estimate the user's sentiment and adjust the page content generation method based on the estimated user sentiment The system according to appended note 3, characterized by this (Appended note 45) The page content generation unit When generating page content, adjust the content detail level based on the importance of the site The system according to appended note 3, characterized by this (Appended note 46) The page content generation unit When generating page content, different content generation algorithms are applied depending on the site category. The system described in Appendix 3, characterized by the features described herein. (Note 47) The aforementioned page content generation unit, It estimates user sentiment and prioritizes page content based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 48) The aforementioned page content generation unit, When generating page content, prioritize content based on the site's submission date. The system described in Appendix 3, characterized by the features described herein. (Note 49) The aforementioned page content generation unit, When generating page content, adjust the order of content based on site relevance. The system described in Appendix 3, characterized by the features described herein. (Note 50) The tag generation unit, It estimates the user's emotions and adjusts how tags are generated based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 51) The tag generation unit, When generating tags, adjust the tag's specificity based on the site's importance. The system described in Appendix 4, characterized by the features described herein. (Note 52) The tag generation unit, When generating tags, different tag generation algorithms are applied depending on the site category. The system described in Appendix 4, characterized by the features described herein. (Note 53) The tag generation unit, It estimates user sentiment and determines tag priority based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 54) The tag generation unit, When generating tags, prioritize them based on when the site was submitted. The system described in Appendix 4, characterized by the features described herein. (Note 55) The tag generation unit, When generating tags, adjust the order of tags based on site relevance. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0227] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The Domain Management Department, which handles domain acquisition and application, A server management unit arranges servers based on the domains acquired by the aforementioned domain management unit, The CMS Construction Department constructs a CMS environment on the server arranged by the Server Management Department, A design generation unit that generates a site design based on the CMS environment built by the CMS construction unit, The system includes an SEO setting unit that performs SEO settings based on the design generated by the design generation unit. A system characterized by the following features.

2. It includes a no-code tool provider section that provides no-code tools. The system according to feature 1.

3. It includes a page content generation unit that generates page content. The system according to feature 1.

4. It includes a tag generation unit that generates appropriate tags. The system according to feature 1.

5. The aforementioned domain management unit, The system automatically retrieves and applies the domain selected by the user. The system according to feature 1.

6. The aforementioned server management unit, Based on the domain acquired by the aforementioned domain management department, the most suitable server is arranged. The system according to feature 1.

7. The aforementioned CMS construction unit is The CMS environment will be built on the server arranged by the aforementioned server management department. The system according to feature 1.

8. The aforementioned domain management unit, It estimates user sentiment and adjusts the timing of domain acquisition based on the estimated user sentiment. The system according to feature 1.

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