System

Abstract

An object of a system according to an embodiment is to automate update and maintenance work of a home page.SOLUTION: A system according to an embodiment includes an analysis unit, an update unit, and a maintenance work unit. The analyzing unit analyzes the contents of the home page. An updating part updates the home page based on the analyzed contents. The section for performing maintenance work performs maintenance work of the home page.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, updating and maintaining websites was often done manually, which was inefficient.

[0005] The system according to the embodiment aims to automate the updating and maintenance of homepages. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, an update unit, and a maintenance unit. The analysis unit analyzes the content of a homepage. The update unit updates the homepage based on the analyzed content. The maintenance unit performs maintenance work on the homepage. [Effects of the Invention]

[0007] The system according to the embodiment can automate updating and maintenance of a homepage. [Brief explanation of the drawings]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The homepage management system according to the embodiment of the present invention is a system in which AI performs the creation, updating, and maintenance of homepages, thereby enabling the homepage management system to efficiently perform the creation, updating, and maintenance of homepages.

[0029] A homepage management system according to an embodiment includes an analysis unit, an update unit, and a maintenance unit. The analysis unit analyzes the content of a homepage. For example, the analysis unit analyzes the text content of a homepage using text analysis technology. The analysis unit can also analyze the image content of a homepage using image analysis technology. The analysis unit can also analyze the metadata of a homepage using data mining technology. For example, the text analysis technology analyzes the meaning of text using natural language processing. The image analysis technology analyzes the content of images using an image recognition algorithm. The data mining technology extracts useful information from large amounts of data. The update unit updates the homepage based on the content analyzed by the analysis unit. For example, the update unit adds content. The update unit can also change the layout. The update unit can also implement SEO measures. For example, adding content involves adding new articles or images to the homepage. Changing the layout involves changing the design and layout of the homepage. SEO measures involve optimization to improve the ranking of the homepage in search engines. The maintenance unit performs homepage maintenance work. For example, the maintenance unit implements security measures. The maintenance unit can also optimize performance. The maintenance department can also create backups. For example, security measures fix vulnerabilities and prevent unauthorized access. Performance optimization improves the display speed of a homepage. Backups periodically save data to ensure data integrity. This allows the homepage management system according to the embodiment to efficiently create, update, and maintain homepages. For example, a company's homepage can be kept up-to-date with the latest information, ensuring security while significantly reducing the burden on users.

[0030] The analysis unit learns the user's past instruction history and can automatically suggest designs and layouts that suit the user's preferences. For example, the analysis unit uses AI to analyze the user's past instruction history and learn the user's preferred design and layout patterns. For example, it makes new suggestions based on previously selected colors, fonts, and layout placements. The analysis unit also uses AI to automatically customize design templates based on the user's past instruction history. For example, it adjusts the placement of images and text to match the user's preferred style. The analysis unit also uses AI to learn the user's past instruction history and suggest designs and layouts that suit the user's preferences in real time. For example, it presents the optimal design option each time the user enters information. This makes it possible to automatically suggest designs and layouts that suit the user's preferences.

[0031] The analysis unit analyzes the user's industry and business model and can automatically incorporate optimal content structure and SEO measures. For example, the analysis unit uses AI to analyze the user's industry and business model and automatically suggest content structure tailored to those characteristics. For example, a homepage for a B2B company could emphasize product information and case studies. The analysis unit also uses AI to automatically incorporate SEO measures specific to the user's industry. For example, specific keywords and meta tags could be optimized to improve search engine rankings. The analysis unit also uses AI to analyze the user's business model and suggest optimal content structure. For example, for an e-commerce site, it could suggest a layout that makes product reviews and purchase buttons stand out. This makes it possible to automatically incorporate optimal content structure and SEO measures tailored to the industry and business model.

[0032] The update unit can analyze the user's business calendar and perform automatic updates in line with important events and campaigns. The update unit, for example, uses AI to analyze the user's business calendar and build a system that performs automatic updates in line with important events and campaigns. For example, the announcement page is automatically updated the day before an event. The update unit also uses AI to automatically update the homepage based on the information in the business calendar. For example, a special page is automatically published on the start date of a campaign. The update unit also uses AI to analyze the business calendar and perform automatic updates in line with important events and campaigns. For example, the results report page is automatically updated after the event ends. This makes it possible to automatically update in line with important events and campaigns based on the business calendar.

[0033] The update unit can link a user's social media accounts and automatically reflect the content posted on the social media on the homepage. For example, the update unit uses AI to link a user's social media accounts and build a system in which the content posted on the social media is automatically reflected on the homepage. For example, Twitter posts are automatically displayed in the news section of the homepage. The update unit also analyzes the content of social media posts and the AI ​​automatically reflects it on the homepage. For example, Instagram image posts are automatically added to the gallery page. The update unit also links a user's social media accounts and the AI ​​automatically reflects the content posted on the homepage. For example, Facebook event information is automatically added to the homepage calendar. This allows the content posted on social media to be automatically reflected on the homepage.

[0034] The maintenance department can analyze the homepage access logs, detect abnormal access patterns, and automatically take countermeasures. For example, the maintenance department could build a system in which AI analyzes the homepage access logs in real time and detects abnormal access patterns. For example, it could detect signs of a DDoS attack and automatically take countermeasures. The maintenance department could also analyze the access logs and have AI automatically detect abnormal access patterns and take countermeasures. For example, when abnormal traffic is detected, it could block a specific IP address. The maintenance department could also periodically analyze the homepage access logs with AI, detect abnormal access patterns, and automatically take countermeasures. For example, it could issue a warning if abnormal access continues. This makes it possible to detect abnormal access patterns and automatically take countermeasures.

[0035] The maintenance department can periodically analyze the website code and automatically optimize and fix bugs. For example, the maintenance department could build a system in which AI periodically analyzes the website code and automatically optimizes and fixes bugs. For example, it could remove redundant code and improve performance. The maintenance department could also use code analysis algorithms to automatically optimize the website code using AI. For example, it could remove unnecessary libraries and improve page loading speed. The maintenance department could also periodically analyze the website code using AI and automatically fix bugs. For example, it could detect security holes and fix them automatically. This allows the website code to be periodically analyzed and automatically optimized and fix bugs.

[0036] The maintenance department can collect security information from other websites and automatically implement countermeasures against the latest threats. For example, the maintenance department could build a system where AI automatically collects security information from other websites and automatically implements countermeasures against the latest threats. For example, when a new vulnerability is discovered, patches could be applied immediately. The maintenance department could also develop an algorithm for collecting security information, and AI could automatically implement countermeasures against the latest threats. For example, it could detect signs of a phishing attack and automatically implement countermeasures. The maintenance department could also use AI to analyze security information from other websites and automatically implement countermeasures against the latest threats. For example, it could automatically implement countermeasures to prevent malware infection. This allows the maintenance department to collect security information from other websites and automatically implement countermeasures against the latest threats.

[0037] The maintenance department can work in conjunction with the user's cloud storage and automatically save backup data to the cloud. For example, the maintenance department will build a system in which AI works in conjunction with the user's cloud storage and automatically saves backup data to the cloud. For example, regularly backing up homepage data to the cloud. The maintenance department will also develop an algorithm for working with cloud storage, so that AI will automatically save backup data to the cloud. For example, when data is changed, it will immediately back up to the cloud. The maintenance department will also have AI analyze the user's cloud storage and save backup data to the cloud at the optimal time. For example, backups will be performed during times of low access at night. This will allow backup data to be automatically saved to the cloud.

[0038] The analysis unit can collect trend information from other websites and social media platforms and suggest the latest designs and content based on that information. For example, the analysis unit uses AI to automatically collect trend information from other websites and social media platforms and suggest the latest designs and content based on that information. For example, it can suggest designs that incorporate trendy colors and fonts. The analysis unit also develops algorithms for collecting trend information, allowing the AI ​​to automatically suggest the latest designs and content. For example, it can incorporate popular layouts and content formats. The analysis unit also uses AI to analyze trend information on social media platforms and suggest the latest designs and content based on the results. For example, it can incorporate design elements used by influencers. This makes it possible to suggest the latest designs and content based on trend information.

[0039] The analysis unit analyzes the user's voice instructions, allowing the creation of a homepage to be completed with just voice input. The analysis unit, for example, analyzes the user's voice instructions and builds a system that allows the creation of a homepage to be completed with just voice input. For example, an instruction such as "Add a company introduction to the top page" is input by voice. The analysis unit then uses voice recognition technology to convert the user's voice instructions into text and creates a homepage based on that text. For example, an instruction such as "Use a simple design" is input by voice. The analysis unit also analyzes the user's voice instructions in real time, allowing the creation of a homepage to be completed with just voice input. For example, an instruction such as "Add a new product page" is input by voice. This allows the creation of a homepage to be completed with just voice input.

[0040] The update unit can collect update information from other websites and make update suggestions based on the trends of competitors. For example, the update unit can build a system in which AI automatically collects update information from other websites and makes update suggestions based on the trends of competitors. For example, when a competitor announces a new product, the system can suggest similar updates. The update unit can also analyze update information from other websites and the AI ​​can automatically make update suggestions based on the trends of competitors. For example, the AI ​​can suggest a similar campaign based on campaign information from competitors. The update unit can also collect update information from other websites and make update suggestions based on the trends of competitors. For example, when a competitor publishes a new blog post, the AI ​​can suggest similar content. This makes it possible to make update suggestions based on the trends of competitors.

[0041] The update unit can analyze a user's email and chat history and automatically reflect important information on the homepage. For example, the update unit could use AI to analyze a user's email and chat history and automatically reflect important information on the homepage. For example, minutes of important meetings could be automatically posted on the homepage. The update unit could also analyze email and chat history and have AI automatically reflect important information on the homepage. For example, feedback from customers could be automatically added to the reviews section of the homepage. The update unit could also analyze a user's email and chat history and have AI automatically reflect important information on the homepage. For example, project progress could be automatically updated on the homepage. This makes it possible to automatically reflect important information from email and chat history on the homepage.

[0042] The analysis unit can learn the user's operation history and automatically suggest optimal operation procedures and shortcuts. For example, the analysis unit uses AI to analyze the user's operation history and build a system that automatically suggests optimal operation procedures and shortcuts. For example, it may suggest frequently used functions as shortcuts. The analysis unit also uses AI to automatically suggest optimal operation procedures based on the operation history. For example, it may prioritize displaying functions that the user uses frequently. The analysis unit also uses AI to learn the user's operation history and suggest optimal operation procedures and shortcuts in real time. For example, it may present the optimal shortcut every time the user performs an operation. This makes it possible to automatically suggest optimal operation procedures and shortcuts based on the user's operation history.

[0043] The analysis unit can analyze the user's device and browser environment and automatically provide the optimal interface. For example, the analysis unit uses AI to analyze the user's device and browser environment and build a system that automatically provides the optimal interface. For example, it automatically provides an interface for smartphones. The analysis unit also uses AI to automatically provide the optimal interface based on the device and browser environment. For example, it automatically provides an interface for tablets. The analysis unit also uses AI to analyze the user's device and browser environment and provide the optimal interface in real time. For example, it automatically provides an interface for desktops. This makes it possible to automatically provide the optimal interface based on the user's device and browser environment.

[0044] The analysis unit can analyze a user's gestures and touch operations and provide an interface that allows intuitive operation. For example, the analysis unit uses AI to analyze a user's gestures and touch operations and build a system that provides an interface that allows intuitive operation. For example, it provides an interface that supports swipe and pinch operations. The analysis unit also uses AI to automatically provide an interface that allows intuitive operation based on the gestures and touch operations. For example, it provides an interface that supports drag and drop operations. The analysis unit also uses AI to analyze a user's gestures and touch operations and provide an interface that allows intuitive operation in real time. For example, it provides an interface that supports tap operations. This makes it possible to provide an interface that allows intuitive operation based on the user's gestures and touch operations.

[0045] The analysis unit can analyze homepage access data in detail and generate reports that identify user behavior patterns and interests. The analysis unit, for example, builds a system in which AI analyzes homepage access data in detail and generates reports that identify user behavior patterns and interests. For example, a user's interests are identified based on the time spent on a particular page and the number of clicks. The analysis unit also analyzes the access data and AI automatically generates reports that identify user behavior patterns and interests. For example, reports are created based on pages frequently visited by users and search keywords. The analysis unit also periodically analyzes homepage access data using AI and generates reports that identify user behavior patterns and interests. For example, reports are created based on the user's time of visit and the type of device. This makes it possible to generate reports that identify user behavior patterns and interests.

[0046] The analysis unit can analyze the update history of the homepage and generate a report that evaluates the effects and impact of the update. The analysis unit, for example, builds a system in which AI analyzes the update history of the homepage and generates a report that evaluates the effects and impact of the update. For example, a report is created based on changes in the number of accesses and length of stay after the update. The analysis unit also analyzes the update history and AI automatically generates a report that evaluates the effects and impact of the update. For example, a report is created based on user reactions after new content is added. The analysis unit also periodically analyzes the update history of the homepage using AI and generates a report that evaluates the effects and impact of the update. For example, a report is created based on changes in search engine rankings before and after the update. This makes it possible to generate a report that evaluates the effects and impact of the update.

[0047] The analysis unit can compare data from other websites and generate competitive analysis reports. For example, the analysis unit builds a system in which AI collects data from other websites and compares it with one's own homepage to generate competitive analysis reports. For example, a report is created based on the number of visits and duration of visits to competitors. The analysis unit also analyzes data from other websites and AI automatically generates competitive analysis reports. For example, a report is created based on competitors' SEO measures and content strategies. The analysis unit also periodically collects data from other websites and AI generates competitive analysis reports. For example, a report is created based on competitors' update history and user responses. This makes it possible to generate competitive analysis reports by comparing data from other websites.

[0048] The analysis unit can work with the user's business data to generate reports based on business results and KPIs. For example, the analysis unit builds a system in which AI works with the user's business data to generate reports based on business results and KPIs. For example, reports are created based on sales data and customer satisfaction. The analysis unit also analyzes the business data, and the AI ​​automatically generates reports based on business results and KPIs. For example, reports are created based on the effectiveness of marketing campaigns. The analysis unit also periodically analyzes the user's business data, and the AI ​​generates reports based on business results and KPIs. For example, reports are created based on monthly sales data and the status of new customer acquisition. This makes it possible to generate reports based on business results and KPIs.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The homepage management system can further include a voice analysis unit that analyzes the user's voice instructions. The voice analysis unit can, for example, update and maintain the homepage by the user issuing voice instructions. For example, by analyzing a voice instruction such as "add a new article," the update unit can automatically add a new article. The voice analysis unit can also convert the user's voice instructions into text, and the analysis unit can analyze the content of the homepage based on that text. This allows the user to manage the homepage using only voice input.

[0051] The analysis unit can further analyze the user's gestures and touch operations. For example, if the user is using a touch screen, the analysis unit can analyze the gestures and touch operations to provide an interface that allows intuitive operation. For example, an interface that supports swipe and pinch operations can be provided. Furthermore, based on the gesture analysis, it can also prioritize the display of functions that the user uses frequently. This allows the user to manage their homepage intuitively.

[0052] The update unit can also analyze the user's business calendar and perform automatic updates to coincide with important events and campaigns. For example, it can automatically update an announcement page the day before an event. It can also automatically publish a special page on the campaign start date. This makes it possible to automatically update the system to coincide with important events and campaigns based on the business calendar.

[0053] The maintenance department can further analyze the homepage's access logs, detect abnormal access patterns, and automatically take countermeasures. For example, they can detect signs of a DDoS attack and automatically take countermeasures. They can also block specific IP addresses when abnormal traffic is detected. This allows them to detect abnormal access patterns and automatically take countermeasures.

[0054] The analysis unit can further learn the user's operation history and automatically suggest optimal operation procedures and shortcuts. For example, it can suggest frequently used functions as shortcuts. It can also prioritize the display of functions that the user uses most often. This makes it possible to automatically suggest optimal operation procedures and shortcuts based on the user's operation history.

[0055] The update section can also link users' social media accounts and automatically update their social media posts on the homepage. For example, Twitter posts can be automatically displayed in the news section of the homepage. Instagram image posts can also be automatically added to the gallery page. This allows social media posts to be automatically updated on the homepage.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The analysis unit analyzes the content of the website. For example, the analysis unit may use text analysis technology to analyze the text content of the website. It may also use image analysis technology to analyze the image content of the website. It may also use data mining technology to analyze the metadata of the website. Specifically, text analysis technology uses natural language processing to analyze the meaning of text, image analysis technology uses image recognition algorithms to analyze the content of images, and data mining technology extracts useful information from large amounts of data. Step 2: The update department updates the homepage based on the content analyzed by the analysis department. For example, the update department adds content, changes the layout, and implements SEO measures. Specifically, the update department adds new articles and images to the homepage, changes the design and layout of the homepage, and optimizes it to improve its ranking in search engines. Step 3: The maintenance department performs maintenance work on the website. For example, the maintenance department implements security measures, optimizes performance, and creates backups. Specifically, they fix vulnerabilities, prevent unauthorized access, improve the display speed of the website, and periodically save data to ensure data integrity.

[0058] (Example 2) The homepage management system according to the embodiment of the present invention is a system in which AI performs the creation, updating, and maintenance of homepages, thereby enabling the homepage management system to efficiently perform the creation, updating, and maintenance of homepages.

[0059] A homepage management system according to an embodiment includes an analysis unit, an update unit, and a maintenance unit. The analysis unit analyzes the content of a homepage. For example, the analysis unit analyzes the text content of a homepage using text analysis technology. The analysis unit can also analyze the image content of a homepage using image analysis technology. The analysis unit can also analyze the metadata of a homepage using data mining technology. For example, the text analysis technology analyzes the meaning of text using natural language processing. The image analysis technology analyzes the content of images using an image recognition algorithm. The data mining technology extracts useful information from large amounts of data. The update unit updates the homepage based on the content analyzed by the analysis unit. For example, the update unit adds content. The update unit can also change the layout. The update unit can also implement SEO measures. For example, adding content involves adding new articles or images to the homepage. Changing the layout involves changing the design and layout of the homepage. SEO measures involve optimization to improve the ranking of the homepage in search engines. The maintenance unit performs homepage maintenance work. For example, the maintenance unit implements security measures. The maintenance unit can also optimize performance. The maintenance department can also create backups. For example, security measures fix vulnerabilities and prevent unauthorized access. Performance optimization improves the display speed of a homepage. Backups periodically save data to ensure data integrity. This allows the homepage management system according to the embodiment to efficiently create, update, and maintain homepages. For example, a company's homepage can be kept up-to-date with the latest information, ensuring security while significantly reducing the burden on users.

[0060] The analysis unit learns the user's past instruction history and can automatically suggest designs and layouts that suit the user's preferences. For example, the analysis unit uses AI to analyze the user's past instruction history and learn the user's preferred design and layout patterns. For example, it makes new suggestions based on previously selected colors, fonts, and layout placements. The analysis unit also uses AI to automatically customize design templates based on the user's past instruction history. For example, it adjusts the placement of images and text to match the user's preferred style. The analysis unit also uses AI to learn the user's past instruction history and suggest designs and layouts that suit the user's preferences in real time. For example, it presents the optimal design option each time the user enters information. This makes it possible to automatically suggest designs and layouts that suit the user's preferences.

[0061] The analysis unit analyzes the user's industry and business model and can automatically incorporate optimal content structure and SEO measures. For example, the analysis unit uses AI to analyze the user's industry and business model and automatically suggest content structure tailored to those characteristics. For example, a homepage for a B2B company could emphasize product information and case studies. The analysis unit also uses AI to automatically incorporate SEO measures specific to the user's industry. For example, specific keywords and meta tags could be optimized to improve search engine rankings. The analysis unit also uses AI to analyze the user's business model and suggest optimal content structure. For example, for an e-commerce site, it could suggest a layout that makes product reviews and purchase buttons stand out. This makes it possible to automatically incorporate optimal content structure and SEO measures tailored to the industry and business model.

[0062] The analysis unit can use the emotion estimation function to automatically select designs and colors according to the user's emotional state. For example, the analysis unit uses the emotion estimation function to analyze the user's emotional state in real time and automatically selects designs and colors based on the results. For example, if the user is relaxed, it selects calm colors. The analysis unit also builds a system that automatically selects designs and colors according to the user's emotional state. For example, if the user is excited, it selects vivid colors. The analysis unit also uses the emotion estimation function to suggest designs and colors based on the user's emotional state. For example, if the user is feeling stressed, it selects calm colors. In this way, it is possible to automatically select designs and colors according to the user's emotional state.

[0063] The update unit can analyze the user's business calendar and perform automatic updates in line with important events and campaigns. The update unit, for example, uses AI to analyze the user's business calendar and build a system that performs automatic updates in line with important events and campaigns. For example, the announcement page is automatically updated the day before an event. The update unit also uses AI to automatically update the homepage based on the information in the business calendar. For example, a special page is automatically published on the start date of a campaign. The update unit also uses AI to analyze the business calendar and perform automatic updates in line with important events and campaigns. For example, the results report page is automatically updated after the event ends. This makes it possible to automatically update in line with important events and campaigns based on the business calendar.

[0064] The update unit can link a user's social media accounts and automatically reflect the content posted on the social media on the homepage. For example, the update unit uses AI to link a user's social media accounts and build a system in which the content posted on the social media is automatically reflected on the homepage. For example, Twitter posts are automatically displayed in the news section of the homepage. The update unit also analyzes the content of social media posts and the AI ​​automatically reflects it on the homepage. For example, Instagram image posts are automatically added to the gallery page. The update unit also links a user's social media accounts and the AI ​​automatically reflects the content posted on the homepage. For example, Facebook event information is automatically added to the homepage calendar. This allows the content posted on social media to be automatically reflected on the homepage.

[0065] The maintenance department can analyze the homepage access logs, detect abnormal access patterns, and automatically take countermeasures. For example, the maintenance department could build a system in which AI analyzes the homepage access logs in real time and detects abnormal access patterns. For example, it could detect signs of a DDoS attack and automatically take countermeasures. The maintenance department could also analyze the access logs and have AI automatically detect abnormal access patterns and take countermeasures. For example, when abnormal traffic is detected, it could block a specific IP address. The maintenance department could also periodically analyze the homepage access logs with AI, detect abnormal access patterns, and automatically take countermeasures. For example, it could issue a warning if abnormal access continues. This makes it possible to detect abnormal access patterns and automatically take countermeasures.

[0066] The maintenance department can periodically analyze the website code and automatically optimize and fix bugs. For example, the maintenance department could build a system in which AI periodically analyzes the website code and automatically optimizes and fixes bugs. For example, it could remove redundant code and improve performance. The maintenance department could also use code analysis algorithms to automatically optimize the website code using AI. For example, it could remove unnecessary libraries and improve page loading speed. The maintenance department could also periodically analyze the website code using AI and automatically fix bugs. For example, it could detect security holes and fix them automatically. This allows the website code to be periodically analyzed and automatically optimized and fix bugs.

[0067] The maintenance department can collect security information from other websites and automatically implement countermeasures against the latest threats. For example, the maintenance department could build a system where AI automatically collects security information from other websites and automatically implements countermeasures against the latest threats. For example, when a new vulnerability is discovered, patches could be applied immediately. The maintenance department could also develop an algorithm for collecting security information, and AI could automatically implement countermeasures against the latest threats. For example, it could detect signs of a phishing attack and automatically implement countermeasures. The maintenance department could also use AI to analyze security information from other websites and automatically implement countermeasures against the latest threats. For example, it could automatically implement countermeasures to prevent malware infection. This allows the maintenance department to collect security information from other websites and automatically implement countermeasures against the latest threats.

[0068] The maintenance department can work in conjunction with the user's cloud storage and automatically save backup data to the cloud. For example, the maintenance department will build a system in which AI works in conjunction with the user's cloud storage and automatically saves backup data to the cloud. For example, regularly backing up homepage data to the cloud. The maintenance department will also develop an algorithm for working with cloud storage, so that AI will automatically save backup data to the cloud. For example, when data is changed, it will immediately back up to the cloud. The maintenance department will also have AI analyze the user's cloud storage and save backup data to the cloud at the optimal time. For example, backups will be performed during times of low access at night. This will allow backup data to be automatically saved to the cloud.

[0069] The analysis unit can collect trend information from other websites and social media platforms and suggest the latest designs and content based on that information. For example, the analysis unit uses AI to automatically collect trend information from other websites and social media platforms and suggest the latest designs and content based on that information. For example, it can suggest designs that incorporate trendy colors and fonts. The analysis unit also develops algorithms for collecting trend information, allowing the AI ​​to automatically suggest the latest designs and content. For example, it can incorporate popular layouts and content formats. The analysis unit also uses AI to analyze trend information on social media platforms and suggest the latest designs and content based on the results. For example, it can incorporate design elements used by influencers. This makes it possible to suggest the latest designs and content based on trend information.

[0070] The analysis unit analyzes the user's voice instructions, allowing the creation of a homepage to be completed with just voice input. The analysis unit, for example, analyzes the user's voice instructions and builds a system that allows the creation of a homepage to be completed with just voice input. For example, an instruction such as "Add a company introduction to the top page" is input by voice. The analysis unit then uses voice recognition technology to convert the user's voice instructions into text and creates a homepage based on that text. For example, an instruction such as "Use a simple design" is input by voice. The analysis unit also analyzes the user's voice instructions in real time, allowing the creation of a homepage to be completed with just voice input. For example, an instruction such as "Add a new product page" is input by voice. This allows the creation of a homepage to be completed with just voice input.

[0071] The analysis unit can use the emotion estimation function to analyze the emotional nuances of instructions input by the user and propose a more appropriate design or layout. For example, the analysis unit can use the emotion estimation function to analyze the emotional nuances of instructions input by the user and propose an appropriate design or layout based on the results. For example, if the user is excited, vivid colors can be selected. The analysis unit can also analyze the emotional nuances of the user's instructions and build a system that proposes a design or layout based on the results. For example, if the user is relaxed, calm colors can be selected. The analysis unit can also use the emotion estimation function to analyze the emotional nuances of the user's instructions and propose a more appropriate design or layout. For example, if the user is feeling stressed, calm colors can be selected. This makes it possible to propose a more appropriate design or layout based on the user's emotional nuances.

[0072] The update unit can collect update information from other websites and make update suggestions based on the trends of competitors. For example, the update unit can build a system in which AI automatically collects update information from other websites and makes update suggestions based on the trends of competitors. For example, when a competitor announces a new product, the system can suggest similar updates. The update unit can also analyze update information from other websites and the AI ​​can automatically make update suggestions based on the trends of competitors. For example, the AI ​​can suggest a similar campaign based on campaign information from competitors. The update unit can also collect update information from other websites and make update suggestions based on the trends of competitors. For example, when a competitor publishes a new blog post, the AI ​​can suggest similar content. This makes it possible to make update suggestions based on the trends of competitors.

[0073] The update unit can analyze a user's email and chat history and automatically reflect important information on the homepage. For example, the update unit could use AI to analyze a user's email and chat history and automatically reflect important information on the homepage. For example, minutes of important meetings could be automatically posted on the homepage. The update unit could also analyze email and chat history and have AI automatically reflect important information on the homepage. For example, feedback from customers could be automatically added to the reviews section of the homepage. The update unit could also analyze a user's email and chat history and have AI automatically reflect important information on the homepage. For example, project progress could be automatically updated on the homepage. This makes it possible to automatically reflect important information from email and chat history on the homepage.

[0074] The update unit uses the emotion estimation function to analyze the emotion of the user when inputting update content and can make update suggestions that elicit positive emotions. For example, the update unit uses the emotion estimation function to analyze the emotion of the user when inputting update content and can make update suggestions that elicit positive emotions based on the results. For example, if the user is feeling stressed, the update unit can suggest relaxing content. The update unit also builds a system that analyzes the user's emotional state and makes update suggestions that elicit positive emotions. For example, if the user is feeling down, the update unit can suggest encouraging messages. The update unit also uses the emotion estimation function to analyze the emotion of the user when inputting update content and can make update suggestions that elicit positive emotions. For example, if the user is excited, the update unit can suggest exciting news. In this way, the user's emotions can be analyzed and update suggestions that elicit positive emotions can be made.

[0075] The analysis unit can learn the user's operation history and automatically suggest optimal operation procedures and shortcuts. For example, the analysis unit uses AI to analyze the user's operation history and build a system that automatically suggests optimal operation procedures and shortcuts. For example, it may suggest frequently used functions as shortcuts. The analysis unit also uses AI to automatically suggest optimal operation procedures based on the operation history. For example, it may prioritize displaying functions that the user uses frequently. The analysis unit also uses AI to learn the user's operation history and suggest optimal operation procedures and shortcuts in real time. For example, it may present the optimal shortcut every time the user performs an operation. This makes it possible to automatically suggest optimal operation procedures and shortcuts based on the user's operation history.

[0076] The analysis unit can analyze the user's device and browser environment and automatically provide the optimal interface. For example, the analysis unit uses AI to analyze the user's device and browser environment and build a system that automatically provides the optimal interface. For example, it automatically provides an interface for smartphones. The analysis unit also uses AI to automatically provide the optimal interface based on the device and browser environment. For example, it automatically provides an interface for tablets. The analysis unit also uses AI to analyze the user's device and browser environment and provide the optimal interface in real time. For example, it automatically provides an interface for desktops. This makes it possible to automatically provide the optimal interface based on the user's device and browser environment.

[0077] The analysis unit can use the emotion estimation function to automatically change the interface design according to the user's emotional state. For example, the analysis unit uses the emotion estimation function to analyze the user's emotional state in real time and automatically change the interface design based on the results. For example, if the user is relaxed, a calm design is provided. The analysis unit also builds a system that automatically changes the interface design according to the user's emotional state. For example, if the user is excited, a vivid design is provided. The analysis unit also uses the emotion estimation function to suggest an interface design based on the user's emotional state. For example, if the user is feeling stressed, a calm design is provided. This makes it possible to automatically change the interface design according to the user's emotional state.

[0078] The analysis unit can analyze a user's gestures and touch operations and provide an interface that allows intuitive operation. For example, the analysis unit uses AI to analyze a user's gestures and touch operations and build a system that provides an interface that allows intuitive operation. For example, it provides an interface that supports swipe and pinch operations. The analysis unit also uses AI to automatically provide an interface that allows intuitive operation based on the gestures and touch operations. For example, it provides an interface that supports drag and drop operations. The analysis unit also uses AI to analyze a user's gestures and touch operations and provide an interface that allows intuitive operation in real time. For example, it provides an interface that supports tap operations. This makes it possible to provide an interface that allows intuitive operation based on the user's gestures and touch operations.

[0079] The analysis unit can use the emotion estimation function to analyze the emotion a user feels when operating the interface, and provide an interface that elicits positive emotions. For example, the analysis unit can use the emotion estimation function to analyze the emotion a user feels when operating the interface, and provide an interface that elicits positive emotions based on the results. For example, a design that allows the user to relax is provided. The analysis unit also builds a system that analyzes the emotional state of a user and provides an interface that elicits positive emotions. For example, if the user is excited, an exciting design is provided. The analysis unit also uses the emotion estimation function to provide an interface based on the emotional state of the user. For example, if the user is feeling stressed, a calm design is provided. In this way, it is possible to analyze the user's emotions and provide an interface that elicits positive emotions.

[0080] The analysis unit can analyze homepage access data in detail and generate reports that identify user behavior patterns and interests. The analysis unit, for example, builds a system in which AI analyzes homepage access data in detail and generates reports that identify user behavior patterns and interests. For example, a user's interests are identified based on the time spent on a particular page and the number of clicks. The analysis unit also analyzes the access data and AI automatically generates reports that identify user behavior patterns and interests. For example, reports are created based on pages frequently visited by users and search keywords. The analysis unit also periodically analyzes homepage access data using AI and generates reports that identify user behavior patterns and interests. For example, reports are created based on the user's time of visit and the type of device. This makes it possible to generate reports that identify user behavior patterns and interests.

[0081] The analysis unit can analyze the update history of the homepage and generate a report that evaluates the effects and impact of the update. The analysis unit, for example, builds a system in which AI analyzes the update history of the homepage and generates a report that evaluates the effects and impact of the update. For example, a report is created based on changes in the number of accesses and length of stay after the update. The analysis unit also analyzes the update history and AI automatically generates a report that evaluates the effects and impact of the update. For example, a report is created based on user reactions after new content is added. The analysis unit also periodically analyzes the update history of the homepage using AI and generates a report that evaluates the effects and impact of the update. For example, a report is created based on changes in search engine rankings before and after the update. This makes it possible to generate a report that evaluates the effects and impact of the update.

[0082] The analysis unit uses the emotion estimation function to generate a report based on the user's emotional state and make suggestions to improve the user's satisfaction. The analysis unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotional state and generates a report based on the results. For example, if the user has positive emotions, a report that emphasizes success stories is created. The analysis unit also generates a report based on the user's emotional state and makes suggestions to improve the user's satisfaction. For example, if the user is feeling stressed, relaxing content is suggested. The analysis unit also uses the emotion estimation function to analyze the user's emotional state and generate a report based on the results. For example, if the user is excited, a report that emphasizes exciting news is created. In this way, a report based on the user's emotional state can be generated and suggestions to improve the user's satisfaction can be made.

[0083] The analysis unit can compare data from other websites and generate competitive analysis reports. For example, the analysis unit builds a system in which AI collects data from other websites and compares it with one's own homepage to generate competitive analysis reports. For example, a report is created based on the number of visits and duration of visits to competitors. The analysis unit also analyzes data from other websites and AI automatically generates competitive analysis reports. For example, a report is created based on competitors' SEO measures and content strategies. The analysis unit also periodically collects data from other websites and AI generates competitive analysis reports. For example, a report is created based on competitors' update history and user responses. This makes it possible to generate competitive analysis reports by comparing data from other websites.

[0084] The analysis unit can work with the user's business data to generate reports based on business results and KPIs. For example, the analysis unit builds a system in which AI works with the user's business data to generate reports based on business results and KPIs. For example, reports are created based on sales data and customer satisfaction. The analysis unit also analyzes the business data, and the AI ​​automatically generates reports based on business results and KPIs. For example, reports are created based on the effectiveness of marketing campaigns. The analysis unit also periodically analyzes the user's business data, and the AI ​​generates reports based on business results and KPIs. For example, reports are created based on monthly sales data and the status of new customer acquisition. This makes it possible to generate reports based on business results and KPIs.

[0085] The analysis unit can use the emotion estimation function to analyze the emotions felt by the user when viewing a report and provide a report design that elicits positive emotions. For example, the analysis unit uses the emotion estimation function to analyze the emotions felt by the user when viewing a report, and builds a system that provides a report design that elicits positive emotions based on the results. For example, a design that allows the user to relax is provided. The analysis unit also analyzes the user's emotional state and provides a report design that elicits positive emotions. For example, if the user is excited, an exciting design is provided. The analysis unit also uses the emotion estimation function to provide a report design based on the user's emotional state. For example, if the user is feeling stressed, a calm design is provided. In this way, the user's emotions can be analyzed and a report design that elicits positive emotions can be provided.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The homepage management system can further include a voice analysis unit that analyzes the user's voice instructions. The voice analysis unit can, for example, update and maintain the homepage by the user issuing voice instructions. For example, by analyzing a voice instruction such as "add a new article," the update unit can automatically add a new article. The voice analysis unit can also convert the user's voice instructions into text, and the analysis unit can analyze the content of the homepage based on that text. This allows the user to manage the homepage using only voice input.

[0088] The analysis unit can further analyze the user's gestures and touch operations. For example, if the user is using a touch screen, the analysis unit can analyze the gestures and touch operations to provide an interface that allows intuitive operation. For example, an interface that supports swipe and pinch operations can be provided. Furthermore, based on the gesture analysis, it can also prioritize the display of functions that the user uses frequently. This allows the user to manage their homepage intuitively.

[0089] The analysis unit can further customize the content of the homepage based on the user's emotional state. For example, if the user is feeling stressed, it can suggest relaxing content. If the user is excited, it can suggest exciting content. This makes it possible to provide the most appropriate content according to the user's emotional state.

[0090] The update unit can also analyze the user's business calendar and perform automatic updates to coincide with important events and campaigns. For example, it can automatically update an announcement page the day before an event. It can also automatically publish a special page on the campaign start date. This makes it possible to automatically update the system to coincide with important events and campaigns based on the business calendar.

[0091] The maintenance department can further analyze the homepage's access logs, detect abnormal access patterns, and automatically take countermeasures. For example, they can detect signs of a DDoS attack and automatically take countermeasures. They can also block specific IP addresses when abnormal traffic is detected. This allows them to detect abnormal access patterns and automatically take countermeasures.

[0092] The analysis unit can further learn the user's operation history and automatically suggest optimal operation procedures and shortcuts. For example, it can suggest frequently used functions as shortcuts. It can also prioritize the display of functions that the user uses most often. This makes it possible to automatically suggest optimal operation procedures and shortcuts based on the user's operation history.

[0093] The analysis unit can further use its emotion estimation function to analyze the emotional nuances of the instructions entered by the user and propose more appropriate designs and layouts. For example, if the user is excited, it can select vivid colors. On the other hand, if the user is relaxed, it can select calm colors. This makes it possible to propose more appropriate designs and layouts based on the user's emotional nuances.

[0094] The update section can also link users' social media accounts and automatically update their social media posts on the homepage. For example, Twitter posts can be automatically displayed in the news section of the homepage. Instagram image posts can also be automatically added to the gallery page. This allows social media posts to be automatically updated on the homepage.

[0095] The analysis unit can further use the emotion estimation function to automatically change the interface design based on the user's emotional state. For example, if the user is relaxed, a calm design can be provided. If the user is excited, a vivid design can be provided. This makes it possible to automatically change the interface design according to the user's emotional state.

[0096] The analysis unit further uses the emotion estimation function to analyze the emotions felt by the user when viewing the report, and can provide a report design that elicits positive emotions. For example, a design that allows the user to relax can be provided. Also, if the user is excited, an exciting design can be provided. In this way, it is possible to analyze the user's emotions and provide a report design that elicits positive emotions.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The analysis unit analyzes the content of the website. For example, the analysis unit may use text analysis technology to analyze the text content of the website. It may also use image analysis technology to analyze the image content of the website. It may also use data mining technology to analyze the metadata of the website. Specifically, text analysis technology uses natural language processing to analyze the meaning of text, image analysis technology uses image recognition algorithms to analyze the content of images, and data mining technology extracts useful information from large amounts of data. Step 2: The update department updates the homepage based on the content analyzed by the analysis department. For example, the update department adds content, changes the layout, and implements SEO measures. Specifically, the update department adds new articles and images to the homepage, changes the design and layout of the homepage, and optimizes it to improve its ranking in search engines. Step 3: The maintenance department performs maintenance work on the website. For example, the maintenance department implements security measures, optimizes performance, and creates backups. Specifically, they fix vulnerabilities, prevent unauthorized access, improve the display speed of the website, and periodically save data to ensure data integrity.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0139] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0140] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0143] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0150] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0151] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0152] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0156] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0157] 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.

[0158] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0159] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0160] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0161] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0163] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0164] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0165] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes the contents of the homepage; an updating unit that updates the homepage based on the analyzed content; A maintenance department that performs maintenance work on the homepage. A system characterized by:

2. The analysis unit Analyzes the user's industry and business model, and automatically incorporates optimal content structure and SEO measures 2. The system of claim 1.

3. The update unit Link users' SNS accounts and automatically reflect their SNS posts on the homepage.

2. The system of claim 1.

4. The maintenance unit Analyzes website access logs, detects abnormal access patterns, and automatically takes measures 2. The system of claim 1.

5. The analysis unit Automatically changing interface design according to the user's emotional state 2. The system of claim 1.

6. The analysis unit Generate reports based on the user's emotional state and make suggestions to improve user satisfaction 2. The system of claim 1.

7. The analysis unit Automatically selects designs and colors according to the user's emotional state 2. The system of claim 1.

8. The update unit Analyzes the emotions users feel when they update their content and suggests updates that elicit positive emotions.

2. The system of claim 1.

Images